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You don't remember every skill, so ask.

A flow is a path through the skills. Most paths run along one main flow, and two on-ramps merge onto it. Everything else is standalone, or a vocabulary layer that runs underneath.

The main flow: idea → ship

The route most work travels. You have an idea and want it built.

1. /grill-with-docs — sharpen the idea by interview. Start here when you have a codebase: it's stateful, retaining what it learns in CONTEXT.md and ADRs. (No codebase? Use /grill-me — see Standalone. Both run the same /grilling primitive; grill-with-docs is the one that leaves a paper trail.)

2. Branch — can you settle every question in conversation? If a question needs a runnable answer (state, business logic, a UI you have to see), detour through a prototype, bridged by /handoff in both directions (see Crossing sessions):

   • /handoff out, then open a fresh session against that file,
   • /prototype to answer the question with throwaway code,
   • /handoff back what you learned, and reference it from the original idea thread.

3. Branch — is this a multi-session build?

   • Yes → /to-spec (turn the thread into a spec), then /to-tickets to split it into tracer-bullet tickets, each declaring its blocking edges. On a local tracker that's an ordered tickets.md you work by hand; on a real tracker the edges become native blocking links, so any ticket whose blockers are done can be grabbed — kick off /implement per ticket, clearing context between each one.
   • No → /implement right here, in the same context window.

   Either way, /implement builds each issue by driving /tdd internally — one red-green slice at a time — then closes out by running /code-review, a two-axis review (Standards + Spec) of the diff, before committing. Reach for /tdd on its own when you just want to build a concrete behaviour test-first without a full spec, and /code-review on its own whenever you want to review a branch or PR against a fixed point.

Context hygiene

Keep steps 1–3 in one unbroken context window — don't compact or clear until after /to-tickets — so the grilling, spec, and tickets all build on the same thinking. Each /implement then starts fresh, working from the ticket.

The limit on this is the smart zone: the window (~120k tokens on state-of-the-art models) within which the model still reasons sharply. If a session approaches it before /to-tickets, don't push on degraded — /handoff and continue in a fresh thread.

On-ramps

A starting situation that generates work, then merges onto the main flow.

• Bugs and requests piling up → /triage. It moves issues through triage roles and produces agent-ready issues, which /implement later picks up.

  Triage is only for issues you didn't create — bug reports, incoming feature requests, anything that arrives raw. Tickets that /to-tickets produced are already agent-ready, so don't triage them.

• Something's broken → /diagnosing-bugs. For the hard ones: the bug that resists a first glance, the intermittent flake, the regression that crept in between two known-good states. It refuses to theorise until it has a tight feedback loop — one command that already goes red on this bug — then fixes with a regression test. Its post-mortem hands off to /improve-codebase-architecture when the real finding is that there's no good seam to lock the bug down.

• A huge, foggy effort — a greenfield project or a huge feature build, too big for one session → /wayfinder. When the way from here to the destination isn't visible yet, it charts a shared map of investigation tickets on the issue tracker and resolves them one at a time — producing decisions, not deliverables — until the fog is pushed back and the way is clear. Then it merges onto the main flow at /to-spec (or, if the effort turned out small enough, straight to /implement). Where /grill-with-docs sharpens an idea you can hold in one session, wayfinder is for the idea you can't.

Codebase health

Not feature work — upkeep.

• /improve-codebase-architecture — run whenever you have a spare moment to keep the codebase good for agents to operate in. It surfaces deepening opportunities; picking one generates an idea you can take into the main flow at /grill-with-docs. It's the survey that finds the candidates; /codebase-design (below) is the bench you design the chosen one on.

Vocabulary underneath

Two model-invoked references that run beneath the other skills — each the single source of truth for its vocabulary. Reach for them directly when the words, not the process, are the problem; or let the skills above pull them in.

• /domain-modeling — sharpen the project's domain language: challenge a fuzzy term, resolve an overloaded word ("account" doing three jobs), record a hard-to-reverse decision as an ADR. It's the active discipline /grill-with-docs drives to keep CONTEXT.md a clean glossary.
• /codebase-design — the deep-module vocabulary (module, interface, depth, seam, adapter, leverage, locality) for designing a module's shape: a lot of behaviour behind a small interface at a clean seam. /tdd and /improve-codebase-architecture both speak it.

Crossing sessions

• /handoff — when a thread is full or you need to branch off (e.g. into a /prototype session), this compacts the conversation into a markdown file. You don't continue in place — you open a new session and reference that file to carry the context across. It's the bridge between context windows, in either direction. Use it when you want a fresh session but need the current conversation preserved.
• /compact (built-in) — stay in the same conversation, letting the earlier turns be summarized. Use it at intentional breaks between phases, when you don't mind losing the verbatim history. Don't compact mid-phase — the agent can lose its way. /handoff forks; /compact continues.

Standalone

Off the main flow entirely.

• /grill-me — the same relentless interview as /grill-with-docs, but for when you have no codebase. Stateless: it saves nothing locally, builds no CONTEXT.md. Reach for it to sharpen any plan or design that doesn't live in a repo.
• /prototype — a small, throwaway program that answers one design question: does this state model feel right, or what should this UI look like. Throwaway from day one — keep the answer, delete the code. It's the detour in step 2 of the main flow, but reach for it any time a design question is hard to settle on paper.
• /research — delegate reading legwork to a background agent: it investigates a question against primary sources, then leaves a cited Markdown file in the repo. Keep working while it reads. The file it produces is something to take into the main flow at /grill-with-docs — research feeds the thinking, it doesn't replace it.
• /teach — learn a concept over multiple sessions, using the current directory as a stateful workspace.
• /writing-great-skills — reference for writing and editing skills well.

Precondition

/setup-matt-pocock-skills — run before your first engineering flow to configure the issue tracker, triage labels, and doc layout the other skills assume. Custom issue trackers also work.

Write a handoff summary of the current conversation so a fresh agent can continue the work. Instead of saving it, launch a background agent seeded with the summary as its prompt: claude --bg --name "<descriptive name>" "<handoff summary>". It starts in the current working directory and returns immediately; the user manages it with claude agents.

Always pass -n/--name with a descriptive name (e.g. --name "Fix login bug") — it sets the display name shown in the job list, session picker, and terminal title.

Include a "suggested skills" section in the summary, which suggests skills that the agent should invoke.

Do not duplicate content already captured in other artifacts (PRDs, plans, ADRs, issues, commits, diffs). Reference them by path or URL instead.

Redact any sensitive information, such as API keys, passwords, or personally identifiable information — the summary becomes the agent's prompt.

If the user passed arguments, treat them as a description of what the next session will focus on and tailor the summary accordingly.

Two-axis review of the diff between HEAD and a fixed point the user supplies:

• Standards — does the code conform to this repo's documented coding standards?
• Spec — does the code faithfully implement the originating issue / PRD / spec?

Both axes run as parallel sub-agents so they don't pollute each other's context, then this skill aggregates their findings.

The issue tracker should have been provided to you — run /setup-matt-pocock-skills if docs/agents/issue-tracker.md is missing.

Process

1. Pin the fixed point

Whatever the user said is the fixed point — a commit SHA, branch name, tag, main, HEAD~5, etc. If they didn't specify one, ask for it.

Capture the diff command once: git diff <fixed-point>...HEAD (three-dot, so the comparison is against the merge-base). Also note the list of commits via git log <fixed-point>..HEAD --oneline.

Before going further, confirm the fixed point resolves (git rev-parse <fixed-point>) and the diff is non-empty. A bad ref or empty diff should fail here — not inside two parallel sub-agents.

2. Identify the spec source

Look for the originating spec, in this order:

1. Issue references in the commit messages (#123, Closes #45, GitLab !67, etc.) — fetch via the workflow in docs/agents/issue-tracker.md.
2. A path the user passed as an argument.
3. A PRD/spec file under docs/, specs/, or .scratch/ matching the branch name or feature.
4. If nothing is found, ask the user where the spec is. If they say there isn't one, the Spec sub-agent will skip and report "no spec available".

3. Identify the standards sources

Anything in the repo that documents how code should be written, such as CODING_STANDARDS.md or CONTRIBUTING.md.

On top of whatever the repo documents, the Standards axis always carries the smell baseline below — a fixed set of Fowler code smells (Refactoring, ch.3) that applies even when a repo documents nothing. Two rules bind it:

• The repo overrides. A documented repo standard always wins; where it endorses something the baseline would flag, suppress the smell.
• Always a judgement call. Each smell is a labelled heuristic ("possible Feature Envy"), never a hard violation — and, like any standard here, skip anything tooling already enforces.

Each smell reads what it is → how to fix; match it against the diff:

• Mysterious Name — a function, variable, or type whose name doesn't reveal what it does or holds. → rename it; if no honest name comes, the design's murky.
• Duplicated Code — the same logic shape appears in more than one hunk or file in the change. → extract the shared shape, call it from both.
• Feature Envy — a method that reaches into another object's data more than its own. → move the method onto the data it envies.
• Data Clumps — the same few fields or params keep travelling together (a type wanting to be born). → bundle them into one type, pass that.
• Primitive Obsession — a primitive or string standing in for a domain concept that deserves its own type. → give the concept its own small type.
• Repeated Switches — the same switch/if-cascade on the same type recurs across the change. → replace with polymorphism, or one map both sites share.
• Shotgun Surgery — one logical change forces scattered edits across many files in the diff. → gather what changes together into one module.
• Divergent Change — one file or module is edited for several unrelated reasons. → split so each module changes for one reason.
• Speculative Generality — abstraction, parameters, or hooks added for needs the spec doesn't have. → delete it; inline back until a real need shows.
• Message Chains — long a.b().c().d() navigation the caller shouldn't depend on. → hide the walk behind one method on the first object.
• Middle Man — a class or function that mostly just delegates onward. → cut it, call the real target direct.
• Refused Bequest — a subclass or implementer that ignores or overrides most of what it inherits. → drop the inheritance, use composition.

4. Spawn both sub-agents in parallel

Send a single message with two Agent tool calls. Use the general-purpose subagent for both.

Standards sub-agent prompt — include:

• The full diff command and commit list.
• The list of standards-source files you found in step 3, plus the smell baseline from step 3 pasted in full — the sub-agent has no other access to it.
• The brief: "Report — per file/hunk where relevant — (a) every place the diff violates a documented standard: cite the standard (file + the rule); and (b) any baseline smell you spot: name it and quote the hunk. Distinguish hard violations from judgement calls — documented-standard breaches can be hard, but baseline smells are always judgement calls, and a documented repo standard overrides the baseline. Skip anything tooling enforces. Under 400 words."

Spec sub-agent prompt — include:

• The diff command and commit list.
• The path or fetched contents of the spec.
• The brief: "Report: (a) requirements the spec asked for that are missing or partial; (b) behaviour in the diff that wasn't asked for (scope creep); (c) requirements that look implemented but where the implementation looks wrong. Quote the spec line for each finding. Under 400 words."

If the spec is missing, skip the Spec sub-agent and note this in the final report.

5. Aggregate

Present the two reports under ## Standards and ## Spec headings, verbatim or lightly cleaned. Do not merge or rerank findings — the two axes are deliberately separate (see Why two axes).

End with a one-line summary: total findings per axis, and the worst issue within each axis (if any). Don't pick a single winner across axes — that's the reranking the separation exists to prevent.

Why two axes

A change can pass one axis and fail the other:

• Code that follows every standard but implements the wrong thing → Standards pass, Spec fail.
• Code that does exactly what the issue asked but breaks the project's conventions → Spec pass, Standards fail.

Reporting them separately stops one axis from masking the other.

Codebase Design

Design deep modules: a lot of behaviour behind a small interface, placed at a clean seam, testable through that interface. Use this language and these principles wherever code is being designed or restructured. The aim is leverage for callers, locality for maintainers, and testability for everyone.

Glossary

Use these terms exactly — don't substitute "component," "service," "API," or "boundary." Consistent language is the whole point.

Module — anything with an interface and an implementation. Deliberately scale-agnostic: a function, class, package, or tier-spanning slice. Avoid: unit, component, service.

Interface — everything a caller must know to use the module correctly: the type signature, but also invariants, ordering constraints, error modes, required configuration, and performance characteristics. Avoid: API, signature (too narrow — they refer only to the type-level surface).

Implementation — what's inside a module, its body of code. Distinct from Adapter: a thing can be a small adapter with a large implementation (a Postgres repo) or a large adapter with a small implementation (an in-memory fake). Reach for "adapter" when the seam is the topic; "implementation" otherwise.

Depth — leverage at the interface: the amount of behaviour a caller (or test) can exercise per unit of interface they have to learn. A module is deep when a large amount of behaviour sits behind a small interface, shallow when the interface is nearly as complex as the implementation.

Seam (Michael Feathers) — a place where you can alter behaviour without editing in that place; the location at which a module's interface lives. Where to put the seam is its own design decision, distinct from what goes behind it. Avoid: boundary (overloaded with DDD's bounded context).

Adapter — a concrete thing that satisfies an interface at a seam. Describes role (what slot it fills), not substance (what's inside).

Leverage — what callers get from depth: more capability per unit of interface they learn. One implementation pays back across N call sites and M tests.

Locality — what maintainers get from depth: change, bugs, knowledge, and verification concentrate in one place rather than spreading across callers. Fix once, fixed everywhere.

Deep vs shallow

Deep module = small interface + lots of implementation:

┌─────────────────────┐
│   Small Interface   │  ← Few methods, simple params
├─────────────────────┤
│                     │
│  Deep Implementation│  ← Complex logic hidden
│                     │
└─────────────────────┘

Shallow module = large interface + little implementation (avoid):

┌─────────────────────────────────┐
│       Large Interface           │  ← Many methods, complex params
├─────────────────────────────────┤
│  Thin Implementation            │  ← Just passes through
└─────────────────────────────────┘

When designing an interface, ask:

• Can I reduce the number of methods?
• Can I simplify the parameters?
• Can I hide more complexity inside?

Principles

• Depth is a property of the interface, not the implementation. A deep module can be internally composed of small, mockable, swappable parts — they just aren't part of the interface. A module can have internal seams (private to its implementation, used by its own tests) as well as the external seam at its interface.
• The deletion test. Imagine deleting the module. If complexity vanishes, it was a pass-through. If complexity reappears across N callers, it was earning its keep.
• The interface is the test surface. Callers and tests cross the same seam. If you want to test past the interface, the module is probably the wrong shape.
• One adapter means a hypothetical seam. Two adapters means a real one. Don't introduce a seam unless something actually varies across it.

Designing for testability

Good interfaces make testing natural:

1. Accept dependencies, don't create them.

   // Testable
   function processOrder(order, paymentGateway) {}
   
   // Hard to test
   function processOrder(order) {
     const gateway = new StripeGateway();
   }
   

2. Return results, don't produce side effects.

   // Testable
   function calculateDiscount(cart): Discount {}
   
   // Hard to test
   function applyDiscount(cart): void {
     cart.total -= discount;
   }
   

3. Small surface area. Fewer methods = fewer tests needed. Fewer params = simpler test setup.

Relationships

• A Module has exactly one Interface (the surface it presents to callers and tests).
• Depth is a property of a Module, measured against its Interface.
• A Seam is where a Module's Interface lives.
• An Adapter sits at a Seam and satisfies the Interface.
• Depth produces Leverage for callers and Locality for maintainers.

Rejected framings

• Depth as ratio of implementation-lines to interface-lines (Ousterhout): rewards padding the implementation. We use depth-as-leverage instead.
• "Interface" as the TypeScript interface keyword or a class's public methods: too narrow — interface here includes every fact a caller must know.
• "Boundary": overloaded with DDD's bounded context. Say seam or interface.

Going deeper

• Deepening a cluster given its dependencies — see DEEPENING.md: dependency categories, seam discipline, and replace-don't-layer testing.
• Exploring alternative interfaces — see DESIGN-IT-TWICE.md: spin up parallel sub-agents to design the interface several radically different ways, then compare on depth, locality, and seam placement.

Design an Interface

Based on "Design It Twice" from "A Philosophy of Software Design": your first idea is unlikely to be the best. Generate multiple radically different designs, then compare.

Workflow

1. Gather Requirements

Before designing, understand:

• What problem does this module solve?
• Who are the callers? (other modules, external users, tests)
• What are the key operations?
• Any constraints? (performance, compatibility, existing patterns)
• What should be hidden inside vs exposed?

Ask: "What does this module need to do? Who will use it?"

2. Generate Designs (Parallel Sub-Agents)

Spawn 3+ sub-agents simultaneously using Task tool. Each must produce a radically different approach.

Prompt template for each sub-agent:

Design an interface for: [module description]

Requirements: [gathered requirements]

Constraints for this design: [assign a different constraint to each agent]
- Agent 1: "Minimize method count - aim for 1-3 methods max"
- Agent 2: "Maximize flexibility - support many use cases"
- Agent 3: "Optimize for the most common case"
- Agent 4: "Take inspiration from [specific paradigm/library]"

Output format:
1. Interface signature (types/methods)
2. Usage example (how caller uses it)
3. What this design hides internally
4. Trade-offs of this approach

3. Present Designs

Show each design with:

1. Interface signature - types, methods, params
2. Usage examples - how callers actually use it in practice
3. What it hides - complexity kept internal

Present designs sequentially so user can absorb each approach before comparison.

4. Compare Designs

After showing all designs, compare them on:

• Interface simplicity: fewer methods, simpler params
• General-purpose vs specialized: flexibility vs focus
• Implementation efficiency: does shape allow efficient internals?
• Depth: small interface hiding significant complexity (good) vs large interface with thin implementation (bad)
• Ease of correct use vs ease of misuse

Discuss trade-offs in prose, not tables. Highlight where designs diverge most.

5. Synthesize

Often the best design combines insights from multiple options. Ask:

• "Which design best fits your primary use case?"
• "Any elements from other designs worth incorporating?"

Evaluation Criteria

From "A Philosophy of Software Design":

Interface simplicity: Fewer methods, simpler params = easier to learn and use correctly.

General-purpose: Can handle future use cases without changes. But beware over-generalization.

Implementation efficiency: Does interface shape allow efficient implementation? Or force awkward internals?

Depth: Small interface hiding significant complexity = deep module (good). Large interface with thin implementation = shallow module (avoid).

Anti-Patterns

• Don't let sub-agents produce similar designs - enforce radical difference
• Don't skip comparison - the value is in contrast
• Don't implement - this is purely about interface shape
• Don't evaluate based on implementation effort

Diagnosing Bugs

A discipline for hard bugs. Skip phases only when explicitly justified.

When exploring the codebase, read CONTEXT.md (if it exists) to get a clear mental model of the relevant modules, and check ADRs in the area you're touching.

Phase 1 — Build a feedback loop

This is the skill. Everything else is mechanical. If you have a tight pass/fail signal for the bug — one that goes red on this bug — you will find the cause; bisection, hypothesis-testing, and instrumentation all just consume it. If you don't have one, no amount of staring at code will save you.

Spend disproportionate effort here. Be aggressive. Be creative. Refuse to give up.

Ways to construct one — try them in roughly this order

1. Failing test at whatever seam reaches the bug — unit, integration, e2e.
2. Curl / HTTP script against a running dev server.
3. CLI invocation with a fixture input, diffing stdout against a known-good snapshot.
4. Headless browser script (Playwright / Puppeteer) — drives the UI, asserts on DOM/console/network.
5. Replay a captured trace. Save a real network request / payload / event log to disk; replay it through the code path in isolation.
6. Throwaway harness. Spin up a minimal subset of the system (one service, mocked deps) that exercises the bug code path with a single function call.
7. Property / fuzz loop. If the bug is "sometimes wrong output", run 1000 random inputs and look for the failure mode.
8. Bisection harness. If the bug appeared between two known states (commit, dataset, version), automate "boot at state X, check, repeat" so you can git bisect run it.
9. Differential loop. Run the same input through old-version vs new-version (or two configs) and diff outputs.
10. HITL bash script. Last resort. If a human must click, drive them with scripts/hitl-loop.template.sh so the loop is still structured. Captured output feeds back to you.

Build the right feedback loop, and the bug is 90% fixed.

Tighten the loop

Treat the loop as a product. Once you have a loop, tighten it:

• Can I make it faster? (Cache setup, skip unrelated init, narrow the test scope.)
• Can I make the signal sharper? (Assert on the specific symptom, not "didn't crash".)
• Can I make it more deterministic? (Pin time, seed RNG, isolate filesystem, freeze network.)

A 30-second flaky loop is barely better than no loop; a 2-second deterministic one is tight — a debugging superpower.

Non-deterministic bugs

The goal is not a clean repro but a higher reproduction rate. Loop the trigger 100×, parallelise, add stress, narrow timing windows, inject sleeps. A 50%-flake bug is debuggable; 1% is not — keep raising the rate until it's debuggable.

When you genuinely cannot build a loop

Stop and say so explicitly. List what you tried. Ask the user for: (a) access to whatever environment reproduces it, (b) a captured artifact (HAR file, log dump, core dump, screen recording with timestamps), or (c) permission to add temporary production instrumentation. Do not proceed to hypothesise without a loop.

Completion criterion — a tight loop that goes red

Phase 1 is done when the loop is tight and red-capable: you can name one command — a script path, a test invocation, a curl — that you have already run at least once (paste the invocation and its output), and that is:

• Red-capable — it drives the actual bug code path and asserts the user's exact symptom, so it can go red on this bug and green once fixed. Not "runs without erroring" — it must be able to catch this specific bug.
• Deterministic — same verdict every run (flaky bugs: a pinned, high reproduction rate, per above).
• Fast — seconds, not minutes.
• Agent-runnable — you can run it unattended; a human in the loop only via scripts/hitl-loop.template.sh.

If you catch yourself reading code to build a theory before this command exists, stop — jumping straight to a hypothesis is the exact failure this skill prevents. No red-capable command, no Phase 2.

Phase 2 — Reproduce + minimise

Run the loop. Watch it go red — the bug appears.

Confirm:

• The loop produces the failure mode the user described — not a different failure that happens to be nearby. Wrong bug = wrong fix.
• The failure is reproducible across multiple runs (or, for non-deterministic bugs, reproducible at a high enough rate to debug against).
• You have captured the exact symptom (error message, wrong output, slow timing) so later phases can verify the fix actually addresses it.

Minimise

Once it's red, shrink the repro to the smallest scenario that still goes red. Cut inputs, callers, config, data, and steps one at a time, re-running the loop after each cut — keep only what's load-bearing for the failure.

Why bother: a minimal repro shrinks the hypothesis space in Phase 3 (fewer moving parts left to suspect) and becomes the clean regression test in Phase 5.

Done when every remaining element is load-bearing — removing any one of them makes the loop go green.

Do not proceed until you have reproduced and minimised.

Phase 3 — Hypothesise

Generate 3–5 ranked hypotheses before testing any of them. Single-hypothesis generation anchors on the first plausible idea.

Each hypothesis must be falsifiable: state the prediction it makes.

Format: "If <X> is the cause, then <changing Y> will make the bug disappear / <changing Z> will make it worse."

If you cannot state the prediction, the hypothesis is a vibe — discard or sharpen it.

Show the ranked list to the user before testing. They often have domain knowledge that re-ranks instantly ("we just deployed a change to #3"), or know hypotheses they've already ruled out. Cheap checkpoint, big time saver. Don't block on it — proceed with your ranking if the user is AFK.

Phase 4 — Instrument

Each probe must map to a specific prediction from Phase 3. Change one variable at a time.

Tool preference:

1. Debugger / REPL inspection if the env supports it. One breakpoint beats ten logs.
2. Targeted logs at the boundaries that distinguish hypotheses.
3. Never "log everything and grep".

Tag every debug log with a unique prefix, e.g. [DEBUG-a4f2]. Cleanup at the end becomes a single grep. Untagged logs survive; tagged logs die.

Perf branch. For performance regressions, logs are usually wrong. Instead: establish a baseline measurement (timing harness, performance.now(), profiler, query plan), then bisect. Measure first, fix second.

Phase 5 — Fix + regression test

Write the regression test before the fix — but only if there is a correct seam for it.

A correct seam is one where the test exercises the real bug pattern as it occurs at the call site. If the only available seam is too shallow (single-caller test when the bug needs multiple callers, unit test that can't replicate the chain that triggered the bug), a regression test there gives false confidence.

If no correct seam exists, that itself is the finding. Note it. The codebase architecture is preventing the bug from being locked down. Flag this for the next phase.

If a correct seam exists:

1. Turn the minimised repro into a failing test at that seam.
2. Watch it fail.
3. Apply the fix.
4. Watch it pass.
5. Re-run the Phase 1 feedback loop against the original (un-minimised) scenario.

Phase 6 — Cleanup + post-mortem

Required before declaring done:

• Original repro no longer reproduces (re-run the Phase 1 loop)
• Regression test passes (or absence of seam is documented)
• All [DEBUG-...] instrumentation removed (grep the prefix)
• Throwaway prototypes deleted (or moved to a clearly-marked debug location)
• The hypothesis that turned out correct is stated in the commit / PR message — so the next debugger learns

Then ask: what would have prevented this bug? If the answer involves architectural change (no good test seam, tangled callers, hidden coupling) hand off to the /improve-codebase-architecture skill with the specifics. Make the recommendation after the fix is in, not before — you have more information now than when you started.

Domain Modeling

Actively build and sharpen the project's domain model as you design. This is the active discipline — challenging terms, inventing edge-case scenarios, and writing the glossary and decisions down the moment they crystallise. (Merely reading CONTEXT.md for vocabulary is not this skill — that's a one-line habit any skill can do. This skill is for when you're changing the model, not just consuming it.)

File structure

Most repos have a single context:

/
├── CONTEXT.md
├── docs/
│   └── adr/
│       ├── 0001-event-sourced-orders.md
│       └── 0002-postgres-for-write-model.md
└── src/

If a CONTEXT-MAP.md exists at the root, the repo has multiple contexts. The map points to where each one lives:

/
├── CONTEXT-MAP.md
├── docs/
│   └── adr/                          ← system-wide decisions
├── src/
│   ├── ordering/
│   │   ├── CONTEXT.md
│   │   └── docs/adr/                 ← context-specific decisions
│   └── billing/
│       ├── CONTEXT.md
│       └── docs/adr/

Create files lazily — only when you have something to write. If no CONTEXT.md exists, create one when the first term is resolved. If no docs/adr/ exists, create it when the first ADR is needed.

During the session

Challenge against the glossary

When the user uses a term that conflicts with the existing language in CONTEXT.md, call it out immediately. "Your glossary defines 'cancellation' as X, but you seem to mean Y — which is it?"

Sharpen fuzzy language

When the user uses vague or overloaded terms, propose a precise canonical term. "You're saying 'account' — do you mean the Customer or the User? Those are different things."

Discuss concrete scenarios

When domain relationships are being discussed, stress-test them with specific scenarios. Invent scenarios that probe edge cases and force the user to be precise about the boundaries between concepts.

Cross-reference with code

When the user states how something works, check whether the code agrees. If you find a contradiction, surface it: "Your code cancels entire Orders, but you just said partial cancellation is possible — which is right?"

Update CONTEXT.md inline

When a term is resolved, update CONTEXT.md right there. Don't batch these up — capture them as they happen. Use the format in CONTEXT-FORMAT.md.

CONTEXT.md should be totally devoid of implementation details. Do not treat CONTEXT.md as a spec, a scratch pad, or a repository for implementation decisions. It is a glossary and nothing else.

Offer ADRs sparingly

Only offer to create an ADR when all three are true:

1. Hard to reverse — the cost of changing your mind later is meaningful
2. Surprising without context — a future reader will wonder "why did they do it this way?"
3. The result of a real trade-off — there were genuine alternatives and you picked one for specific reasons

If any of the three is missing, skip the ADR. Use the format in ADR-FORMAT.md.

1. First, divide the article into sections based on its headings. Think about the main points you want to make during those sections.

Consider that information is a directed acyclic graph, and that pieces of information can depend on other pieces of information. Make sure that the order of the sections and their contents respects these dependencies.

Confirm the sections with the user.

2. For each section:

2a. Rewrite the section to improve clarity, coherence, and flow. Use maximum 240 characters per paragraph.

Setup Git Guardrails

Sets up a PreToolUse hook that intercepts and blocks dangerous git commands before Claude executes them.

What Gets Blocked

• git push (all variants including --force)
• git reset --hard
• git clean -f / git clean -fd
• git branch -D
• git checkout . / git restore .

When blocked, Claude sees a message telling it that it does not have authority to access these commands.

Steps

1. Ask scope

Ask the user: install for this project only (.claude/settings.json) or all projects (~/.claude/settings.json)?

2. Copy the hook script

The bundled script is at: scripts/block-dangerous-git.sh

Copy it to the target location based on scope:

• Project: .claude/hooks/block-dangerous-git.sh
• Global: ~/.claude/hooks/block-dangerous-git.sh

Make it executable with chmod +x.

3. Add hook to settings

Add to the appropriate settings file:

Project (.claude/settings.json):

{
  "hooks": {
    "PreToolUse": [
      {
        "matcher": "Bash",
        "hooks": [
          {
            "type": "command",
            "command": "\"$CLAUDE_PROJECT_DIR\"/.claude/hooks/block-dangerous-git.sh"
          }
        ]
      }
    ]
  }
}

Global (~/.claude/settings.json):

{
  "hooks": {
    "PreToolUse": [
      {
        "matcher": "Bash",
        "hooks": [
          {
            "type": "command",
            "command": "~/.claude/hooks/block-dangerous-git.sh"
          }
        ]
      }
    ]
  }
}

If the settings file already exists, merge the hook into existing hooks.PreToolUse array — don't overwrite other settings.

4. Ask about customization

Ask if user wants to add or remove any patterns from the blocked list. Edit the copied script accordingly.

5. Verify

Run a quick test:

echo '{"tool_input":{"command":"git push origin main"}}' | <path-to-script>

Should exit with code 2 and print a BLOCKED message to stderr.

Interview me relentlessly about every aspect of this plan until we reach a shared understanding. Walk down each branch of the design tree, resolving dependencies between decisions one-by-one. For each question, provide your recommended answer.

Ask the questions one at a time, waiting for feedback on each question before continuing. Asking multiple questions at once is bewildering.

If a fact can be found by exploring the codebase, look it up rather than asking me. The decisions, though, are mine — put each one to me and wait for my answer.

Do not enact the plan until I confirm we have reached a shared understanding.

Write a handoff document summarising the current conversation so a fresh agent can continue the work. Save to the temporary directory of the user's OS - not the current workspace.

Include a "suggested skills" section in the document, which suggests skills that the agent should invoke.

Do not duplicate content already captured in other artifacts (specs, plans, ADRs, issues, commits, diffs). Reference them by path or URL instead.

Redact any sensitive information, such as API keys, passwords, or personally identifiable information.

If the user passed arguments, treat them as a description of what the next session will focus on and tailor the doc accordingly.

Implement the work described by the user in the spec or tickets.

Use /tdd where possible, at pre-agreed seams.

Run typechecking regularly, single test files regularly, and the full test suite once at the end.

Once done, use /code-review to review the work.

Commit your work to the current branch.

Improve Codebase Architecture

Surface architectural friction and propose deepening opportunities — refactors that turn shallow modules into deep ones. The aim is testability and AI-navigability.

This command is informed by the project's domain model and built on a shared design vocabulary:

• Run the /codebase-design skill for the architecture vocabulary (module, interface, depth, seam, adapter, leverage, locality) and its principles (the deletion test, "the interface is the test surface", "one adapter = hypothetical seam, two = real"). Use these terms exactly in every suggestion — don't drift into "component," "service," "API," or "boundary."
• The domain language in CONTEXT.md gives names to good seams; ADRs in docs/adr/ record decisions this command should not re-litigate.

Process

1. Explore

Read the project's domain glossary (CONTEXT.md) and any ADRs in the area you're touching first.

Then use the Agent tool with subagent_type=Explore to walk the codebase. Don't follow rigid heuristics — explore organically and note where you experience friction:

• Where does understanding one concept require bouncing between many small modules?
• Where are modules shallow — interface nearly as complex as the implementation?
• Where have pure functions been extracted just for testability, but the real bugs hide in how they're called (no locality)?
• Where do tightly-coupled modules leak across their seams?
• Which parts of the codebase are untested, or hard to test through their current interface?

Apply the deletion test to anything you suspect is shallow: would deleting it concentrate complexity, or just move it? A "yes, concentrates" is the signal you want.

2. Present candidates as an HTML report

Write a self-contained HTML file to the OS temp directory so nothing lands in the repo. Resolve the temp dir from $TMPDIR, falling back to /tmp (or %TEMP% on Windows), and write to <tmpdir>/architecture-review-<timestamp>.html so each run gets a fresh file. Open it for the user — xdg-open <path> on Linux, open <path> on macOS, start <path> on Windows — and tell them the absolute path.

The report uses Tailwind via CDN for layout and styling, and Mermaid via CDN for diagrams where a graph/flow/sequence reliably communicates the structure. Mix Mermaid with hand-crafted CSS/SVG visuals — use Mermaid when relationships are graph-shaped (call graphs, dependencies, sequences), and hand-built divs/SVG when you want something more editorial (mass diagrams, cross-sections, collapse animations). Each candidate gets a before/after visualisation. Be visual.

For each candidate, render a card with:

• Files — which files/modules are involved
• Problem — why the current architecture is causing friction
• Solution — plain English description of what would change
• Benefits — explained in terms of locality and leverage, and how tests would improve
• Before / After diagram — side-by-side, custom-drawn, illustrating the shallowness and the deepening
• Recommendation strength — one of Strong, Worth exploring, Speculative, rendered as a badge

End the report with a Top recommendation section: which candidate you'd tackle first and why.

Use CONTEXT.md vocabulary for the domain, and the /codebase-design vocabulary for the architecture. If CONTEXT.md defines "Order," talk about "the Order intake module" — not "the FooBarHandler," and not "the Order service."

ADR conflicts: if a candidate contradicts an existing ADR, only surface it when the friction is real enough to warrant revisiting the ADR. Mark it clearly in the card (e.g. a warning callout: "contradicts ADR-0007 — but worth reopening because…"). Don't list every theoretical refactor an ADR forbids.

See HTML-REPORT.md for the full HTML scaffold, diagram patterns, and styling guidance.

Do NOT propose interfaces yet. After the file is written, ask the user: "Which of these would you like to explore?"

3. Grilling loop

Once the user picks a candidate, run the /grilling skill to walk the design tree with them — constraints, dependencies, the shape of the deepened module, what sits behind the seam, what tests survive.

Side effects happen inline as decisions crystallize — run the /domain-modeling skill to keep the domain model current as you go:

• Naming a deepened module after a concept not in CONTEXT.md? Add the term to CONTEXT.md. Create the file lazily if it doesn't exist.
• Sharpening a fuzzy term during the conversation? Update CONTEXT.md right there.
• User rejects the candidate with a load-bearing reason? Offer an ADR, framed as: "Want me to record this as an ADR so future architecture reviews don't re-suggest it?" Only offer when the reason would actually be needed by a future explorer to avoid re-suggesting the same thing — skip ephemeral reasons ("not worth it right now") and self-evident ones.
• Want to explore alternative interfaces for the deepened module? Run the /codebase-design skill and use its design-it-twice parallel sub-agent pattern.

Run a stateful /grilling session whose only output is workflow specs. Use the grilling discipline — relentless, one question at a time, a recommended answer attached to each — aimed at the vocabulary and goal below. Create, edit, and delete specs as the grilling resolves things.

The loop lens

A loop is a recurring pattern in the user's life: their career, their week, their morning, a single repeated activity. Picturing a life as loops within loops reveals how predictable its activities really are — which is what makes them worth delegating. Use the lens to find loops worth specifying, and propose ones the user hasn't noticed.

A workflow is the spec of one loop, made real. You run a workflow on a loop — the loop is its running instantiation. Workflows live in workflows/*.md and are the source of truth.

Vocabulary

A shared language, reached for only when a workflow calls for it — never a checklist. Mandate nothing structural: a workflow needs no AI, no checkpoint, and no schedule unless the grilling shows it does.

• Trigger — what fires each run: an event (a new email, a new issue) or a schedule (every morning). Event-triggering is usually the more efficient.
• Checkpoint — a human-in-the-loop point where the user is asked to verify or decide. Some workflows have none and run autonomously; some use no AI at all.
• Push right — defer the checkpoint as far as it will go. Do maximal work before involving the human, so they are asked once, late, with everything prepared.
• Brief — what a checkpoint presents: a tight, decision-ready summary — what was produced, why, and a link down to the asset itself — never the raw output. The user reads a brief, not a draft. Speed of review is imperative.

Definition of done

A workflow spec is done when an implementer agent could build it without asking a single question. Grill until then; nothing is done while a question remains.

The workspace

• workflows/*.md — one spec per workflow.
• NOTES.md — raw notes on the user's world: the tools they use, the channels they process, and their own terminology for both. When it is empty or thin, interview them about their world before specifying anything. Sharpen fuzzy terms into canonical ones as they surface, and record them here.

Migrate to Shoehorn

Why shoehorn?

shoehorn lets you pass partial data in tests while keeping TypeScript happy. It replaces as assertions with type-safe alternatives.

Test code only. Never use shoehorn in production code.

Problems with as in tests:

• Trained not to use it
• Must manually specify target type
• Double-as (as unknown as Type) for intentionally wrong data

Install

npm i @total-typescript/shoehorn

Migration patterns

Large objects with few needed properties

Before:

type Request = {
  body: { id: string };
  headers: Record<string, string>;
  cookies: Record<string, string>;
  // ...20 more properties
};

it("gets user by id", () => {
  // Only care about body.id but must fake entire Request
  getUser({
    body: { id: "123" },
    headers: {},
    cookies: {},
    // ...fake all 20 properties
  });
});

After:

import { fromPartial } from "@total-typescript/shoehorn";

it("gets user by id", () => {
  getUser(
    fromPartial({
      body: { id: "123" },
    }),
  );
});

as Type → fromPartial()

Before:

getUser({ body: { id: "123" } } as Request);

After:

import { fromPartial } from "@total-typescript/shoehorn";

getUser(fromPartial({ body: { id: "123" } }));

as unknown as Type → fromAny()

Before:

getUser({ body: { id: 123 } } as unknown as Request); // wrong type on purpose

After:

import { fromAny } from "@total-typescript/shoehorn";

getUser(fromAny({ body: { id: 123 } }));

When to use each

Workflow

1. Gather requirements - ask user:

   • What test files have as assertions causing problems?
   • Are they dealing with large objects where only some properties matter?
   • Do they need to pass intentionally wrong data for error testing?

2. Install and migrate:

   • Install: npm i @total-typescript/shoehorn
   • Find test files with as assertions: grep -r " as [A-Z]" --include="*.test.ts" --include="*.spec.ts"
   • Replace as Type with fromPartial()
   • Replace as unknown as Type with fromAny()
   • Add imports from @total-typescript/shoehorn
   • Run type check to verify

Obsidian Vault

Vault location

/mnt/d/Obsidian Vault/AI Research/

Mostly flat at root level.

Naming conventions

• Index notes: aggregate related topics (e.g., Ralph Wiggum Index.md, Skills Index.md, RAG Index.md)
• Title case for all note names
• No folders for organization - use links and index notes instead

Linking

• Use Obsidian [[wikilinks]] syntax: [[Note Title]]
• Notes link to dependencies/related notes at the bottom
• Index notes are just lists of [[wikilinks]]

Workflows

Search for notes

# Search by filename
find "/mnt/d/Obsidian Vault/AI Research/" -name "*.md" | grep -i "keyword"

# Search by content
grep -rl "keyword" "/mnt/d/Obsidian Vault/AI Research/" --include="*.md"

Or use Grep/Glob tools directly on the vault path.

Create a new note

1. Use Title Case for filename
2. Write content as a unit of learning (per vault rules)
3. Add [[wikilinks]] to related notes at the bottom
4. If part of a numbered sequence, use the hierarchical numbering scheme

Find related notes

Search for [[Note Title]] across the vault to find backlinks:

grep -rl "\\[\\[Note Title\\]\\]" "/mnt/d/Obsidian Vault/AI Research/"

Find index notes

find "/mnt/d/Obsidian Vault/AI Research/" -name "*Index*"

Prototype

A prototype is throwaway code that answers a question. The question decides the shape.

Pick a branch

Identify which question is being answered — from the user's prompt, the surrounding code, or by asking if the user is around:

• "Does this logic / state model feel right?" → LOGIC.md. Build a tiny interactive terminal app that pushes the state machine through cases that are hard to reason about on paper.
• "What should this look like?" → UI.md. Generate several radically different UI variations on a single route, switchable via a URL search param and a floating bottom bar.

The two branches produce very different artifacts — getting this wrong wastes the whole prototype. If the question is genuinely ambiguous and the user isn't reachable, default to whichever branch better matches the surrounding code (a backend module → logic; a page or component → UI) and state the assumption at the top of the prototype.

Rules that apply to both

1. Throwaway from day one, and clearly marked as such. Locate the prototype code close to where it will actually be used (next to the module or page it's prototyping for) so context is obvious — but name it so a casual reader can see it's a prototype, not production. For throwaway UI routes, obey whatever routing convention the project already uses; don't invent a new top-level structure.
2. One command to run. Whatever the project's existing task runner supports — pnpm <name>, python <path>, bun <path>, etc. The user must be able to start it without thinking.
3. No persistence by default. State lives in memory. Persistence is the thing the prototype is checking, not something it should depend on. If the question explicitly involves a database, hit a scratch DB or a local file with a clear "PROTOTYPE — wipe me" name.
4. Skip the polish. No tests, no error handling beyond what makes the prototype runnable, no abstractions. The point is to learn something fast and then delete it.
5. Surface the state. After every action (logic) or on every variant switch (UI), print or render the full relevant state so the user can see what changed.
6. Delete or absorb when done. When the prototype has answered its question, either delete it or fold the validated decision into the real code — don't leave it rotting in the repo.

When done

The answer is the only thing worth keeping from a prototype. Capture it somewhere durable (commit message, ADR, issue, or a NOTES.md next to the prototype) along with the question it was answering. If the user is around, that capture is a quick conversation; if not, leave the placeholder so they (or you, on the next pass) can fill in the verdict before deleting the prototype.

QA Session

Run an interactive QA session. The user describes problems they're encountering. You clarify, explore the codebase for context, and file GitHub issues that are durable, user-focused, and use the project's domain language.

For each issue the user raises

1. Listen and lightly clarify

Let the user describe the problem in their own words. Ask at most 2-3 short clarifying questions focused on:

• What they expected vs what actually happened
• Steps to reproduce (if not obvious)
• Whether it's consistent or intermittent

Do NOT over-interview. If the description is clear enough to file, move on.

2. Explore the codebase in the background

While talking to the user, kick off an Agent (subagent_type=Explore) in the background to understand the relevant area. The goal is NOT to find a fix — it's to:

• Learn the domain language used in that area (check UBIQUITOUS_LANGUAGE.md)
• Understand what the feature is supposed to do
• Identify the user-facing behavior boundary

This context helps you write a better issue — but the issue itself should NOT reference specific files, line numbers, or internal implementation details.

3. Assess scope: single issue or breakdown?

Before filing, decide whether this is a single issue or needs to be broken down into multiple issues.

Break down when:

• The fix spans multiple independent areas (e.g. "the form validation is wrong AND the success message is missing AND the redirect is broken")
• There are clearly separable concerns that different people could work on in parallel
• The user describes something that has multiple distinct failure modes or symptoms

Keep as a single issue when:

• It's one behavior that's wrong in one place
• The symptoms are all caused by the same root behavior

4. File the GitHub issue(s)

Create issues with gh issue create. Do NOT ask the user to review first — just file and share URLs.

Issues must be durable — they should still make sense after major refactors. Write from the user's perspective.

For a single issue

Use this template:

## What happened

[Describe the actual behavior the user experienced, in plain language]

## What I expected

[Describe the expected behavior]

## Steps to reproduce

1. [Concrete, numbered steps a developer can follow]
2. [Use domain terms from the codebase, not internal module names]
3. [Include relevant inputs, flags, or configuration]

## Additional context

[Any extra observations from the user or from codebase exploration that help frame the issue — e.g. "this only happens when using the Docker layer, not the filesystem layer" — use domain language but don't cite files]

For a breakdown (multiple issues)

Create issues in dependency order (blockers first) so you can reference real issue numbers.

Use this template for each sub-issue:

## Parent issue

#<parent-issue-number> (if you created a tracking issue) or "Reported during QA session"

## What's wrong

[Describe this specific behavior problem — just this slice, not the whole report]

## What I expected

[Expected behavior for this specific slice]

## Steps to reproduce

1. [Steps specific to THIS issue]

## Blocked by

- #<issue-number> (if this issue can't be fixed until another is resolved)

Or "None — can start immediately" if no blockers.

## Additional context

[Any extra observations relevant to this slice]

When creating a breakdown:

• Prefer many thin issues over few thick ones — each should be independently fixable and verifiable
• Mark blocking relationships honestly — if issue B genuinely can't be tested until issue A is fixed, say so. If they're independent, mark both as "None — can start immediately"
• Create issues in dependency order so you can reference real issue numbers in "Blocked by"
• Maximize parallelism — the goal is that multiple people (or agents) can grab different issues simultaneously

Rules for all issue bodies

• No file paths or line numbers — these go stale
• Use the project's domain language (check UBIQUITOUS_LANGUAGE.md if it exists)
• Describe behaviors, not code — "the sync service fails to apply the patch" not "applyPatch() throws on line 42"
• Reproduction steps are mandatory — if you can't determine them, ask the user
• Keep it concise — a developer should be able to read the issue in 30 seconds

After filing, print all issue URLs (with blocking relationships summarized) and ask: "Next issue, or are we done?"

5. Continue the session

Keep going until the user says they're done. Each issue is independent — don't batch them.

This skill will be invoked when the user wants to create a refactor request. You should go through the steps below. You may skip steps if you don't consider them necessary.

1. Ask the user for a long, detailed description of the problem they want to solve and any potential ideas for solutions.

2. Explore the repo to verify their assertions and understand the current state of the codebase.

3. Ask whether they have considered other options, and present other options to them.

4. Interview the user about the implementation. Be extremely detailed and thorough.

5. Hammer out the exact scope of the implementation. Work out what you plan to change and what you plan not to change.

6. Look in the codebase to check for test coverage of this area of the codebase. If there is insufficient test coverage, ask the user what their plans for testing are.

7. Break the implementation into a plan of tiny commits. Remember Martin Fowler's advice to "make each refactoring step as small as possible, so that you can always see the program working."

8. Create a GitHub issue with the refactor plan. Use the following template for the issue description:

Spin up a background agent to do the research, so you keep working while it reads.

Its job:

1. Investigate the question against primary sources — official docs, source code, specs, first-party APIs — not a secondary write-up of them. Follow every claim back to the source that owns it.
2. Write the findings to a single Markdown file, citing each claim's source.
3. Save it where the repo already keeps such notes; match the existing convention, and if there is none, put it somewhere sensible and say where.

1. See the current state of the merge/rebase. Check git history, and the conflicting files.

2. Find the primary sources for each conflict. Understand deeply why each change was made, and what the original intent was. Read the commit messages, check the PRs, check original issues/tickets.

3. Resolve each hunk. Preserve both intents where possible. Where incompatible, pick the one matching the merge's stated goal and note the trade-off. Do not invent new behaviour. Always resolve; never --abort.

4. Discover the project's automated checks and run them — typically typecheck, then tests, then format. Fix anything the merge broke.

5. Finish the merge/rebase. Stage everything and commit. If rebasing, continue the rebase process until all commits are rebased.

Scaffold Exercises

Create exercise directory structures that pass pnpm ai-hero-cli internal lint, then commit with git commit.

Directory naming

• Sections: XX-section-name/ inside exercises/ (e.g., 01-retrieval-skill-building)
• Exercises: XX.YY-exercise-name/ inside a section (e.g., 01.03-retrieval-with-bm25)
• Section number = XX, exercise number = XX.YY
• Names are dash-case (lowercase, hyphens)

Exercise variants

Each exercise needs at least one of these subfolders:

• problem/ - student workspace with TODOs
• solution/ - reference implementation
• explainer/ - conceptual material, no TODOs

When stubbing, default to explainer/ unless the plan specifies otherwise.

Required files

Each subfolder (problem/, solution/, explainer/) needs a readme.md that:

• Is not empty (must have real content, even a single title line works)
• Has no broken links

When stubbing, create a minimal readme with a title and a description:

# Exercise Title

Description here

If the subfolder has code, it also needs a main.ts (>1 line). But for stubs, a readme-only exercise is fine.

Workflow

1. Parse the plan - extract section names, exercise names, and variant types
2. Create directories - mkdir -p for each path
3. Create stub readmes - one readme.md per variant folder with a title
4. Run lint - pnpm ai-hero-cli internal lint to validate
5. Fix any errors - iterate until lint passes

Lint rules summary

The linter (pnpm ai-hero-cli internal lint) checks:

• Each exercise has subfolders (problem/, solution/, explainer/)
• At least one of problem/, explainer/, or explainer.1/ exists
• readme.md exists and is non-empty in the primary subfolder
• No .gitkeep files
• No speaker-notes.md files
• No broken links in readmes
• No pnpm run exercise commands in readmes
• main.ts required per subfolder unless it's readme-only

Moving/renaming exercises

When renumbering or moving exercises:

1. Use git mv (not mv) to rename directories - preserves git history
2. Update the numeric prefix to maintain order
3. Re-run lint after moves

Example:

git mv exercises/01-retrieval/01.03-embeddings exercises/01-retrieval/01.04-embeddings

Example: stubbing from a plan

Given a plan like:

Section 05: Memory Skill Building
- 05.01 Introduction to Memory
- 05.02 Short-term Memory (explainer + problem + solution)
- 05.03 Long-term Memory

Create:

mkdir -p exercises/05-memory-skill-building/05.01-introduction-to-memory/explainer
mkdir -p exercises/05-memory-skill-building/05.02-short-term-memory/{explainer,problem,solution}
mkdir -p exercises/05-memory-skill-building/05.03-long-term-memory/explainer

Then create readme stubs:

exercises/05-memory-skill-building/05.01-introduction-to-memory/explainer/readme.md -> "# Introduction to Memory"
exercises/05-memory-skill-building/05.02-short-term-memory/explainer/readme.md -> "# Short-term Memory"
exercises/05-memory-skill-building/05.02-short-term-memory/problem/readme.md -> "# Short-term Memory"
exercises/05-memory-skill-building/05.02-short-term-memory/solution/readme.md -> "# Short-term Memory"
exercises/05-memory-skill-building/05.03-long-term-memory/explainer/readme.md -> "# Long-term Memory"

Setup Matt Pocock's Skills

Scaffold the per-repo configuration that the engineering skills assume:

• Issue tracker — where issues live (GitHub by default; local markdown is also supported out of the box)
• Triage labels — the strings used for the five canonical triage roles
• Domain docs — where CONTEXT.md and ADRs live, and the consumer rules for reading them

This is a prompt-driven skill, not a deterministic script. Explore, present what you found, confirm with the user, then write.

Process

1. Explore

Look at the current repo to understand its starting state. Read whatever exists; don't assume:

• git remote -v and .git/config — is this a GitHub repo? Which one?
• AGENTS.md and CLAUDE.md at the repo root — does either exist? Is there already an ## Agent skills section in either?
• CONTEXT.md and CONTEXT-MAP.md at the repo root
• docs/adr/ and any src/*/docs/adr/ directories
• docs/agents/ — does this skill's prior output already exist?
• .scratch/ — sign that a local-markdown issue tracker convention is already in use

2. Present findings and ask

Summarise what's present and what's missing. Then walk the user through the three decisions one at a time — present a section, get the user's answer, then move to the next. Don't dump all three at once.

Assume the user does not know what these terms mean. Each section starts with a short explainer (what it is, why these skills need it, what changes if they pick differently). Then show the choices and the default.

Section A — Issue tracker.

Explainer: The "issue tracker" is where issues live for this repo. Skills like to-tickets, triage, to-spec, and qa read from and write to it — they need to know whether to call gh issue create, write a markdown file under .scratch/, or follow some other workflow you describe. Pick the place you actually track work for this repo.

Default posture: these skills were designed for GitHub. If a git remote points at GitHub, propose that. If a git remote points at GitLab (gitlab.com or a self-hosted host), propose GitLab. Otherwise (or if the user prefers), offer:

• GitHub — issues live in the repo's GitHub Issues (uses the gh CLI)
• GitLab — issues live in the repo's GitLab Issues (uses the glab CLI)
• Local markdown — issues live as files under .scratch/<feature>/ in this repo (good for solo projects or repos without a remote)
• Other (Jira, Linear, etc.) — ask the user to describe the workflow in one paragraph; the skill will record it as freeform prose

If — and only if — the user picked GitHub or GitLab, ask one follow-up:

Explainer: Open-source repos often receive feature requests as pull requests, not just issues — a PR is an issue with attached code. If you turn this on, /triage pulls external PRs into the same queue and runs them through the same labels and states as issues (collaborators' in-flight PRs are left alone). Leave it off if PRs aren't a request surface for you.

• PRs as a request surface — yes / no (default: no). Record the answer in docs/agents/issue-tracker.md. For local-markdown and other trackers, skip this question — there are no PRs.

Section B — Triage label vocabulary.

Explainer: When the triage skill processes an incoming issue, it moves it through a state machine — needs evaluation, waiting on reporter, ready for an AFK agent to pick up, ready for a human, or won't fix. To do that, it needs to apply labels (or the equivalent in your issue tracker) that match strings you've actually configured. If your repo already uses different label names (e.g. bug:triage instead of needs-triage), map them here so the skill applies the right ones instead of creating duplicates.

The five canonical roles:

• needs-triage — maintainer needs to evaluate
• needs-info — waiting on reporter
• ready-for-agent — fully specified, AFK-ready (an agent can pick it up with no human context)
• ready-for-human — needs human implementation
• wontfix — will not be actioned

Default: each role's string equals its name. Ask the user if they want to override any. If their issue tracker has no existing labels, the defaults are fine.

Section C — Domain docs.

Explainer: Some skills (improve-codebase-architecture, diagnosing-bugs, tdd) read a CONTEXT.md file to learn the project's domain language, and docs/adr/ for past architectural decisions. They need to know whether the repo has one global context or multiple (e.g. a monorepo with separate frontend/backend contexts) so they look in the right place.

Confirm the layout:

• Single-context — one CONTEXT.md + docs/adr/ at the repo root. Most repos are this.
• Multi-context — CONTEXT-MAP.md at the root pointing to per-context CONTEXT.md files (typically a monorepo).

3. Confirm and edit

Show the user a draft of:

• The ## Agent skills block to add to whichever of CLAUDE.md / AGENTS.md is being edited (see step 4 for selection rules)
• The contents of docs/agents/issue-tracker.md, docs/agents/triage-labels.md, docs/agents/domain.md

Let them edit before writing.

4. Write

Pick the file to edit:

• If CLAUDE.md exists, edit it.
• Else if AGENTS.md exists, edit it.
• If neither exists, ask the user which one to create — don't pick for them.

Never create AGENTS.md when CLAUDE.md already exists (or vice versa) — always edit the one that's already there.

If an ## Agent skills block already exists in the chosen file, update its contents in-place rather than appending a duplicate. Don't overwrite user edits to the surrounding sections.

The block:

## Agent skills

### Issue tracker

[one-line summary of where issues are tracked, plus whether external PRs are a triage surface]. See `docs/agents/issue-tracker.md`.

### Triage labels

[one-line summary of the label vocabulary]. See `docs/agents/triage-labels.md`.

### Domain docs

[one-line summary of layout — "single-context" or "multi-context"]. See `docs/agents/domain.md`.

Then write the three docs files using the seed templates in this skill folder as a starting point:

• issue-tracker-github.md — GitHub issue tracker
• issue-tracker-gitlab.md — GitLab issue tracker
• issue-tracker-local.md — local-markdown issue tracker
• triage-labels.md — label mapping
• domain.md — domain doc consumer rules + layout

For "other" issue trackers, write docs/agents/issue-tracker.md from scratch using the user's description.

5. Done

Tell the user the setup is complete and which engineering skills will now read from these files. Mention they can edit docs/agents/*.md directly later — re-running this skill is only necessary if they want to switch issue trackers or restart from scratch.

Setup Pre-Commit Hooks

What This Sets Up

• Husky pre-commit hook
• lint-staged running Prettier on all staged files
• Prettier config (if missing)
• typecheck and test scripts in the pre-commit hook

Steps

1. Detect package manager

Check for package-lock.json (npm), pnpm-lock.yaml (pnpm), yarn.lock (yarn), bun.lockb (bun). Use whichever is present. Default to npm if unclear.

2. Install dependencies

Install as devDependencies:

husky lint-staged prettier

3. Initialize Husky

npx husky init

This creates .husky/ dir and adds prepare: "husky" to package.json.

4. Create .husky/pre-commit

Write this file (no shebang needed for Husky v9+):

npx lint-staged
npm run typecheck
npm run test

Adapt: Replace npm with detected package manager. If repo has no typecheck or test script in package.json, omit those lines and tell the user.

5. Create .lintstagedrc

{
  "*": "prettier --ignore-unknown --write"
}

6. Create .prettierrc (if missing)

Only create if no Prettier config exists. Use these defaults:

{
  "useTabs": false,
  "tabWidth": 2,
  "printWidth": 80,
  "singleQuote": false,
  "trailingComma": "es5",
  "semi": true,
  "arrowParens": "always"
}

7. Verify

• .husky/pre-commit exists and is executable
• .lintstagedrc exists
• prepare script in package.json is "husky"
• prettier config exists
• Run npx lint-staged to verify it works

8. Commit

Stage all changed/created files and commit with message: Add pre-commit hooks (husky + lint-staged + prettier)

This will run through the new pre-commit hooks — a good smoke test that everything works.

Notes

• Husky v9+ doesn't need shebangs in hook files
• prettier --ignore-unknown skips files Prettier can't parse (images, etc.)
• The pre-commit runs lint-staged first (fast, staged-only), then full typecheck and tests

Test-Driven Development

TDD is the red → green loop. This skill is the reference that makes that loop produce tests worth keeping: what a good test is, where tests go, the anti-patterns, and the rules of the loop. Every section applies on every cycle — consult them before and during the loop, not after.

When exploring the codebase, read CONTEXT.md (if it exists) so test names and interface vocabulary match the project's domain language, and respect ADRs in the area you're touching.

What a good test is

Tests verify behavior through public interfaces, not implementation details. Code can change entirely; tests shouldn't. A good test reads like a specification — "user can checkout with valid cart" tells you exactly what capability exists — and survives refactors because it doesn't care about internal structure.

See tests.md for examples and mocking.md for mocking guidelines.

Seams — where tests go

A seam is the public boundary you test at: the interface where you observe behavior without reaching inside. Tests live at seams, never against internals.

Test only at pre-agreed seams. Before writing any test, write down the seams under test and confirm them with the user. No test is written at an unconfirmed seam. You can't test everything — agreeing the seams up front is how testing effort lands on the critical paths and complex logic instead of every edge case.

Ask: "What's the public interface, and which seams should we test?"

Anti-patterns

• Implementation-coupled — mocks internal collaborators, tests private methods, or verifies through a side channel (querying the database instead of using the interface). The tell: the test breaks when you refactor but behavior hasn't changed.
• Tautological — the assertion recomputes the expected value the way the code does (expect(add(a, b)).toBe(a + b), a snapshot derived by hand the same way, a constant asserted equal to itself), so it passes by construction and can never disagree with the code. Expected values must come from an independent source of truth — a known-good literal, a worked example, the spec.
• Horizontal slicing — writing all tests first, then all implementation. Bulk tests verify imagined behavior: you test the shape of things rather than user-facing behavior, the tests go insensitive to real changes, and you commit to test structure before understanding the implementation. Work in vertical slices instead — one test → one implementation → repeat, each test a tracer bullet that responds to what the last cycle taught you.

Rules of the loop

• Red before green. Write the failing test first, then only enough code to pass it. Don't anticipate future tests or add speculative features.
• One slice at a time. One seam, one test, one minimal implementation per cycle.
• Refactoring is not part of the loop. It belongs to the review stage (see the code-review skill), not the red → green implementation cycle.

The user has asked you to teach them something. This is a stateful request - they intend to learn the topic over multiple sessions.

Teaching Workspace

Treat the current directory as a teaching workspace. The state of their learning is captured in this directory in several files:

• MISSION.md: A document capturing the reason the user is interested in the topic. This should be used to ground all teaching. Use the format in MISSION-FORMAT.md.
• ./reference/*.html: A directory of reference materials. These are the compressed learnings from the lessons - cheat sheets, reference algorithms, syntax, yoga poses, glossaries. They are the raw units of learning. They should be beautiful documents which print out well, and are designed for quick reference.
• RESOURCES.md: A list of resources which can be explored to ground your teaching in contextual knowledge, or to acquire knowledge and wisdom. Use the format in RESOURCES-FORMAT.md.
• ./learning-records/*.md: A directory of learning records, which capture what the user has learned. These are loosely equivalent to architectural decision records in software development - they capture non-obvious lessons and key insights that may need to be revised later, or drive future sessions. These should be used to calculate the zone of proximal development. They are titled 0001-<dash-case-name>.md, where the number increments each time. Use the format in LEARNING-RECORD-FORMAT.md.
• ./lessons/*.html: A directory of lessons. A lesson is a single, self-contained HTML output that teaches one tightly-scoped thing tied to the mission. This is the primary unit of teaching in this workspace.
• ./assets/*: Reusable components shared across lessons. See Assets.
• NOTES.md: A scratchpad for you to jot down user preferences, or working notes.

Philosophy

To learn at a deep level, the user needs three things:

• Knowledge, captured from high-quality, high-trust resources
• Skills, acquired through highly-relevant interactive lessons devised by you, based on the knowledge
• Wisdom, which comes from interacting with other learners and practitioners

Before the RESOURCES.md is well-populated, your focus should be to find high-quality resources which will help the user acquire knowledge. Never trust your parametric knowledge.

Some topics may require more skills than knowledge. Learning more about theoretical physics might be more knowledge-based. For yoga, more skills-based.

Fluency vs Storage Strength

You should be careful to split between two types of learning:

• Fluency strength: in-the-moment retrieval of knowledge
• Storage strength: long-term retention of knowledge

Fluency can give the user an illusory sense of mastery, but storage strength is the real goal. Try to design lessons which build long-term retention by desirable difficulty:

• Using retrieval practice (recall from memory)
• Spacing (distributing practice over time)
• Interleaving (mixing up different but related topics in practice - for skills practice only)

Lessons

A lesson is the main thing you produce — the unit in which knowledge and skills reach the user. Each lesson is one self-contained HTML file, saved to ./lessons/ and titled 0001-<dash-case-name>.html where the number increments each time.

A lesson should be beautiful — clean, readable typography and layout — since the user will return to these later to review. Think Tufte.

The lesson should be short, and completable very quickly. Learners' working memory is very small, and we need to stay within it. But each lesson should give the user a single tangible win that they can build on. It should be directly tied to the mission, and should be in the user's zone of proximal development.

If possible, open the lesson file for the user by running a CLI command.

Each lesson should link via HTML anchors to other lessons and reference documents.

Each lesson should recommend a primary source for the user to read or watch. This should be the most high-quality, high-trust resource you found on the topic.

Each lesson should contain a reminder to ask followup questions to the agent. The agent is their teacher, and can assist with anything that's unclear.

Assets

Lessons are built from reusable components, stored in ./assets/: stylesheets, quiz widgets, simulators, diagram helpers — anything a second lesson could reuse.

Reuse is the default, not the exception. Before authoring a lesson, read ./assets/ and build from the components already there. When a lesson needs something new and reusable, write it as a component in ./assets/ and link to it — never inline code a future lesson would duplicate.

A shared stylesheet is the first component every workspace earns: every lesson links it, so the lessons look like one consistent course rather than a pile of one-offs. As the workspace grows, so should the component library.

The Mission

Every lesson should be tied into the mission - the reason that the user is interested in learning about the topic.

If the user is unclear about the mission, or the MISSION.md is not populated, your first job should be to question the user on why they want to learn this.

Failing to understand the mission will mean knowledge acquisition is not grounded in real-world goals. Lessons will feel too abstract. You will have no way of judging what the user should do next.

Missions may change as the user develops more skills and knowledge. This is normal - make sure to update the MISSION.md and add a learning record to capture the change. Confirm with the user before changing the mission.

Zone Of Proximal Development

Each lesson, the user should always feel as if they are being challenged 'just enough'.

The user may specify an exact thing they want to learn. If they don't, figure out their zone of proximal development by:

• Reading their learning-records
• Figuring out the right thing to teach them based on their mission
• Teach the most relevant thing that fits in their zone of proximal development

Knowledge

Lessons should be designed around a skill the user is going to learn. The knowledge in the lesson should be only what's required to acquire that skill. You teach the knowledge first, then get the user to practice the skills via an interactive feedback loop.

Knowledge should first be gathered from trusted resources. Use RESOURCES.md to keep track of them. Lessons should be littered with citations - links to external resources to back up any claim made. This increases the trustworthiness of the lesson.

For acquiring knowledge, difficulty is the enemy. It eats working memory you need for understanding.

Skills

If knowledge is all about acquisition, skills are about durability and flexibility. Make the knowledge stick.

For skill acquisition, difficulty is the tool. Effortful retrieval is what builds storage strength. Skills should be taught through interactive lessons. There are several tools at your disposal:

• Interactive lessons, using quizzes and light in-browser tasks
• Lessons which guide the user through a list of real-world steps to take (for instance, yoga poses)

Each of these should be based on a feedback loop, where the user receives feedback on their performance. This feedback loop should be as tight as possible, giving feedback immediately - and ideally automatically.

For quizzes, each answer should be exactly the same number of words (and characters, if possible). Don't give the user any clues about the answer through formatting.

Acquiring Wisdom

Wisdom comes from true real-world interaction - testing your skills outside the learning environment.

When the user asks a question that appears to require wisdom, your default posture should be to attempt to answer - but to ultimately delegate to a community.

A community is a place (online or offline) where the user can test their skills in the real world. This might be a forum, a subreddit, a real-world class (budget permitting) or a local interest group.

You should attempt to find high-reputation communities the user can join. If the user expresses a preference that they don't want to join a community, respect it.

Reference Documents

While creating lessons, you should also create reference documents. Lessons can reference these documents - they are useful for tracking raw units of knowledge useful across lessons.

Lessons will rarely be revisited later - reference documents will be. They should be the compressed essence of the lesson, in a format designed for quick reference.

Some learning topics lend themselves to reference:

• Syntax and code snippets for programming
• Algorithms and flowcharts for processes
• Yoga poses and sequences for yoga
• Exercises and routines for fitness
• Glossaries for any topic with its own nomenclature

Glossaries, in particular, are an essential reference. Once one is created, it should be adhered to in every lesson.

NOTES.md

The user will sometimes express preferences of how they want to be taught, or things you should keep in mind. This is the place to record those preferences, so you can refer back to them when designing lessons or working with the user.

This skill takes the current conversation context and codebase understanding and produces a spec (you may know this document as a PRD). Do NOT interview the user — just synthesize what you already know.

The issue tracker and triage label vocabulary should have been provided to you — run /setup-matt-pocock-skills if not.

Process

1. Explore the repo to understand the current state of the codebase, if you haven't already. Use the project's domain glossary vocabulary throughout the spec, and respect any ADRs in the area you're touching.

2. Sketch out the seams at which you're going to test the feature. Existing seams should be preferred to new ones. Use the highest seam possible. If new seams are needed, propose them at the highest point you can. The fewer seams across the codebase, the better - the ideal number is one.

Check with the user that these seams match their expectations.

3. Write the spec using the template below, then publish it to the project issue tracker. Apply the ready-for-agent triage label - no need for additional triage.

To Tickets

Break a plan, spec, or conversation into a set of tickets — tracer-bullet vertical slices, each declaring the tickets that block it.

The issue tracker and triage label vocabulary should have been provided to you — run /setup-matt-pocock-skills if not.

Process

1. Gather context

Work from whatever is already in the conversation context. If the user passes a reference (a spec path, an issue number or URL) as an argument, fetch it and read its full body and comments.

2. Explore the codebase (optional)

If you have not already explored the codebase, do so to understand the current state of the code. Ticket titles and descriptions should use the project's domain glossary vocabulary, and respect ADRs in the area you're touching.

Look for opportunities to prefactor the code to make the implementation easier. "Make the change easy, then make the easy change."

3. Draft vertical slices

Break the work into tracer bullet tickets.

Give each ticket its blocking edges — the other tickets that must complete before it can start. A ticket with no blockers can start immediately.

Wide refactors are the exception to vertical slicing. A wide refactor is one mechanical change — rename a column, retype a shared symbol — whose blast radius fans across the whole codebase, so a single edit breaks thousands of call sites at once and no vertical slice can land green. Don't force it into a tracer bullet; sequence it as expand–contract. First expand: add the new form beside the old so nothing breaks. Then migrate the call sites over in batches sized by blast radius (per package, per directory), each batch its own ticket blocked by the expand, keeping CI green batch to batch because the old form still exists. Finally contract: delete the old form once no caller remains, in a ticket blocked by every migrate batch. When even the batches can't stay green alone, keep the sequence but let them share an integration branch that all block a final integrate-and-verify ticket — green is promised only there.

4. Quiz the user

Present the proposed breakdown as a numbered list. For each ticket, show:

• Title: short descriptive name
• Blocked by: which other tickets (if any) must complete first
• What it delivers: the end-to-end behaviour this ticket makes work

Ask the user:

• Does the granularity feel right? (too coarse / too fine)
• Are the blocking edges correct — does each ticket only depend on tickets that genuinely gate it?
• Should any tickets be merged or split further?

Iterate until the user approves the breakdown.

5. Publish the tickets to the configured tracker

Publish the approved tickets. How depends on the tracker /setup-matt-pocock-skills configured — the tickets are the same either way, only the shape of the blocking edges changes:

• Local files → write one tickets.md in the repo root, all tickets in dependency order (blockers first), each with its "Blocked by" listing the titles it depends on. Use the file template below.
• A real issue tracker (GitHub, Linear, …) → publish one issue per ticket in dependency order (blockers first) so each ticket's blocking edges can reference real identifiers. Use the platform's native blocking / sub-issue relationship where it has one; otherwise set each ticket's "Blocked by" to the blocking issues. Apply the ready-for-agent triage label unless instructed otherwise — the tickets are agent-grabbable by construction.

Do NOT close or modify any parent issue.

In either form, avoid specific file paths or code snippets — they go stale fast. Exception: if a prototype produced a snippet that encodes a decision more precisely than prose can (state machine, reducer, schema, type shape), inline it and note briefly that it came from a prototype. Trim to the decision-rich parts — not a working demo, just the important bits.

Work the frontier one ticket at a time with /implement, clearing context between tickets.

Triage

Move issues on the project issue tracker through a small state machine of triage roles.

If this repo treats external pull requests as a request surface (see the issue-tracker config), triage covers them too: a PR is an issue with attached code — same roles, same states, same machine, with a few deltas marked "for a PR" below. Resolve a bare #42 to an issue or PR per the tracker config.

Every comment or issue posted to the issue tracker during triage must start with this disclaimer:

> *This was generated by AI during triage.*

Reference docs

• AGENT-BRIEF.md — how to write durable agent briefs
• OUT-OF-SCOPE.md — how the .out-of-scope/ knowledge base works

Roles

Two category roles:

• bug — something is broken
• enhancement — new feature or improvement

Five state roles:

• needs-triage — maintainer needs to evaluate
• needs-info — waiting on reporter for more information
• ready-for-agent — fully specified, ready for an AFK agent
• ready-for-human — needs human implementation
• wontfix — will not be actioned

For a PR, the same states read against the attached code: ready-for-agent means a brief is attached and an agent should take the next step on the diff; ready-for-human means it's ready for a human to merge.

Every triaged issue should carry exactly one category role and one state role. If state roles conflict, flag it and ask the maintainer before doing anything else.

These are canonical role names — the actual label strings used in the issue tracker may differ. The mapping should have been provided to you - run /setup-matt-pocock-skills if not.

State transitions: an unlabeled issue normally goes to needs-triage first; from there it moves to needs-info, ready-for-agent, ready-for-human, or wontfix. needs-info returns to needs-triage once the reporter replies. The maintainer can override at any time — flag transitions that look unusual and ask before proceeding.

Invocation

The maintainer invokes /triage and describes what they want in natural language. Interpret the request and act. Examples:

• "Show me anything that needs my attention"
• "Let's look at #42" (issue or PR)
• "Move #42 to ready-for-agent"
• "What's ready for agents to pick up?"

Show what needs attention

Query the issue tracker and present three buckets, oldest first:

1. Unlabeled — never triaged.
2. needs-triage — evaluation in progress.
3. needs-info with reporter activity since the last triage notes — needs re-evaluation.

When PRs are in scope, include external PRs in these buckets and tag each line [PR] or [issue]. Discovery surfaces only external PRs (the tracker config defines who counts as external) — a collaborator's in-flight PR is not triage work. This filter is discovery-only; an explicitly named PR is always triaged regardless of author.

Show counts and a one-line summary per item. Let the maintainer pick.

Triage a specific issue or PR

1. Gather context. Read the full issue or PR (body, comments, labels, author, dates; for a PR, the diff too). Parse any prior triage notes so you don't re-ask resolved questions. Explore the codebase using the project's domain glossary, respecting ADRs in the area. Run two checks against the codebase: (a) redundancy — search for an existing implementation of the requested behavior by domain concept (not just the request's wording), and report where you looked. If found, it's an already-implemented wontfix (step 5). (b) prior rejection — read .out-of-scope/*.md and surface any that resembles this request.

2. Recommend. Tell the maintainer your category and state recommendation with reasoning, plus a brief codebase summary relevant to the request — including whether it's already implemented. Wait for direction.

3. Verify the claim. Before any grilling, check that the claim holds up. For a bug, reproduce it from the reporter's steps. For a PR, confirm the diff does what it claims — check it out, run the relevant tests or commands. Report what happened: confirmed (with code path), failed, or insufficient detail (a strong needs-info signal). A confirmed verification makes a much stronger agent brief.

4. Grill (if needed). If the request needs fleshing out, run the /grilling and /domain-modeling skills together — grill it into shape one question at a time, sharpening domain terms and updating CONTEXT.md/ADRs inline as decisions land.

5. Apply the outcome:

   • ready-for-agent — post an agent brief comment (AGENT-BRIEF.md).
   • ready-for-human — same structure as an agent brief, but note why it can't be delegated (judgment calls, external access, design decisions, manual testing).
   • needs-info — post triage notes (template below).
   • wontfix — close, with the comment depending on why:
     • Already implemented — the change already exists in the codebase. Point to where it lives; do not write to .out-of-scope/ (that KB is for rejected requests, not built ones).
     • Rejected (bug) — polite explanation, then close.
     • Rejected (enhancement) — write to .out-of-scope/, link to it from a comment, then close (OUT-OF-SCOPE.md).
   • needs-triage — apply the role. Optional comment if there's partial progress.

Quick state override

If the maintainer says "move #42 to ready-for-agent", trust them and apply the role directly. Confirm what you're about to do (role changes, comment, close), then act. Skip grilling. If moving to ready-for-agent without a grilling session, ask whether they want to write an agent brief.

Needs-info template

## Triage Notes

**What we've established so far:**

- point 1
- point 2

**What we still need from you (@reporter):**

- question 1
- question 2

Capture everything resolved during grilling under "established so far" so the work isn't lost. Questions must be specific and actionable, not "please provide more info".

Resuming a previous session

If prior triage notes exist on the issue or PR, read them, check whether the reporter has answered any outstanding questions, and present an updated picture before continuing. Don't re-ask resolved questions.

Ubiquitous Language

Extract and formalize domain terminology from the current conversation into a consistent glossary, saved to a local file.

Process

1. Scan the conversation for domain-relevant nouns, verbs, and concepts
2. Identify problems:
   • Same word used for different concepts (ambiguity)
   • Different words used for the same concept (synonyms)
   • Vague or overloaded terms
3. Propose a canonical glossary with opinionated term choices
4. Write to UBIQUITOUS_LANGUAGE.md in the working directory using the format below
5. Output a summary inline in the conversation

Output Format

Write a UBIQUITOUS_LANGUAGE.md file with this structure:

# Ubiquitous Language

## Order lifecycle

| Term        | Definition                                              | Aliases to avoid      |
| ----------- | ------------------------------------------------------- | --------------------- |
| **Order**   | A customer's request to purchase one or more items      | Purchase, transaction |
| **Invoice** | A request for payment sent to a customer after delivery | Bill, payment request |

## People

| Term         | Definition                                  | Aliases to avoid       |
| ------------ | ------------------------------------------- | ---------------------- |
| **Customer** | A person or organization that places orders | Client, buyer, account |
| **User**     | An authentication identity in the system    | Login, account         |

## Relationships

- An **Invoice** belongs to exactly one **Customer**
- An **Order** produces one or more **Invoices**

## Example dialogue

> **Dev:** "When a **Customer** places an **Order**, do we create the **Invoice** immediately?"
> **Domain expert:** "No — an **Invoice** is only generated once a **Fulfillment** is confirmed. A single **Order** can produce multiple **Invoices** if items ship in separate **Shipments**."
> **Dev:** "So if a **Shipment** is cancelled before dispatch, no **Invoice** exists for it?"
> **Domain expert:** "Exactly. The **Invoice** lifecycle is tied to the **Fulfillment**, not the **Order**."

## Flagged ambiguities

- "account" was used to mean both **Customer** and **User** — these are distinct concepts: a **Customer** places orders, while a **User** is an authentication identity that may or may not represent a **Customer**.

Rules

• Be opinionated. When multiple words exist for the same concept, pick the best one and list the others as aliases to avoid.
• Flag conflicts explicitly. If a term is used ambiguously in the conversation, call it out in the "Flagged ambiguities" section with a clear recommendation.
• Only include terms relevant for domain experts. Skip the names of modules or classes unless they have meaning in the domain language.
• Keep definitions tight. One sentence max. Define what it IS, not what it does.
• Show relationships. Use bold term names and express cardinality where obvious.
• Only include domain terms. Skip generic programming concepts (array, function, endpoint) unless they have domain-specific meaning.
• Group terms into multiple tables when natural clusters emerge (e.g. by subdomain, lifecycle, or actor). Each group gets its own heading and table. If all terms belong to a single cohesive domain, one table is fine — don't force groupings.
• Write an example dialogue. A short conversation (3-5 exchanges) between a dev and a domain expert that demonstrates how the terms interact naturally. The dialogue should clarify boundaries between related concepts and show terms being used precisely.

Re-running

When invoked again in the same conversation:

1. Read the existing UBIQUITOUS_LANGUAGE.md
2. Incorporate any new terms from subsequent discussion
3. Update definitions if understanding has evolved
4. Re-flag any new ambiguities
5. Rewrite the example dialogue to incorporate new terms

A loose idea has arrived — too big for one agent session, and wrapped in fog: the way from here to the destination isn't visible yet. Wayfinding is about finding that way, not charging at the destination. This skill charts the way as a shared map on the repo's issue tracker, then works its tickets one at a time until the route is clear.

The destination varies per effort, and naming it is the first act of charting — it shapes every ticket. It might be a spec to hand off and iterate on, a decision to lock before planning starts, or a change made in place like a data-structure migration. The map is domain-agnostic — engineering work, course content, whatever fits the shape.

Plan, don't do

Wayfinder is planning by default: each ticket resolves a decision, and the map is done when the way is clear — nothing left to decide before someone goes and does the thing. The pull to just do the work is usually the signal you've reached the edge of the map and it's time to hand off. An effort can override this in its Notes — carrying execution into the map itself — but absent that, produce decisions, not deliverables.

Refer by name

Every map and ticket is an issue, so it has a name — its title. In everything the human reads — narration, the map's Decisions-so-far — refer to it by that name, never by a bare id, number, or slug. A wall of #42, #43, #44 is illegible; names read at a glance. The id and URL don't vanish — a name wraps its link — but they ride inside the name, never stand in for it.

The Map

The map is a single issue on this repo's issue tracker, labelled wayfinder:map — the canonical artifact. Its tickets are child issues of the map.

The map is an index, not a store. It lists the decisions made and points at the tickets that hold their detail; a decision lives in exactly one place — its ticket — so the map never restates it, only gists it and links.

Where the map, its child tickets, blocking, and frontier queries physically live is tracker-specific. The issue tracker should have been provided to you — run /setup-matt-pocock-skills if not. Consult the tracker doc's "Wayfinding operations" section for how this repo expresses them. If no tracker has been provided, default to the local-markdown tracker.

The map body

The whole map at low resolution, loaded once per session. Open tickets are not listed — they are open child issues, found by query.

## Destination

<what reaching the end of this map looks like — the spec, decision, or change this effort is finding its way to. One or two lines; every session orients to it before choosing a ticket.>

## Notes

<domain; skills every session should consult; standing preferences for this effort>

## Decisions so far

<!-- the index — one line per closed ticket: enough to judge relevance, then zoom the link for the detail the ticket holds -->

- [<closed ticket title>](link) — <one-line gist of the answer>

## Not yet specified

<!-- see "Fog of war": in-scope fog you can't ticket yet; graduates as the frontier advances -->

## Out of scope

<!-- see "Out of scope": work ruled beyond the destination; closed, never graduates -->

Tickets

Each ticket is a child issue of the map; the tracker's issue id is its identity. Its body is the question, sized to one 100K token agent session:

## Question

<the decision or investigation this ticket resolves>

Each ticket carries a wayfinder:<type> label — one of research, prototype, grilling, task (see Ticket Types).

A session claims a ticket by assigning it to the dev driving the map, first, before any work, so concurrent sessions skip it. That assignee is the claim: an open, unassigned ticket is unclaimed.

Blocking uses the tracker's native dependency relationship — essential because it renders the frontier visually in the tracker's own UI, so the human sees what's takeable without opening the map. Only a tracker that lacks native blocking falls back to a body convention. A ticket is unblocked when every ticket blocking it is closed; the frontier is the open, unblocked, unclaimed children — the edge of the known.

The answer isn't part of the body — it's recorded on resolution (see Work through the map). Assets created while resolving a ticket are linked from the issue, not pasted in.

Ticket Types

Every ticket is either HITL — human in the loop, worked with a human who speaks for themselves — or AFK, driven by the agent alone. A HITL ticket only resolves through that live exchange; the agent never stands in for the human's side of it (a grilling agent that answers its own questions has broken this).

• Research (AFK): Reading documentation, third-party APIs, or local resources like knowledge bases. Creates a markdown summary as a linked asset. Use when knowledge outside the current working directory is required.
• Prototype (HITL): Raise the fidelity of the discussion by making a cheap, rough, concrete artifact to react to — an outline, a rough take, a stub, or UI/logic code via the /prototype skill. Links the prototype as an asset. Use when "how should it look" or "how should it behave" is the key question.
• Grilling (HITL): Conversation via the /grilling and /domain-modeling skills, one question at a time. The default case.
• Task (HITL or AFK): Manual work that must happen before a decision can be made — nothing to decide, prototype, or research, but the discussion is blocked until it's done. Signing up for a service so its API can be judged, provisioning access, moving data so its shape can be seen. This is the one type that does rather than decides — and it earns its place by unblocking a decision, not by delivering the destination. The agent drives it alone where it can (AFK); otherwise it hands the human a precise checklist (HITL). Resolved when the work is done; the answer records what was done and any resulting facts (credentials location, new URLs, row counts) later tickets depend on.

Fog of war

The map is deliberately incomplete: don't chart what you can't yet see. Beyond the live tickets lies the fog of war — the dim view of decisions and investigations you can tell are coming but can't yet pin down, because they hang on questions still open. Resolving a ticket clears the fog ahead of it, graduating whatever's now specifiable into fresh tickets — one at a time, until the way to the destination is clear and no tickets remain.

The map's Not yet specified section is where that dim view is written down: the suspected question, the area to revisit later. It's the undiscovered frontier toward the destination — everything here is in scope, just not sharp enough to ticket. Write as loosely or as fully as the view allows; it doubles as a signpost for collaborators reading where the effort is headed.

Fog or ticket? The test is whether you can state the question precisely now — not whether you can answer it now.

• Ticket when the question is already sharp — even if it's blocked and you can't act on it yet.
• Not yet specified when you can't yet phrase it that sharply. Don't pre-slice the fog into ticket-sized pieces: it's coarser than a ticket, and one patch may graduate into several tickets, or none, once the frontier reaches it.

Not yet specified excludes what's already decided (Decisions so far), what's already a live ticket, and what's out of scope (the next section).

Out of scope

Fog only ever gathers toward the destination. The destination fixes the scope, so work beyond it is out of scope — it isn't fog, and it doesn't belong in Not yet specified. It gets its own Out of scope section on the map: work you've consciously ruled out of this effort. Scope, not sharpness, lands it here.

Out-of-scope work never graduates — the frontier stops at the destination — so it returns only if the destination is redrawn, and then as a fresh effort, not a resumption.

Ruling something out of scope is a scoping act, not a step on the route. When a ticket that already exists turns out to sit past the destination — mis-scoped in while charting, or exposed by a resolution — close it (a closed ticket is unambiguously off the frontier) and leave one line in the Out of scope section: the gist plus why it's out of scope, linking the closed ticket. It stays out of Decisions so far, which records the route actually walked — a scope boundary isn't a step on it.

Invocation

Two modes. Either way, never resolve more than one ticket per session.

Chart the map

User invokes with a loose idea.

1. Name the destination. Run a /grilling and /domain-modeling session to pin down what this map is finding its way to — the spec, decision, or change. The destination fixes the scope, so it's settled first.
2. Map the frontier. Grill again, breadth-first this time: fan out across the whole space rather than deep on any one thread, surfacing the open decisions and the first steps takeable now. If this surfaces no fog — the way to the destination is already clear, the whole journey small enough for one session — you don't need a map. Stop and ask the user how they'd like to proceed.
3. Create the map (label wayfinder:map): Destination and Notes filled in, Decisions-so-far empty, the fog sketched into Not yet specified.
4. Create the tickets you can specify now as child issues of the map — then wire blocking edges in a second pass (issues need ids before they can reference each other). Wiring sorts them into the frontier and the blocked; everything you can't yet specify stays in the fog — the Not yet specified section.
5. Stop — charting the map is one session's work; do not also resolve tickets.

Work through the map

User invokes with a map (URL or number). A ticket is optional — without one, you pick the next decision, not the user.

1. Load the map — the low-res view, not every ticket body.
2. Choose the ticket. If the user named one, use it. Otherwise take the first frontier ticket in order. Claim it: assign it to yourself before any work.
3. Resolve it — zoom as needed: fetch the full body of any related or closed ticket on demand; invoke the skills the ## Notes block names. If in doubt, use /grilling and /domain-modeling.
4. Record the resolution: post the answer as a resolution comment, close the issue, and append a context pointer to the map's Decisions-so-far.
5. Add newly-surfaced tickets (create-then-wire); graduate any fog the answer has made specifiable, clearing each graduated patch from Not yet specified so it lives only as its new ticket. If the answer reveals a ticket — this one or another — sits beyond the destination, rule it out of scope rather than resolving it on the route. If the decision invalidates other parts of the map, update or delete those tickets.

The user may run unblocked tickets in parallel, so expect other sessions to be editing the tracker concurrently.

Wizard

A wizard is a bash script that walks a human, step by step, through a manual procedure that's tedious to do by hand and tedious to re-explain to an AI every time. It opens each URL, says exactly what to click and copy, captures the values, writes them where they belong (.env, GitHub secrets), confirms at every stage, and shows how much is left. It might configure third-party services, run a one-off migration, or move the project from one state to another.

The delightful UX is already solved by template.sh — progress with time-remaining, confirmation gates, cross-platform URL opening (including WSL), hidden secret entry, idempotent .env upserts, gh secret/gh variable writes, and a closing summary. Your job is only to scope the procedure and author its stages. The library above the STAGES marker is identical in every wizard; that consistency is the point — never hand-edit it.

A wizard is ephemeral by default — built for one run, saved to a scratch or scripts/ path, deleted when the job's done. Commit it only when the user wants a repeatable setup path that should live in the repo.

Process

1. Scope the procedure

Work out every manual step the human must take and every value that gets captured along the way. Read the repo first — don't ask cold:

• For setup: .env, .env.example, .env.*, README, docker-compose*, framework config, and .github/workflows/* (every secrets.* / vars.* reference is a value the wizard must produce).
• For a migration or transition: the current state, the target state, and the irreversible actions between them.

Then show the user the ordered list of stages and the values each produces, and confirm — they may add, drop, or reorder.

Done when: every stage is named in order, and for each captured value you know (a) where the human gets it, (b) where it's written (.env, a GitHub secret, both, or nowhere — some stages are pure actions), and (c) whether it's secret (hidden entry) or public.

2. Map each stage's journey

For each stage, write the precise path a human follows: which URL to open, what to do there, where a value is shown, which variable it fills — e.g. "Dashboard → Developers → API keys → Reveal test key → copy". Where you don't actually know the current UI or the exact command, say so and ask the user or check the docs — never invent steps that may not exist.

Done when: every stage traces to concrete instructions a stranger could follow.

3. Author the wizard

Copy template.sh to the target path. Replace the example stage with one stage per step, in dependency order. Use the library helpers — stage, say/step, open_url, ask/ask_secret, write_env, set_secret/set_var, pause/confirm — and set TOTAL_STAGES and TOTAL_MINUTES to honest estimates (this drives the time-remaining display).

Hold the bar the template sets: open the URL before asking for its value, use ask_secret for anything secret, write_env every persisted value, set_secret only the values CI actually needs, and confirm before any irreversible action. Each stage clears the screen so only the current step is visible — keep a stage to one focused task so nothing the human needs scrolls away. Don't touch the library above the marker.

4. Verify and hand off

• bash -n <script>; run shellcheck if available.
• chmod +x <script>.
• Don't run it end-to-end yourself — it opens browsers and blocks on human input. Trace it statically instead: every value from step 1 is captured and lands where step 1 said, and every set_secret name exactly matches a secrets.* reference in CI.
• Tell the user how to run it. If it's a repeatable setup path, commit it and link it from the README so the next person runs the script instead of asking an AI.

A skill exists to wrangle determinism out of a stochastic system. Predictability — the agent taking the same process every run, not producing the same output — is the root virtue; every lever below serves it.

Bold terms are defined in GLOSSARY.md; look them up there for the full meaning.

Invocation

Two choices, trading different costs:

• A model-invoked skill keeps a description, so the agent can fire it autonomously and other skills can reach it (you can still type its name too). It contributes to context load — the description sits in the window every turn. Mechanics: omit disable-model-invocation, and write a model-facing description with rich trigger phrasing ("Use when the user wants…, mentions…").
• A user-invoked skill strips the description from the agent's reach: only you, typing its name, can invoke it — and no other skill can. Zero context load, but it spends cognitive load: you are the index that must remember it exists. Mechanics: set disable-model-invocation: true; the description becomes human-facing — a one-line summary, trigger lists stripped.

Pick model-invocation only when the agent must reach the skill on its own, or another skill must. If it only ever fires by hand, make it user-invoked and pay no context load.

When user-invoked skills multiply past what you can remember, that piled-up cognitive load is cured by a router skill: one user-invoked skill that names the others and when to reach for each.

Writing the description

A model-invoked description does two jobs — state what the skill is, and list the branches that should trigger it. Every word increases context load, so a description earns even harder pruning than the body:

• Front-load the skill's leading word — the description is where it does its invocation work.
• One trigger per branch. Synonyms that rename a single branch are duplication — "build features using TDD … asks for test-first development" is one branch written twice. Collapse them; keep only genuinely distinct branches.
• Cut identity that's already in the body. Keep the description to triggers, plus any "when another skill needs…" reach clause.

Information hierarchy

A skill is built from two content types — steps and reference — that mix freely: a skill can be all steps, all reference, or both. The core decision is which to use and where each sits on the information hierarchy, a ladder ranked by how immediately the agent needs the material:

1. In-skill step — an ordered action in SKILL.md, the primary tier: what the agent does, in order. Each step ends on a completion criterion, the condition that tells the agent the work is done. Make it checkable (can the agent tell done from not-done?) and, where it matters, exhaustive ("every modified model accounted for", not "produce a change list") — a vague criterion invites premature completion.
2. In-skill reference — a definition, rule, or fact in SKILL.md, consulted on demand. Often a legitimately flat peer-set (every rule of a review on one rung) — a fine arrangement, not a smell. This skill is all reference.
3. External reference — reference pushed out of SKILL.md into a separate file, reached by a context pointer, loaded only when the pointer fires. (Spans disclosed reference — a sibling file like GLOSSARY.md, still part of the skill — through fully external reference that lives outside the skill system and any skill can point at.)

A demanding completion criterion drives thorough legwork — the digging the agent does within the work — whether the skill has steps or not, since "every rule applied" binds flat reference just as "every step done" binds a sequence.

Push too little down and the top bloats; push too much and you hide material the agent actually needs. That tension is the whole decision.

Progressive disclosure is the move down the ladder — out of SKILL.md into a linked file — so the top stays legible. Mechanics: a linked .md file in the skill folder, named for what it holds (this skill discloses its full definitions to GLOSSARY.md). Some skills are used in more than one way, and each distinct way is a branch — different runs taking different paths through the skill. Branching is the cleanest disclosure test: inline what every branch needs, and push behind a pointer what only some branches reach. A context pointer's wording, not its target, decides when and how reliably the agent reaches the material.

Where the ladder decides how far down a piece sits, co-location decides what sits beside it once there: keep a concept's definition, rules, and caveats under one heading rather than scattered, so reading one part brings its neighbours with it.

When to split

Granularity is how finely you divide skills, and each cut spends one of the two loads, so split only when the cut earns it. Two cuts:

• By invocation — split off a model-invoked skill when you have a distinct leading word that should trigger it on its own, or another skill must reach it. You pay context load for the new always-loaded description, so that independent reach has to be worth it.
• By sequence — split a run of steps when the steps still ahead (a step's post-completion steps) tempt the agent to rush the one in front of it (premature completion). Keeping them out of view encourages the agent to do more legwork on the current task.

Pruning

Keep each meaning in a single source of truth: one authoritative place, so changing the behaviour is a one-place edit.

Check every line for relevance: does it still bear on what the skill does?

Then hunt no-ops sentence by sentence, not just line by line: run the no-op test on each sentence in isolation, and when one fails, delete the whole sentence rather than trim words from it. Be aggressive — most prose that fails should go, not be rewritten.

Leading words

A leading word is a compact concept already living in the model's pretraining that the agent thinks with while running the skill (e.g. lesson, fog of war, tracer bullets). Repeated throughout the text (though not necessarily - a strong leading word might only be needed once), it accumulates a distributed definition and anchors a whole region of behaviour in the fewest tokens, by recruiting priors the model already holds.

It serves predictability twice. In the body it anchors execution: the agent reaches for the same behaviour every time the word appears. In the description it anchors invocation: when the same word lives in your prompts, docs, and code, the agent links that shared language to the skill and fires it more reliably.

Hunt for opportunities to refactor skills to use leading words. A triad spelled out at three sites (duplication), a description spending a sentence to gesture at one idea — each is a passage begging to collapse into a single token. Examples include:

• "fast, deterministic, low-overhead" -> tight — one quality restated across a phase — into a single pretrained word (a tight loop).
• "a loop you believe in" -> red — converts a fuzzy gate into a binary observable state (the loop goes red on the bug, or it doesn't).

You win twice over: fewer tokens, and a sharper hook for the agent to hang its thinking on. Assume every skill is carrying restatements that leading words retire — go find them.

Failure modes

Use these to diagnose issues the user may be having with the skill.

• Premature completion — ending a step before it's genuinely done, attention slipping to being done. Defence, in order: sharpen the completion criterion first (cheap, local); only if it is irreducibly fuzzy and you observe the rush, hide the post-completion steps by splitting (the sequence cut).
• Duplication — the same meaning in more than one place. Costs maintenance and tokens, and inflates a meaning's prominence on the ladder past its real rank.
• Sediment — stale layers that settle because adding feels safe and removing feels risky. The default fate of any skill without a pruning discipline.
• Sprawl — a skill simply too long, even when every line is live and unique. Hurts readability and maintainability and wastes tokens. The cure is the ladder: disclose reference behind pointers, and split by branch or sequence so each path carries only what it needs.
• No-op — a line the model already obeys by default, so you pay load to say nothing. The test: does it change behaviour versus the default? A weak leading word (be thorough when the agent is already thorough-ish) is a no-op; the fix is a stronger word (relentless), not a different technique.
• Negation — steering by prohibition backfires: don't think of an elephant names the elephant and makes it more available, not less. Prompt the positive — state the target behaviour so the banned one is never spoken; keep a prohibition only as a hard guardrail you can't phrase positively, and even then pair it with what to do instead.

Interactivity Best Practices

Use these guidelines when creating or reviewing Remotion animations, especially if the animation should be editable in Remotion Studio Visual Mode.

Prefer interpolate() over standalone spring()

Prefer interpolate() for most animation values.

• It is easier for humans and agents to reason about.
• Bezier easings are familiar from CSS and can express snappy or jumpy motion.
• Studio Visual Mode can edit interpolate() keyframes.

When an animation should feel like a spring, keep the editable value in interpolate() and pass Easing.spring() as the easing function.

Prefer:

style={{
  scale: interpolate(frame, [0, 30], [0, 1], {
    easing: Easing.spring({
      damping: 200,
    }),
  }),
}}

❌ Avoid using spring() as a separate driver when the same motion can be expressed as interpolate() easing:

const scale = spring({
  frame,
  fps,
  config: {
    damping: 200,
  },
});

style={{
  scale,
}}

Easing.spring() supports damping, mass, stiffness, and overshootClamping. It is normalized to the interpolation progress, so it does not take frame, fps, from, to, delay, reverse, durationInFrames, or durationRestThreshold.

Use standalone spring() only when the animation specifically needs a frame-driven physical spring simulation that cannot be represented as an easing on interpolate().

Prefer individual CSS transform properties

Use individual CSS transform properties such as scale, rotate, and translate instead of composing a transform string.

Prefer:

style={{
  scale: interpolate(frame, [0, 100], [0, 2]),
}}

❌ Avoid:

style={{
  transform: `scale(${interpolate(frame, [0, 100], [0, 2])})`,
}}

Individual transform properties are editable in Studio Visual Mode. Values hidden inside a transform string are not.

Keep editable values inline

Put the interpolation directly in the JSX style object when the value should be visually editable.

Prefer:

style={{
  scale: interpolate(frame, [0, 100], [0, 2]),
}}

❌ Avoid:

const scale = interpolate(frame, [0, 100], [0, 2]);

style={{
  scale,
}}

Inline computations and literal values let Studio Visual Mode discover and edit the keyframes and props.

This also applies to effect parameters. Keep editable effect values inline in the effect call.

Prefer:

effects={[
  radialProgressiveBlur({
    center: [0.5, 0.5],
    point1: [0.86, 0.36],
    point2: [0.68, 0.84],
  }),
]}

❌ Avoid hiding editable values behind constants:

const center = [0.5, 0.5] as const;

effects={[
  radialProgressiveBlur({
    center,
  }),
]}

Keep effects unconditional

When using the effects prop, keep the effect array shape unconditional. Studio Visual Mode cannot reliably edit an effect that only exists behind a conditional expression.

Prefer rendering separate elements when one version should have effects and another should not:

<CanvasImage src={src} width={1280} height={720} />
<CanvasImage
  src={src}
  width={1280}
  height={720}
  effects={[
    blur({
      radius: interpolate(frame, [0, 100], [0, 40]),
    }),
  ]}
/>

❌ Avoid conditionally including an effect:

<CanvasImage
  src={src}
  width={1280}
  height={720}
  effects={enabled ? [blur({radius: 40})] : []}
/>

Keep editable effect parameters inline inside the unconditional effect call. Do not pass variables for editable values such as coordinates, colors, numeric controls, or interpolations.

Interpolate rotation and translation directly

Interpolate CSS unit strings directly for rotate and translate.

Prefer:

style={{
  rotate: interpolate(frame, [0, 100], ["20deg", "90deg"]),
  translate: interpolate(frame, [0, 100], ["0px 0px", "100px 100px"]),
}}

❌ Avoid manually building strings around separately interpolated numbers when the property can be interpolated directly.

Direct interpolation of these properties is supported and works better with visual editing in Studio.

Use Interactive.* for tweakable elements

Use the Interactive namespace from remotion when an element is likely to be tweaked in Studio.

import {Interactive} from "remotion";

For example:

<Interactive.Div
  style={{
    color: "red",
    fontSize: 80,
  }}
>
  Hello
</Interactive.Div>

<Interactive.Div>, <Interactive.Span>, and the other Interactive.* components behave like their regular HTML equivalents, but enable interactivity in Studio.

Inline styles on these elements, such as text color and font size, can be standardized interactively.

Ask Matt

You don't remember every skill, so ask.

A flow is a path through the skills. Most paths run along one main flow, and two on-ramps merge onto it. Everything else is standalone, or a vocabulary layer that runs underneath.

The main flow: idea → ship

The route most work travels. You have an idea and want it built.

1. /grill-with-docs — sharpen the idea by interview. Start here when you have a codebase: it's stateful, retaining what it learns in CONTEXT.md and ADRs. (No codebase? Use /grill-me — see Standalone. Both run the same /grilling primitive; grill-with-docs is the one that leaves a paper trail.)

2. Branch — can you settle every question in conversation? If a question needs a runnable answer (state, business logic, a UI you have to see), detour through a prototype, bridged by /handoff in both directions (see Crossing sessions):

   • /handoff out, then open a fresh session against that file,
   • /prototype to answer the question with throwaway code,
   • /handoff back what you learned, and reference it from the original idea thread.

3. Branch — is this a multi-session build?

   • Yes → /to-spec (turn the thread into a spec), then /to-tickets to split it into tracer-bullet tickets, each declaring its blocking edges. On a local tracker that's an ordered tickets.md you work by hand; on a real tracker the edges become native blocking links, so any ticket whose blockers are done can be grabbed — kick off /implement per ticket, clearing context between each one.
   • No → /implement right here, in the same context window.

   Either way, /implement builds each issue by driving /tdd internally — one red-green slice at a time — then closes out by running /code-review, a two-axis review (Standards + Spec) of the diff, before committing. Reach for /tdd on its own when you just want to build a concrete behaviour test-first without a full spec, and /code-review on its own whenever you want to review a branch or PR against a fixed point.

Context hygiene

Keep steps 1–3 in one unbroken context window — don't compact or clear until after /to-tickets — so the grilling, spec, and tickets all build on the same thinking. Each /implement then starts fresh, working from the ticket.

The limit on this is the smart zone: the window (~120k tokens on state-of-the-art models) within which the model still reasons sharply. If a session approaches it before /to-tickets, don't push on degraded — /handoff and continue in a fresh thread.

On-ramps

A starting situation that generates work, then merges onto the main flow.

• Bugs and requests piling up → /triage. It moves issues through triage roles and produces agent-ready issues, which /implement later picks up.

  Triage is only for issues you didn't create — bug reports, incoming feature requests, anything that arrives raw. Tickets that /to-tickets produced are already agent-ready, so don't triage them.

• Something's broken → /diagnosing-bugs. For the hard ones: the bug that resists a first glance, the intermittent flake, the regression that crept in between two known-good states. It refuses to theorise until it has a tight feedback loop — one command that already goes red on this bug — then fixes with a regression test. Its post-mortem hands off to /improve-codebase-architecture when the real finding is that there's no good seam to lock the bug down.

• A huge, foggy effort — a greenfield project or a huge feature build, too big for one session → /wayfinder. When the way from here to the destination isn't visible yet, it charts a shared map of investigation tickets on the issue tracker and resolves them one at a time — producing decisions, not deliverables — until the fog is pushed back and the way is clear. Then it merges onto the main flow at /to-spec (or, if the effort turned out small enough, straight to /implement). Where /grill-with-docs sharpens an idea you can hold in one session, wayfinder is for the idea you can't.

Codebase health

Not feature work — upkeep.

• /improve-codebase-architecture — run whenever you have a spare moment to keep the codebase good for agents to operate in. It surfaces deepening opportunities; picking one generates an idea you can take into the main flow at /grill-with-docs. It's the survey that finds the candidates; /codebase-design (below) is the bench you design the chosen one on.

Vocabulary underneath

Two model-invoked references that run beneath the other skills — each the single source of truth for its vocabulary. Reach for them directly when the words, not the process, are the problem; or let the skills above pull them in.

• /domain-modeling — sharpen the project's domain language: challenge a fuzzy term, resolve an overloaded word ("account" doing three jobs), record a hard-to-reverse decision as an ADR. It's the active discipline /grill-with-docs drives to keep CONTEXT.md a clean glossary.
• /codebase-design — the deep-module vocabulary (module, interface, depth, seam, adapter, leverage, locality) for designing a module's shape: a lot of behaviour behind a small interface at a clean seam. /tdd and /improve-codebase-architecture both speak it.

Crossing sessions

• /handoff — when a thread is full or you need to branch off (e.g. into a /prototype session), this compacts the conversation into a markdown file. You don't continue in place — you open a new session and reference that file to carry the context across. It's the bridge between context windows, in either direction. Use it when you want a fresh session but need the current conversation preserved.
• /compact (built-in) — stay in the same conversation, letting the earlier turns be summarized. Use it at intentional breaks between phases, when you don't mind losing the verbatim history. Don't compact mid-phase — the agent can lose its way. /handoff forks; /compact continues.

Standalone

Off the main flow entirely.

• /grill-me — the same relentless interview as /grill-with-docs, but for when you have no codebase. Stateless: it saves nothing locally, builds no CONTEXT.md. Reach for it to sharpen any plan or design that doesn't live in a repo.
• /prototype — a small, throwaway program that answers one design question: does this state model feel right, or what should this UI look like. Throwaway from day one — keep the answer, delete the code. It's the detour in step 2 of the main flow, but reach for it any time a design question is hard to settle on paper.
• /research — delegate reading legwork to a background agent: it investigates a question against primary sources, then leaves a cited Markdown file in the repo. Keep working while it reads. The file it produces is something to take into the main flow at /grill-with-docs — research feeds the thinking, it doesn't replace it.
• /teach — learn a concept over multiple sessions, using the current directory as a stateful workspace.
• /writing-great-skills — reference for writing and editing skills well.

Precondition

/setup-matt-pocock-skills — run before your first engineering flow to configure the issue tracker, triage labels, and doc layout the other skills assume. Custom issue trackers also work.

Write a handoff summary of the current conversation so a fresh agent can continue the work. Instead of saving it, launch a background agent seeded with the summary as its prompt: claude --bg --name "<descriptive name>" "<handoff summary>". It starts in the current working directory and returns immediately; the user manages it with claude agents.

Always pass -n/--name with a descriptive name (e.g. --name "Fix login bug") — it sets the display name shown in the job list, session picker, and terminal title.

Include a "suggested skills" section in the summary, which suggests skills that the agent should invoke.

Do not duplicate content already captured in other artifacts (PRDs, plans, ADRs, issues, commits, diffs). Reference them by path or URL instead.

Redact any sensitive information, such as API keys, passwords, or personally identifiable information — the summary becomes the agent's prompt.

If the user passed arguments, treat them as a description of what the next session will focus on and tailor the summary accordingly.

Two-axis review of the diff between HEAD and a fixed point the user supplies:

• Standards — does the code conform to this repo's documented coding standards?
• Spec — does the code faithfully implement the originating issue / PRD / spec?

Both axes run as parallel sub-agents so they don't pollute each other's context, then this skill aggregates their findings.

The issue tracker should have been provided to you — run /setup-matt-pocock-skills if docs/agents/issue-tracker.md is missing.

Process

1. Pin the fixed point

Whatever the user said is the fixed point — a commit SHA, branch name, tag, main, HEAD~5, etc. If they didn't specify one, ask for it.

Capture the diff command once: git diff <fixed-point>...HEAD (three-dot, so the comparison is against the merge-base). Also note the list of commits via git log <fixed-point>..HEAD --oneline.

Before going further, confirm the fixed point resolves (git rev-parse <fixed-point>) and the diff is non-empty. A bad ref or empty diff should fail here — not inside two parallel sub-agents.

2. Identify the spec source

Look for the originating spec, in this order:

1. Issue references in the commit messages (#123, Closes #45, GitLab !67, etc.) — fetch via the workflow in docs/agents/issue-tracker.md.
2. A path the user passed as an argument.
3. A PRD/spec file under docs/, specs/, or .scratch/ matching the branch name or feature.
4. If nothing is found, ask the user where the spec is. If they say there isn't one, the Spec sub-agent will skip and report "no spec available".

3. Identify the standards sources

Anything in the repo that documents how code should be written, such as CODING_STANDARDS.md or CONTRIBUTING.md.

On top of whatever the repo documents, the Standards axis always carries the smell baseline below — a fixed set of Fowler code smells (Refactoring, ch.3) that applies even when a repo documents nothing. Two rules bind it:

• The repo overrides. A documented repo standard always wins; where it endorses something the baseline would flag, suppress the smell.
• Always a judgement call. Each smell is a labelled heuristic ("possible Feature Envy"), never a hard violation — and, like any standard here, skip anything tooling already enforces.

Each smell reads what it is → how to fix; match it against the diff:

• Mysterious Name — a function, variable, or type whose name doesn't reveal what it does or holds. → rename it; if no honest name comes, the design's murky.
• Duplicated Code — the same logic shape appears in more than one hunk or file in the change. → extract the shared shape, call it from both.
• Feature Envy — a method that reaches into another object's data more than its own. → move the method onto the data it envies.
• Data Clumps — the same few fields or params keep travelling together (a type wanting to be born). → bundle them into one type, pass that.
• Primitive Obsession — a primitive or string standing in for a domain concept that deserves its own type. → give the concept its own small type.
• Repeated Switches — the same switch/if-cascade on the same type recurs across the change. → replace with polymorphism, or one map both sites share.
• Shotgun Surgery — one logical change forces scattered edits across many files in the diff. → gather what changes together into one module.
• Divergent Change — one file or module is edited for several unrelated reasons. → split so each module changes for one reason.
• Speculative Generality — abstraction, parameters, or hooks added for needs the spec doesn't have. → delete it; inline back until a real need shows.
• Message Chains — long a.b().c().d() navigation the caller shouldn't depend on. → hide the walk behind one method on the first object.
• Middle Man — a class or function that mostly just delegates onward. → cut it, call the real target direct.
• Refused Bequest — a subclass or implementer that ignores or overrides most of what it inherits. → drop the inheritance, use composition.

4. Spawn both sub-agents in parallel

Send a single message with two Agent tool calls. Use the general-purpose subagent for both.

Standards sub-agent prompt — include:

• The full diff command and commit list.
• The list of standards-source files you found in step 3, plus the smell baseline from step 3 pasted in full — the sub-agent has no other access to it.
• The brief: "Report — per file/hunk where relevant — (a) every place the diff violates a documented standard: cite the standard (file + the rule); and (b) any baseline smell you spot: name it and quote the hunk. Distinguish hard violations from judgement calls — documented-standard breaches can be hard, but baseline smells are always judgement calls, and a documented repo standard overrides the baseline. Skip anything tooling enforces. Under 400 words."

Spec sub-agent prompt — include:

• The diff command and commit list.
• The path or fetched contents of the spec.
• The brief: "Report: (a) requirements the spec asked for that are missing or partial; (b) behaviour in the diff that wasn't asked for (scope creep); (c) requirements that look implemented but where the implementation looks wrong. Quote the spec line for each finding. Under 400 words."

If the spec is missing, skip the Spec sub-agent and note this in the final report.

5. Aggregate

Present the two reports under ## Standards and ## Spec headings, verbatim or lightly cleaned. Do not merge or rerank findings — the two axes are deliberately separate (see Why two axes).

End with a one-line summary: total findings per axis, and the worst issue within each axis (if any). Don't pick a single winner across axes — that's the reranking the separation exists to prevent.

Why two axes

A change can pass one axis and fail the other:

• Code that follows every standard but implements the wrong thing → Standards pass, Spec fail.
• Code that does exactly what the issue asked but breaks the project's conventions → Spec pass, Standards fail.

Reporting them separately stops one axis from masking the other.

Codebase Design

Design deep modules: a lot of behaviour behind a small interface, placed at a clean seam, testable through that interface. Use this language and these principles wherever code is being designed or restructured. The aim is leverage for callers, locality for maintainers, and testability for everyone.

Glossary

Use these terms exactly — don't substitute "component," "service," "API," or "boundary." Consistent language is the whole point.

Module — anything with an interface and an implementation. Deliberately scale-agnostic: a function, class, package, or tier-spanning slice. Avoid: unit, component, service.

Interface — everything a caller must know to use the module correctly: the type signature, but also invariants, ordering constraints, error modes, required configuration, and performance characteristics. Avoid: API, signature (too narrow — they refer only to the type-level surface).

Implementation — what's inside a module, its body of code. Distinct from Adapter: a thing can be a small adapter with a large implementation (a Postgres repo) or a large adapter with a small implementation (an in-memory fake). Reach for "adapter" when the seam is the topic; "implementation" otherwise.

Depth — leverage at the interface: the amount of behaviour a caller (or test) can exercise per unit of interface they have to learn. A module is deep when a large amount of behaviour sits behind a small interface, shallow when the interface is nearly as complex as the implementation.

Seam (Michael Feathers) — a place where you can alter behaviour without editing in that place; the location at which a module's interface lives. Where to put the seam is its own design decision, distinct from what goes behind it. Avoid: boundary (overloaded with DDD's bounded context).

Adapter — a concrete thing that satisfies an interface at a seam. Describes role (what slot it fills), not substance (what's inside).

Leverage — what callers get from depth: more capability per unit of interface they learn. One implementation pays back across N call sites and M tests.

Locality — what maintainers get from depth: change, bugs, knowledge, and verification concentrate in one place rather than spreading across callers. Fix once, fixed everywhere.

Deep vs shallow

Deep module = small interface + lots of implementation:

┌─────────────────────┐
│   Small Interface   │  ← Few methods, simple params
├─────────────────────┤
│                     │
│  Deep Implementation│  ← Complex logic hidden
│                     │
└─────────────────────┘

Shallow module = large interface + little implementation (avoid):

┌─────────────────────────────────┐
│       Large Interface           │  ← Many methods, complex params
├─────────────────────────────────┤
│  Thin Implementation            │  ← Just passes through
└─────────────────────────────────┘

When designing an interface, ask:

• Can I reduce the number of methods?
• Can I simplify the parameters?
• Can I hide more complexity inside?

Principles

• Depth is a property of the interface, not the implementation. A deep module can be internally composed of small, mockable, swappable parts — they just aren't part of the interface. A module can have internal seams (private to its implementation, used by its own tests) as well as the external seam at its interface.
• The deletion test. Imagine deleting the module. If complexity vanishes, it was a pass-through. If complexity reappears across N callers, it was earning its keep.
• The interface is the test surface. Callers and tests cross the same seam. If you want to test past the interface, the module is probably the wrong shape.
• One adapter means a hypothetical seam. Two adapters means a real one. Don't introduce a seam unless something actually varies across it.

Designing for testability

Good interfaces make testing natural:

1. Accept dependencies, don't create them.

   // Testable
   function processOrder(order, paymentGateway) {}
   
   // Hard to test
   function processOrder(order) {
     const gateway = new StripeGateway();
   }
   

2. Return results, don't produce side effects.

   // Testable
   function calculateDiscount(cart): Discount {}
   
   // Hard to test
   function applyDiscount(cart): void {
     cart.total -= discount;
   }
   

3. Small surface area. Fewer methods = fewer tests needed. Fewer params = simpler test setup.

Relationships

• A Module has exactly one Interface (the surface it presents to callers and tests).
• Depth is a property of a Module, measured against its Interface.
• A Seam is where a Module's Interface lives.
• An Adapter sits at a Seam and satisfies the Interface.
• Depth produces Leverage for callers and Locality for maintainers.

Rejected framings

• Depth as ratio of implementation-lines to interface-lines (Ousterhout): rewards padding the implementation. We use depth-as-leverage instead.
• "Interface" as the TypeScript interface keyword or a class's public methods: too narrow — interface here includes every fact a caller must know.
• "Boundary": overloaded with DDD's bounded context. Say seam or interface.

Going deeper

• Deepening a cluster given its dependencies — see DEEPENING.md: dependency categories, seam discipline, and replace-don't-layer testing.
• Exploring alternative interfaces — see DESIGN-IT-TWICE.md: spin up parallel sub-agents to design the interface several radically different ways, then compare on depth, locality, and seam placement.

Design an Interface

Based on "Design It Twice" from "A Philosophy of Software Design": your first idea is unlikely to be the best. Generate multiple radically different designs, then compare.

Workflow

1. Gather Requirements

Before designing, understand:

• What problem does this module solve?
• Who are the callers? (other modules, external users, tests)
• What are the key operations?
• Any constraints? (performance, compatibility, existing patterns)
• What should be hidden inside vs exposed?

Ask: "What does this module need to do? Who will use it?"

2. Generate Designs (Parallel Sub-Agents)

Spawn 3+ sub-agents simultaneously using Task tool. Each must produce a radically different approach.

Prompt template for each sub-agent:

Design an interface for: [module description]

Requirements: [gathered requirements]

Constraints for this design: [assign a different constraint to each agent]
- Agent 1: "Minimize method count - aim for 1-3 methods max"
- Agent 2: "Maximize flexibility - support many use cases"
- Agent 3: "Optimize for the most common case"
- Agent 4: "Take inspiration from [specific paradigm/library]"

Output format:
1. Interface signature (types/methods)
2. Usage example (how caller uses it)
3. What this design hides internally
4. Trade-offs of this approach

3. Present Designs

Show each design with:

1. Interface signature - types, methods, params
2. Usage examples - how callers actually use it in practice
3. What it hides - complexity kept internal

Present designs sequentially so user can absorb each approach before comparison.

4. Compare Designs

After showing all designs, compare them on:

• Interface simplicity: fewer methods, simpler params
• General-purpose vs specialized: flexibility vs focus
• Implementation efficiency: does shape allow efficient internals?
• Depth: small interface hiding significant complexity (good) vs large interface with thin implementation (bad)
• Ease of correct use vs ease of misuse

Discuss trade-offs in prose, not tables. Highlight where designs diverge most.

5. Synthesize

Often the best design combines insights from multiple options. Ask:

• "Which design best fits your primary use case?"
• "Any elements from other designs worth incorporating?"

Evaluation Criteria

From "A Philosophy of Software Design":

Interface simplicity: Fewer methods, simpler params = easier to learn and use correctly.

General-purpose: Can handle future use cases without changes. But beware over-generalization.

Implementation efficiency: Does interface shape allow efficient implementation? Or force awkward internals?

Depth: Small interface hiding significant complexity = deep module (good). Large interface with thin implementation = shallow module (avoid).

Anti-Patterns

• Don't let sub-agents produce similar designs - enforce radical difference
• Don't skip comparison - the value is in contrast
• Don't implement - this is purely about interface shape
• Don't evaluate based on implementation effort

Diagnosing Bugs

A discipline for hard bugs. Skip phases only when explicitly justified.

When exploring the codebase, read CONTEXT.md (if it exists) to get a clear mental model of the relevant modules, and check ADRs in the area you're touching.

Phase 1 — Build a feedback loop

This is the skill. Everything else is mechanical. If you have a tight pass/fail signal for the bug — one that goes red on this bug — you will find the cause; bisection, hypothesis-testing, and instrumentation all just consume it. If you don't have one, no amount of staring at code will save you.

Spend disproportionate effort here. Be aggressive. Be creative. Refuse to give up.

Ways to construct one — try them in roughly this order

1. Failing test at whatever seam reaches the bug — unit, integration, e2e.
2. Curl / HTTP script against a running dev server.
3. CLI invocation with a fixture input, diffing stdout against a known-good snapshot.
4. Headless browser script (Playwright / Puppeteer) — drives the UI, asserts on DOM/console/network.
5. Replay a captured trace. Save a real network request / payload / event log to disk; replay it through the code path in isolation.
6. Throwaway harness. Spin up a minimal subset of the system (one service, mocked deps) that exercises the bug code path with a single function call.
7. Property / fuzz loop. If the bug is "sometimes wrong output", run 1000 random inputs and look for the failure mode.
8. Bisection harness. If the bug appeared between two known states (commit, dataset, version), automate "boot at state X, check, repeat" so you can git bisect run it.
9. Differential loop. Run the same input through old-version vs new-version (or two configs) and diff outputs.
10. HITL bash script. Last resort. If a human must click, drive them with scripts/hitl-loop.template.sh so the loop is still structured. Captured output feeds back to you.

Build the right feedback loop, and the bug is 90% fixed.

Tighten the loop

Treat the loop as a product. Once you have a loop, tighten it:

• Can I make it faster? (Cache setup, skip unrelated init, narrow the test scope.)
• Can I make the signal sharper? (Assert on the specific symptom, not "didn't crash".)
• Can I make it more deterministic? (Pin time, seed RNG, isolate filesystem, freeze network.)

A 30-second flaky loop is barely better than no loop; a 2-second deterministic one is tight — a debugging superpower.

Non-deterministic bugs

The goal is not a clean repro but a higher reproduction rate. Loop the trigger 100×, parallelise, add stress, narrow timing windows, inject sleeps. A 50%-flake bug is debuggable; 1% is not — keep raising the rate until it's debuggable.

When you genuinely cannot build a loop

Stop and say so explicitly. List what you tried. Ask the user for: (a) access to whatever environment reproduces it, (b) a captured artifact (HAR file, log dump, core dump, screen recording with timestamps), or (c) permission to add temporary production instrumentation. Do not proceed to hypothesise without a loop.

Completion criterion — a tight loop that goes red

Phase 1 is done when the loop is tight and red-capable: you can name one command — a script path, a test invocation, a curl — that you have already run at least once (paste the invocation and its output), and that is:

• Red-capable — it drives the actual bug code path and asserts the user's exact symptom, so it can go red on this bug and green once fixed. Not "runs without erroring" — it must be able to catch this specific bug.
• Deterministic — same verdict every run (flaky bugs: a pinned, high reproduction rate, per above).
• Fast — seconds, not minutes.
• Agent-runnable — you can run it unattended; a human in the loop only via scripts/hitl-loop.template.sh.

If you catch yourself reading code to build a theory before this command exists, stop — jumping straight to a hypothesis is the exact failure this skill prevents. No red-capable command, no Phase 2.

Phase 2 — Reproduce + minimise

Run the loop. Watch it go red — the bug appears.

Confirm:

• The loop produces the failure mode the user described — not a different failure that happens to be nearby. Wrong bug = wrong fix.
• The failure is reproducible across multiple runs (or, for non-deterministic bugs, reproducible at a high enough rate to debug against).
• You have captured the exact symptom (error message, wrong output, slow timing) so later phases can verify the fix actually addresses it.

Minimise

Once it's red, shrink the repro to the smallest scenario that still goes red. Cut inputs, callers, config, data, and steps one at a time, re-running the loop after each cut — keep only what's load-bearing for the failure.

Why bother: a minimal repro shrinks the hypothesis space in Phase 3 (fewer moving parts left to suspect) and becomes the clean regression test in Phase 5.

Done when every remaining element is load-bearing — removing any one of them makes the loop go green.

Do not proceed until you have reproduced and minimised.

Phase 3 — Hypothesise

Generate 3–5 ranked hypotheses before testing any of them. Single-hypothesis generation anchors on the first plausible idea.

Each hypothesis must be falsifiable: state the prediction it makes.

Format: "If <X> is the cause, then <changing Y> will make the bug disappear / <changing Z> will make it worse."

If you cannot state the prediction, the hypothesis is a vibe — discard or sharpen it.

Show the ranked list to the user before testing. They often have domain knowledge that re-ranks instantly ("we just deployed a change to #3"), or know hypotheses they've already ruled out. Cheap checkpoint, big time saver. Don't block on it — proceed with your ranking if the user is AFK.

Phase 4 — Instrument

Each probe must map to a specific prediction from Phase 3. Change one variable at a time.

Tool preference:

1. Debugger / REPL inspection if the env supports it. One breakpoint beats ten logs.
2. Targeted logs at the boundaries that distinguish hypotheses.
3. Never "log everything and grep".

Tag every debug log with a unique prefix, e.g. [DEBUG-a4f2]. Cleanup at the end becomes a single grep. Untagged logs survive; tagged logs die.

Perf branch. For performance regressions, logs are usually wrong. Instead: establish a baseline measurement (timing harness, performance.now(), profiler, query plan), then bisect. Measure first, fix second.

Phase 5 — Fix + regression test

Write the regression test before the fix — but only if there is a correct seam for it.

A correct seam is one where the test exercises the real bug pattern as it occurs at the call site. If the only available seam is too shallow (single-caller test when the bug needs multiple callers, unit test that can't replicate the chain that triggered the bug), a regression test there gives false confidence.

If no correct seam exists, that itself is the finding. Note it. The codebase architecture is preventing the bug from being locked down. Flag this for the next phase.

If a correct seam exists:

1. Turn the minimised repro into a failing test at that seam.
2. Watch it fail.
3. Apply the fix.
4. Watch it pass.
5. Re-run the Phase 1 feedback loop against the original (un-minimised) scenario.

Phase 6 — Cleanup + post-mortem

Required before declaring done:

• Original repro no longer reproduces (re-run the Phase 1 loop)
• Regression test passes (or absence of seam is documented)
• All [DEBUG-...] instrumentation removed (grep the prefix)
• Throwaway prototypes deleted (or moved to a clearly-marked debug location)
• The hypothesis that turned out correct is stated in the commit / PR message — so the next debugger learns

Then ask: what would have prevented this bug? If the answer involves architectural change (no good test seam, tangled callers, hidden coupling) hand off to the /improve-codebase-architecture skill with the specifics. Make the recommendation after the fix is in, not before — you have more information now than when you started.

Domain Modeling

Actively build and sharpen the project's domain model as you design. This is the active discipline — challenging terms, inventing edge-case scenarios, and writing the glossary and decisions down the moment they crystallise. (Merely reading CONTEXT.md for vocabulary is not this skill — that's a one-line habit any skill can do. This skill is for when you're changing the model, not just consuming it.)

File structure

Most repos have a single context:

/
├── CONTEXT.md
├── docs/
│   └── adr/
│       ├── 0001-event-sourced-orders.md
│       └── 0002-postgres-for-write-model.md
└── src/

If a CONTEXT-MAP.md exists at the root, the repo has multiple contexts. The map points to where each one lives:

/
├── CONTEXT-MAP.md
├── docs/
│   └── adr/                          ← system-wide decisions
├── src/
│   ├── ordering/
│   │   ├── CONTEXT.md
│   │   └── docs/adr/                 ← context-specific decisions
│   └── billing/
│       ├── CONTEXT.md
│       └── docs/adr/

Create files lazily — only when you have something to write. If no CONTEXT.md exists, create one when the first term is resolved. If no docs/adr/ exists, create it when the first ADR is needed.

During the session

Challenge against the glossary

When the user uses a term that conflicts with the existing language in CONTEXT.md, call it out immediately. "Your glossary defines 'cancellation' as X, but you seem to mean Y — which is it?"

Sharpen fuzzy language

When the user uses vague or overloaded terms, propose a precise canonical term. "You're saying 'account' — do you mean the Customer or the User? Those are different things."

Discuss concrete scenarios

When domain relationships are being discussed, stress-test them with specific scenarios. Invent scenarios that probe edge cases and force the user to be precise about the boundaries between concepts.

Cross-reference with code

When the user states how something works, check whether the code agrees. If you find a contradiction, surface it: "Your code cancels entire Orders, but you just said partial cancellation is possible — which is right?"

Update CONTEXT.md inline

When a term is resolved, update CONTEXT.md right there. Don't batch these up — capture them as they happen. Use the format in CONTEXT-FORMAT.md.

CONTEXT.md should be totally devoid of implementation details. Do not treat CONTEXT.md as a spec, a scratch pad, or a repository for implementation decisions. It is a glossary and nothing else.

Offer ADRs sparingly

Only offer to create an ADR when all three are true:

1. Hard to reverse — the cost of changing your mind later is meaningful
2. Surprising without context — a future reader will wonder "why did they do it this way?"
3. The result of a real trade-off — there were genuine alternatives and you picked one for specific reasons

If any of the three is missing, skip the ADR. Use the format in ADR-FORMAT.md.

1. First, divide the article into sections based on its headings. Think about the main points you want to make during those sections.

Consider that information is a directed acyclic graph, and that pieces of information can depend on other pieces of information. Make sure that the order of the sections and their contents respects these dependencies.

Confirm the sections with the user.

2. For each section:

2a. Rewrite the section to improve clarity, coherence, and flow. Use maximum 240 characters per paragraph.

Setup Git Guardrails

Sets up a PreToolUse hook that intercepts and blocks dangerous git commands before Claude executes them.

What Gets Blocked

• git push (all variants including --force)
• git reset --hard
• git clean -f / git clean -fd
• git branch -D
• git checkout . / git restore .

When blocked, Claude sees a message telling it that it does not have authority to access these commands.

Steps

1. Ask scope

Ask the user: install for this project only (.claude/settings.json) or all projects (~/.claude/settings.json)?

2. Copy the hook script

The bundled script is at: scripts/block-dangerous-git.sh

Copy it to the target location based on scope:

• Project: .claude/hooks/block-dangerous-git.sh
• Global: ~/.claude/hooks/block-dangerous-git.sh

Make it executable with chmod +x.

3. Add hook to settings

Add to the appropriate settings file:

Project (.claude/settings.json):

{
  "hooks": {
    "PreToolUse": [
      {
        "matcher": "Bash",
        "hooks": [
          {
            "type": "command",
            "command": "\"$CLAUDE_PROJECT_DIR\"/.claude/hooks/block-dangerous-git.sh"
          }
        ]
      }
    ]
  }
}

Global (~/.claude/settings.json):

{
  "hooks": {
    "PreToolUse": [
      {
        "matcher": "Bash",
        "hooks": [
          {
            "type": "command",
            "command": "~/.claude/hooks/block-dangerous-git.sh"
          }
        ]
      }
    ]
  }
}

If the settings file already exists, merge the hook into existing hooks.PreToolUse array — don't overwrite other settings.

4. Ask about customization

Ask if user wants to add or remove any patterns from the blocked list. Edit the copied script accordingly.

5. Verify

Run a quick test:

echo '{"tool_input":{"command":"git push origin main"}}' | <path-to-script>

Should exit with code 2 and print a BLOCKED message to stderr.

Interview me relentlessly about every aspect of this plan until we reach a shared understanding. Walk down each branch of the design tree, resolving dependencies between decisions one-by-one. For each question, provide your recommended answer.

Ask the questions one at a time, waiting for feedback on each question before continuing. Asking multiple questions at once is bewildering.

If a fact can be found by exploring the codebase, look it up rather than asking me. The decisions, though, are mine — put each one to me and wait for my answer.

Do not enact the plan until I confirm we have reached a shared understanding.

Write a handoff document summarising the current conversation so a fresh agent can continue the work. Save to the temporary directory of the user's OS - not the current workspace.

Include a "suggested skills" section in the document, which suggests skills that the agent should invoke.

Do not duplicate content already captured in other artifacts (specs, plans, ADRs, issues, commits, diffs). Reference them by path or URL instead.

Redact any sensitive information, such as API keys, passwords, or personally identifiable information.

If the user passed arguments, treat them as a description of what the next session will focus on and tailor the doc accordingly.

Implement the work described by the user in the spec or tickets.

Use /tdd where possible, at pre-agreed seams.

Run typechecking regularly, single test files regularly, and the full test suite once at the end.

Once done, use /code-review to review the work.

Commit your work to the current branch.

Improve Codebase Architecture

Surface architectural friction and propose deepening opportunities — refactors that turn shallow modules into deep ones. The aim is testability and AI-navigability.

This command is informed by the project's domain model and built on a shared design vocabulary:

• Run the /codebase-design skill for the architecture vocabulary (module, interface, depth, seam, adapter, leverage, locality) and its principles (the deletion test, "the interface is the test surface", "one adapter = hypothetical seam, two = real"). Use these terms exactly in every suggestion — don't drift into "component," "service," "API," or "boundary."
• The domain language in CONTEXT.md gives names to good seams; ADRs in docs/adr/ record decisions this command should not re-litigate.

Process

1. Explore

Read the project's domain glossary (CONTEXT.md) and any ADRs in the area you're touching first.

Then use the Agent tool with subagent_type=Explore to walk the codebase. Don't follow rigid heuristics — explore organically and note where you experience friction:

• Where does understanding one concept require bouncing between many small modules?
• Where are modules shallow — interface nearly as complex as the implementation?
• Where have pure functions been extracted just for testability, but the real bugs hide in how they're called (no locality)?
• Where do tightly-coupled modules leak across their seams?
• Which parts of the codebase are untested, or hard to test through their current interface?

Apply the deletion test to anything you suspect is shallow: would deleting it concentrate complexity, or just move it? A "yes, concentrates" is the signal you want.

2. Present candidates as an HTML report

Write a self-contained HTML file to the OS temp directory so nothing lands in the repo. Resolve the temp dir from $TMPDIR, falling back to /tmp (or %TEMP% on Windows), and write to <tmpdir>/architecture-review-<timestamp>.html so each run gets a fresh file. Open it for the user — xdg-open <path> on Linux, open <path> on macOS, start <path> on Windows — and tell them the absolute path.

The report uses Tailwind via CDN for layout and styling, and Mermaid via CDN for diagrams where a graph/flow/sequence reliably communicates the structure. Mix Mermaid with hand-crafted CSS/SVG visuals — use Mermaid when relationships are graph-shaped (call graphs, dependencies, sequences), and hand-built divs/SVG when you want something more editorial (mass diagrams, cross-sections, collapse animations). Each candidate gets a before/after visualisation. Be visual.

For each candidate, render a card with:

• Files — which files/modules are involved
• Problem — why the current architecture is causing friction
• Solution — plain English description of what would change
• Benefits — explained in terms of locality and leverage, and how tests would improve
• Before / After diagram — side-by-side, custom-drawn, illustrating the shallowness and the deepening
• Recommendation strength — one of Strong, Worth exploring, Speculative, rendered as a badge

End the report with a Top recommendation section: which candidate you'd tackle first and why.

Use CONTEXT.md vocabulary for the domain, and the /codebase-design vocabulary for the architecture. If CONTEXT.md defines "Order," talk about "the Order intake module" — not "the FooBarHandler," and not "the Order service."

ADR conflicts: if a candidate contradicts an existing ADR, only surface it when the friction is real enough to warrant revisiting the ADR. Mark it clearly in the card (e.g. a warning callout: "contradicts ADR-0007 — but worth reopening because…"). Don't list every theoretical refactor an ADR forbids.

See HTML-REPORT.md for the full HTML scaffold, diagram patterns, and styling guidance.

Do NOT propose interfaces yet. After the file is written, ask the user: "Which of these would you like to explore?"

3. Grilling loop

Once the user picks a candidate, run the /grilling skill to walk the design tree with them — constraints, dependencies, the shape of the deepened module, what sits behind the seam, what tests survive.

Side effects happen inline as decisions crystallize — run the /domain-modeling skill to keep the domain model current as you go:

• Naming a deepened module after a concept not in CONTEXT.md? Add the term to CONTEXT.md. Create the file lazily if it doesn't exist.
• Sharpening a fuzzy term during the conversation? Update CONTEXT.md right there.
• User rejects the candidate with a load-bearing reason? Offer an ADR, framed as: "Want me to record this as an ADR so future architecture reviews don't re-suggest it?" Only offer when the reason would actually be needed by a future explorer to avoid re-suggesting the same thing — skip ephemeral reasons ("not worth it right now") and self-evident ones.
• Want to explore alternative interfaces for the deepened module? Run the /codebase-design skill and use its design-it-twice parallel sub-agent pattern.

Run a stateful /grilling session whose only output is workflow specs. Use the grilling discipline — relentless, one question at a time, a recommended answer attached to each — aimed at the vocabulary and goal below. Create, edit, and delete specs as the grilling resolves things.

The loop lens

A loop is a recurring pattern in the user's life: their career, their week, their morning, a single repeated activity. Picturing a life as loops within loops reveals how predictable its activities really are — which is what makes them worth delegating. Use the lens to find loops worth specifying, and propose ones the user hasn't noticed.

A workflow is the spec of one loop, made real. You run a workflow on a loop — the loop is its running instantiation. Workflows live in workflows/*.md and are the source of truth.

Vocabulary

A shared language, reached for only when a workflow calls for it — never a checklist. Mandate nothing structural: a workflow needs no AI, no checkpoint, and no schedule unless the grilling shows it does.

• Trigger — what fires each run: an event (a new email, a new issue) or a schedule (every morning). Event-triggering is usually the more efficient.
• Checkpoint — a human-in-the-loop point where the user is asked to verify or decide. Some workflows have none and run autonomously; some use no AI at all.
• Push right — defer the checkpoint as far as it will go. Do maximal work before involving the human, so they are asked once, late, with everything prepared.
• Brief — what a checkpoint presents: a tight, decision-ready summary — what was produced, why, and a link down to the asset itself — never the raw output. The user reads a brief, not a draft. Speed of review is imperative.

Definition of done

A workflow spec is done when an implementer agent could build it without asking a single question. Grill until then; nothing is done while a question remains.

The workspace

• workflows/*.md — one spec per workflow.
• NOTES.md — raw notes on the user's world: the tools they use, the channels they process, and their own terminology for both. When it is empty or thin, interview them about their world before specifying anything. Sharpen fuzzy terms into canonical ones as they surface, and record them here.

Migrate to Shoehorn

Why shoehorn?

shoehorn lets you pass partial data in tests while keeping TypeScript happy. It replaces as assertions with type-safe alternatives.

Test code only. Never use shoehorn in production code.

Problems with as in tests:

• Trained not to use it
• Must manually specify target type
• Double-as (as unknown as Type) for intentionally wrong data

Install

npm i @total-typescript/shoehorn

Migration patterns

Large objects with few needed properties

Before:

type Request = {
  body: { id: string };
  headers: Record<string, string>;
  cookies: Record<string, string>;
  // ...20 more properties
};

it("gets user by id", () => {
  // Only care about body.id but must fake entire Request
  getUser({
    body: { id: "123" },
    headers: {},
    cookies: {},
    // ...fake all 20 properties
  });
});

After:

import { fromPartial } from "@total-typescript/shoehorn";

it("gets user by id", () => {
  getUser(
    fromPartial({
      body: { id: "123" },
    }),
  );
});

as Type → fromPartial()

Before:

getUser({ body: { id: "123" } } as Request);

After:

import { fromPartial } from "@total-typescript/shoehorn";

getUser(fromPartial({ body: { id: "123" } }));

as unknown as Type → fromAny()

Before:

getUser({ body: { id: 123 } } as unknown as Request); // wrong type on purpose

After:

import { fromAny } from "@total-typescript/shoehorn";

getUser(fromAny({ body: { id: 123 } }));

When to use each

Workflow

1. Gather requirements - ask user:

   • What test files have as assertions causing problems?
   • Are they dealing with large objects where only some properties matter?
   • Do they need to pass intentionally wrong data for error testing?

2. Install and migrate:

   • Install: npm i @total-typescript/shoehorn
   • Find test files with as assertions: grep -r " as [A-Z]" --include="*.test.ts" --include="*.spec.ts"
   • Replace as Type with fromPartial()
   • Replace as unknown as Type with fromAny()
   • Add imports from @total-typescript/shoehorn
   • Run type check to verify

Obsidian Vault

Vault location

/mnt/d/Obsidian Vault/AI Research/

Mostly flat at root level.

Naming conventions

• Index notes: aggregate related topics (e.g., Ralph Wiggum Index.md, Skills Index.md, RAG Index.md)
• Title case for all note names
• No folders for organization - use links and index notes instead

Linking

• Use Obsidian [[wikilinks]] syntax: [[Note Title]]
• Notes link to dependencies/related notes at the bottom
• Index notes are just lists of [[wikilinks]]

Workflows

Search for notes

# Search by filename
find "/mnt/d/Obsidian Vault/AI Research/" -name "*.md" | grep -i "keyword"

# Search by content
grep -rl "keyword" "/mnt/d/Obsidian Vault/AI Research/" --include="*.md"

Or use Grep/Glob tools directly on the vault path.

Create a new note

1. Use Title Case for filename
2. Write content as a unit of learning (per vault rules)
3. Add [[wikilinks]] to related notes at the bottom
4. If part of a numbered sequence, use the hierarchical numbering scheme

Find related notes

Search for [[Note Title]] across the vault to find backlinks:

grep -rl "\\[\\[Note Title\\]\\]" "/mnt/d/Obsidian Vault/AI Research/"

Find index notes

find "/mnt/d/Obsidian Vault/AI Research/" -name "*Index*"

Prototype

A prototype is throwaway code that answers a question. The question decides the shape.

Pick a branch

Identify which question is being answered — from the user's prompt, the surrounding code, or by asking if the user is around:

• "Does this logic / state model feel right?" → LOGIC.md. Build a tiny interactive terminal app that pushes the state machine through cases that are hard to reason about on paper.
• "What should this look like?" → UI.md. Generate several radically different UI variations on a single route, switchable via a URL search param and a floating bottom bar.

The two branches produce very different artifacts — getting this wrong wastes the whole prototype. If the question is genuinely ambiguous and the user isn't reachable, default to whichever branch better matches the surrounding code (a backend module → logic; a page or component → UI) and state the assumption at the top of the prototype.

Rules that apply to both

1. Throwaway from day one, and clearly marked as such. Locate the prototype code close to where it will actually be used (next to the module or page it's prototyping for) so context is obvious — but name it so a casual reader can see it's a prototype, not production. For throwaway UI routes, obey whatever routing convention the project already uses; don't invent a new top-level structure.
2. One command to run. Whatever the project's existing task runner supports — pnpm <name>, python <path>, bun <path>, etc. The user must be able to start it without thinking.
3. No persistence by default. State lives in memory. Persistence is the thing the prototype is checking, not something it should depend on. If the question explicitly involves a database, hit a scratch DB or a local file with a clear "PROTOTYPE — wipe me" name.
4. Skip the polish. No tests, no error handling beyond what makes the prototype runnable, no abstractions. The point is to learn something fast and then delete it.
5. Surface the state. After every action (logic) or on every variant switch (UI), print or render the full relevant state so the user can see what changed.
6. Delete or absorb when done. When the prototype has answered its question, either delete it or fold the validated decision into the real code — don't leave it rotting in the repo.

When done

The answer is the only thing worth keeping from a prototype. Capture it somewhere durable (commit message, ADR, issue, or a NOTES.md next to the prototype) along with the question it was answering. If the user is around, that capture is a quick conversation; if not, leave the placeholder so they (or you, on the next pass) can fill in the verdict before deleting the prototype.

QA Session

Run an interactive QA session. The user describes problems they're encountering. You clarify, explore the codebase for context, and file GitHub issues that are durable, user-focused, and use the project's domain language.

For each issue the user raises

1. Listen and lightly clarify

Let the user describe the problem in their own words. Ask at most 2-3 short clarifying questions focused on:

• What they expected vs what actually happened
• Steps to reproduce (if not obvious)
• Whether it's consistent or intermittent

Do NOT over-interview. If the description is clear enough to file, move on.

2. Explore the codebase in the background

While talking to the user, kick off an Agent (subagent_type=Explore) in the background to understand the relevant area. The goal is NOT to find a fix — it's to:

• Learn the domain language used in that area (check UBIQUITOUS_LANGUAGE.md)
• Understand what the feature is supposed to do
• Identify the user-facing behavior boundary

This context helps you write a better issue — but the issue itself should NOT reference specific files, line numbers, or internal implementation details.

3. Assess scope: single issue or breakdown?

Before filing, decide whether this is a single issue or needs to be broken down into multiple issues.

Break down when:

• The fix spans multiple independent areas (e.g. "the form validation is wrong AND the success message is missing AND the redirect is broken")
• There are clearly separable concerns that different people could work on in parallel
• The user describes something that has multiple distinct failure modes or symptoms

Keep as a single issue when:

• It's one behavior that's wrong in one place
• The symptoms are all caused by the same root behavior

4. File the GitHub issue(s)

Create issues with gh issue create. Do NOT ask the user to review first — just file and share URLs.

Issues must be durable — they should still make sense after major refactors. Write from the user's perspective.

For a single issue

Use this template:

## What happened

[Describe the actual behavior the user experienced, in plain language]

## What I expected

[Describe the expected behavior]

## Steps to reproduce

1. [Concrete, numbered steps a developer can follow]
2. [Use domain terms from the codebase, not internal module names]
3. [Include relevant inputs, flags, or configuration]

## Additional context

[Any extra observations from the user or from codebase exploration that help frame the issue — e.g. "this only happens when using the Docker layer, not the filesystem layer" — use domain language but don't cite files]

For a breakdown (multiple issues)

Create issues in dependency order (blockers first) so you can reference real issue numbers.

Use this template for each sub-issue:

## Parent issue

#<parent-issue-number> (if you created a tracking issue) or "Reported during QA session"

## What's wrong

[Describe this specific behavior problem — just this slice, not the whole report]

## What I expected

[Expected behavior for this specific slice]

## Steps to reproduce

1. [Steps specific to THIS issue]

## Blocked by

- #<issue-number> (if this issue can't be fixed until another is resolved)

Or "None — can start immediately" if no blockers.

## Additional context

[Any extra observations relevant to this slice]

When creating a breakdown:

• Prefer many thin issues over few thick ones — each should be independently fixable and verifiable
• Mark blocking relationships honestly — if issue B genuinely can't be tested until issue A is fixed, say so. If they're independent, mark both as "None — can start immediately"
• Create issues in dependency order so you can reference real issue numbers in "Blocked by"
• Maximize parallelism — the goal is that multiple people (or agents) can grab different issues simultaneously

Rules for all issue bodies

• No file paths or line numbers — these go stale
• Use the project's domain language (check UBIQUITOUS_LANGUAGE.md if it exists)
• Describe behaviors, not code — "the sync service fails to apply the patch" not "applyPatch() throws on line 42"
• Reproduction steps are mandatory — if you can't determine them, ask the user
• Keep it concise — a developer should be able to read the issue in 30 seconds

After filing, print all issue URLs (with blocking relationships summarized) and ask: "Next issue, or are we done?"

5. Continue the session

Keep going until the user says they're done. Each issue is independent — don't batch them.

This skill will be invoked when the user wants to create a refactor request. You should go through the steps below. You may skip steps if you don't consider them necessary.

1. Ask the user for a long, detailed description of the problem they want to solve and any potential ideas for solutions.

2. Explore the repo to verify their assertions and understand the current state of the codebase.

3. Ask whether they have considered other options, and present other options to them.

4. Interview the user about the implementation. Be extremely detailed and thorough.

5. Hammer out the exact scope of the implementation. Work out what you plan to change and what you plan not to change.

6. Look in the codebase to check for test coverage of this area of the codebase. If there is insufficient test coverage, ask the user what their plans for testing are.

7. Break the implementation into a plan of tiny commits. Remember Martin Fowler's advice to "make each refactoring step as small as possible, so that you can always see the program working."

8. Create a GitHub issue with the refactor plan. Use the following template for the issue description:

Spin up a background agent to do the research, so you keep working while it reads.

Its job:

1. Investigate the question against primary sources — official docs, source code, specs, first-party APIs — not a secondary write-up of them. Follow every claim back to the source that owns it.
2. Write the findings to a single Markdown file, citing each claim's source.
3. Save it where the repo already keeps such notes; match the existing convention, and if there is none, put it somewhere sensible and say where.

1. See the current state of the merge/rebase. Check git history, and the conflicting files.

2. Find the primary sources for each conflict. Understand deeply why each change was made, and what the original intent was. Read the commit messages, check the PRs, check original issues/tickets.

3. Resolve each hunk. Preserve both intents where possible. Where incompatible, pick the one matching the merge's stated goal and note the trade-off. Do not invent new behaviour. Always resolve; never --abort.

4. Discover the project's automated checks and run them — typically typecheck, then tests, then format. Fix anything the merge broke.

5. Finish the merge/rebase. Stage everything and commit. If rebasing, continue the rebase process until all commits are rebased.

Scaffold Exercises

Create exercise directory structures that pass pnpm ai-hero-cli internal lint, then commit with git commit.

Directory naming

• Sections: XX-section-name/ inside exercises/ (e.g., 01-retrieval-skill-building)
• Exercises: XX.YY-exercise-name/ inside a section (e.g., 01.03-retrieval-with-bm25)
• Section number = XX, exercise number = XX.YY
• Names are dash-case (lowercase, hyphens)

Exercise variants

Each exercise needs at least one of these subfolders:

• problem/ - student workspace with TODOs
• solution/ - reference implementation
• explainer/ - conceptual material, no TODOs

When stubbing, default to explainer/ unless the plan specifies otherwise.

Required files

Each subfolder (problem/, solution/, explainer/) needs a readme.md that:

• Is not empty (must have real content, even a single title line works)
• Has no broken links

When stubbing, create a minimal readme with a title and a description:

# Exercise Title

Description here

If the subfolder has code, it also needs a main.ts (>1 line). But for stubs, a readme-only exercise is fine.

Workflow

1. Parse the plan - extract section names, exercise names, and variant types
2. Create directories - mkdir -p for each path
3. Create stub readmes - one readme.md per variant folder with a title
4. Run lint - pnpm ai-hero-cli internal lint to validate
5. Fix any errors - iterate until lint passes

Lint rules summary

The linter (pnpm ai-hero-cli internal lint) checks:

• Each exercise has subfolders (problem/, solution/, explainer/)
• At least one of problem/, explainer/, or explainer.1/ exists
• readme.md exists and is non-empty in the primary subfolder
• No .gitkeep files
• No speaker-notes.md files
• No broken links in readmes
• No pnpm run exercise commands in readmes
• main.ts required per subfolder unless it's readme-only

Moving/renaming exercises

When renumbering or moving exercises:

1. Use git mv (not mv) to rename directories - preserves git history
2. Update the numeric prefix to maintain order
3. Re-run lint after moves

Example:

git mv exercises/01-retrieval/01.03-embeddings exercises/01-retrieval/01.04-embeddings

Example: stubbing from a plan

Given a plan like:

Section 05: Memory Skill Building
- 05.01 Introduction to Memory
- 05.02 Short-term Memory (explainer + problem + solution)
- 05.03 Long-term Memory

Create:

mkdir -p exercises/05-memory-skill-building/05.01-introduction-to-memory/explainer
mkdir -p exercises/05-memory-skill-building/05.02-short-term-memory/{explainer,problem,solution}
mkdir -p exercises/05-memory-skill-building/05.03-long-term-memory/explainer

Then create readme stubs:

exercises/05-memory-skill-building/05.01-introduction-to-memory/explainer/readme.md -> "# Introduction to Memory"
exercises/05-memory-skill-building/05.02-short-term-memory/explainer/readme.md -> "# Short-term Memory"
exercises/05-memory-skill-building/05.02-short-term-memory/problem/readme.md -> "# Short-term Memory"
exercises/05-memory-skill-building/05.02-short-term-memory/solution/readme.md -> "# Short-term Memory"
exercises/05-memory-skill-building/05.03-long-term-memory/explainer/readme.md -> "# Long-term Memory"

Setup Matt Pocock's Skills

Scaffold the per-repo configuration that the engineering skills assume:

• Issue tracker — where issues live (GitHub by default; local markdown is also supported out of the box)
• Triage labels — the strings used for the five canonical triage roles
• Domain docs — where CONTEXT.md and ADRs live, and the consumer rules for reading them

This is a prompt-driven skill, not a deterministic script. Explore, present what you found, confirm with the user, then write.

Process

1. Explore

Look at the current repo to understand its starting state. Read whatever exists; don't assume:

• git remote -v and .git/config — is this a GitHub repo? Which one?
• AGENTS.md and CLAUDE.md at the repo root — does either exist? Is there already an ## Agent skills section in either?
• CONTEXT.md and CONTEXT-MAP.md at the repo root
• docs/adr/ and any src/*/docs/adr/ directories
• docs/agents/ — does this skill's prior output already exist?
• .scratch/ — sign that a local-markdown issue tracker convention is already in use

2. Present findings and ask

Summarise what's present and what's missing. Then walk the user through the three decisions one at a time — present a section, get the user's answer, then move to the next. Don't dump all three at once.

Assume the user does not know what these terms mean. Each section starts with a short explainer (what it is, why these skills need it, what changes if they pick differently). Then show the choices and the default.

Section A — Issue tracker.

Explainer: The "issue tracker" is where issues live for this repo. Skills like to-tickets, triage, to-spec, and qa read from and write to it — they need to know whether to call gh issue create, write a markdown file under .scratch/, or follow some other workflow you describe. Pick the place you actually track work for this repo.

Default posture: these skills were designed for GitHub. If a git remote points at GitHub, propose that. If a git remote points at GitLab (gitlab.com or a self-hosted host), propose GitLab. Otherwise (or if the user prefers), offer:

• GitHub — issues live in the repo's GitHub Issues (uses the gh CLI)
• GitLab — issues live in the repo's GitLab Issues (uses the glab CLI)
• Local markdown — issues live as files under .scratch/<feature>/ in this repo (good for solo projects or repos without a remote)
• Other (Jira, Linear, etc.) — ask the user to describe the workflow in one paragraph; the skill will record it as freeform prose

If — and only if — the user picked GitHub or GitLab, ask one follow-up:

Explainer: Open-source repos often receive feature requests as pull requests, not just issues — a PR is an issue with attached code. If you turn this on, /triage pulls external PRs into the same queue and runs them through the same labels and states as issues (collaborators' in-flight PRs are left alone). Leave it off if PRs aren't a request surface for you.

• PRs as a request surface — yes / no (default: no). Record the answer in docs/agents/issue-tracker.md. For local-markdown and other trackers, skip this question — there are no PRs.

Section B — Triage label vocabulary.

Explainer: When the triage skill processes an incoming issue, it moves it through a state machine — needs evaluation, waiting on reporter, ready for an AFK agent to pick up, ready for a human, or won't fix. To do that, it needs to apply labels (or the equivalent in your issue tracker) that match strings you've actually configured. If your repo already uses different label names (e.g. bug:triage instead of needs-triage), map them here so the skill applies the right ones instead of creating duplicates.

The five canonical roles:

• needs-triage — maintainer needs to evaluate
• needs-info — waiting on reporter
• ready-for-agent — fully specified, AFK-ready (an agent can pick it up with no human context)
• ready-for-human — needs human implementation
• wontfix — will not be actioned

Default: each role's string equals its name. Ask the user if they want to override any. If their issue tracker has no existing labels, the defaults are fine.

Section C — Domain docs.

Explainer: Some skills (improve-codebase-architecture, diagnosing-bugs, tdd) read a CONTEXT.md file to learn the project's domain language, and docs/adr/ for past architectural decisions. They need to know whether the repo has one global context or multiple (e.g. a monorepo with separate frontend/backend contexts) so they look in the right place.

Confirm the layout:

• Single-context — one CONTEXT.md + docs/adr/ at the repo root. Most repos are this.
• Multi-context — CONTEXT-MAP.md at the root pointing to per-context CONTEXT.md files (typically a monorepo).

3. Confirm and edit

Show the user a draft of:

• The ## Agent skills block to add to whichever of CLAUDE.md / AGENTS.md is being edited (see step 4 for selection rules)
• The contents of docs/agents/issue-tracker.md, docs/agents/triage-labels.md, docs/agents/domain.md

Let them edit before writing.

4. Write

Pick the file to edit:

• If CLAUDE.md exists, edit it.
• Else if AGENTS.md exists, edit it.
• If neither exists, ask the user which one to create — don't pick for them.

Never create AGENTS.md when CLAUDE.md already exists (or vice versa) — always edit the one that's already there.

If an ## Agent skills block already exists in the chosen file, update its contents in-place rather than appending a duplicate. Don't overwrite user edits to the surrounding sections.

The block:

## Agent skills

### Issue tracker

[one-line summary of where issues are tracked, plus whether external PRs are a triage surface]. See `docs/agents/issue-tracker.md`.

### Triage labels

[one-line summary of the label vocabulary]. See `docs/agents/triage-labels.md`.

### Domain docs

[one-line summary of layout — "single-context" or "multi-context"]. See `docs/agents/domain.md`.

Then write the three docs files using the seed templates in this skill folder as a starting point:

• issue-tracker-github.md — GitHub issue tracker
• issue-tracker-gitlab.md — GitLab issue tracker
• issue-tracker-local.md — local-markdown issue tracker
• triage-labels.md — label mapping
• domain.md — domain doc consumer rules + layout

For "other" issue trackers, write docs/agents/issue-tracker.md from scratch using the user's description.

5. Done

Tell the user the setup is complete and which engineering skills will now read from these files. Mention they can edit docs/agents/*.md directly later — re-running this skill is only necessary if they want to switch issue trackers or restart from scratch.

Setup Pre-Commit Hooks

What This Sets Up

• Husky pre-commit hook
• lint-staged running Prettier on all staged files
• Prettier config (if missing)
• typecheck and test scripts in the pre-commit hook

Steps

1. Detect package manager

Check for package-lock.json (npm), pnpm-lock.yaml (pnpm), yarn.lock (yarn), bun.lockb (bun). Use whichever is present. Default to npm if unclear.

2. Install dependencies

Install as devDependencies:

husky lint-staged prettier

3. Initialize Husky

npx husky init

This creates .husky/ dir and adds prepare: "husky" to package.json.

4. Create .husky/pre-commit

Write this file (no shebang needed for Husky v9+):

npx lint-staged
npm run typecheck
npm run test

Adapt: Replace npm with detected package manager. If repo has no typecheck or test script in package.json, omit those lines and tell the user.

5. Create .lintstagedrc

{
  "*": "prettier --ignore-unknown --write"
}

6. Create .prettierrc (if missing)

Only create if no Prettier config exists. Use these defaults:

{
  "useTabs": false,
  "tabWidth": 2,
  "printWidth": 80,
  "singleQuote": false,
  "trailingComma": "es5",
  "semi": true,
  "arrowParens": "always"
}

7. Verify

• .husky/pre-commit exists and is executable
• .lintstagedrc exists
• prepare script in package.json is "husky"
• prettier config exists
• Run npx lint-staged to verify it works

8. Commit

Stage all changed/created files and commit with message: Add pre-commit hooks (husky + lint-staged + prettier)

This will run through the new pre-commit hooks — a good smoke test that everything works.

Notes

• Husky v9+ doesn't need shebangs in hook files
• prettier --ignore-unknown skips files Prettier can't parse (images, etc.)
• The pre-commit runs lint-staged first (fast, staged-only), then full typecheck and tests

Test-Driven Development

TDD is the red → green loop. This skill is the reference that makes that loop produce tests worth keeping: what a good test is, where tests go, the anti-patterns, and the rules of the loop. Every section applies on every cycle — consult them before and during the loop, not after.

When exploring the codebase, read CONTEXT.md (if it exists) so test names and interface vocabulary match the project's domain language, and respect ADRs in the area you're touching.

What a good test is

Tests verify behavior through public interfaces, not implementation details. Code can change entirely; tests shouldn't. A good test reads like a specification — "user can checkout with valid cart" tells you exactly what capability exists — and survives refactors because it doesn't care about internal structure.

See tests.md for examples and mocking.md for mocking guidelines.

Seams — where tests go

A seam is the public boundary you test at: the interface where you observe behavior without reaching inside. Tests live at seams, never against internals.

Test only at pre-agreed seams. Before writing any test, write down the seams under test and confirm them with the user. No test is written at an unconfirmed seam. You can't test everything — agreeing the seams up front is how testing effort lands on the critical paths and complex logic instead of every edge case.

Ask: "What's the public interface, and which seams should we test?"

Anti-patterns

• Implementation-coupled — mocks internal collaborators, tests private methods, or verifies through a side channel (querying the database instead of using the interface). The tell: the test breaks when you refactor but behavior hasn't changed.
• Tautological — the assertion recomputes the expected value the way the code does (expect(add(a, b)).toBe(a + b), a snapshot derived by hand the same way, a constant asserted equal to itself), so it passes by construction and can never disagree with the code. Expected values must come from an independent source of truth — a known-good literal, a worked example, the spec.
• Horizontal slicing — writing all tests first, then all implementation. Bulk tests verify imagined behavior: you test the shape of things rather than user-facing behavior, the tests go insensitive to real changes, and you commit to test structure before understanding the implementation. Work in vertical slices instead — one test → one implementation → repeat, each test a tracer bullet that responds to what the last cycle taught you.

Rules of the loop

• Red before green. Write the failing test first, then only enough code to pass it. Don't anticipate future tests or add speculative features.
• One slice at a time. One seam, one test, one minimal implementation per cycle.
• Refactoring is not part of the loop. It belongs to the review stage (see the code-review skill), not the red → green implementation cycle.

The user has asked you to teach them something. This is a stateful request - they intend to learn the topic over multiple sessions.

Teaching Workspace

Treat the current directory as a teaching workspace. The state of their learning is captured in this directory in several files:

• MISSION.md: A document capturing the reason the user is interested in the topic. This should be used to ground all teaching. Use the format in MISSION-FORMAT.md.
• ./reference/*.html: A directory of reference materials. These are the compressed learnings from the lessons - cheat sheets, reference algorithms, syntax, yoga poses, glossaries. They are the raw units of learning. They should be beautiful documents which print out well, and are designed for quick reference.
• RESOURCES.md: A list of resources which can be explored to ground your teaching in contextual knowledge, or to acquire knowledge and wisdom. Use the format in RESOURCES-FORMAT.md.
• ./learning-records/*.md: A directory of learning records, which capture what the user has learned. These are loosely equivalent to architectural decision records in software development - they capture non-obvious lessons and key insights that may need to be revised later, or drive future sessions. These should be used to calculate the zone of proximal development. They are titled 0001-<dash-case-name>.md, where the number increments each time. Use the format in LEARNING-RECORD-FORMAT.md.
• ./lessons/*.html: A directory of lessons. A lesson is a single, self-contained HTML output that teaches one tightly-scoped thing tied to the mission. This is the primary unit of teaching in this workspace.
• ./assets/*: Reusable components shared across lessons. See Assets.
• NOTES.md: A scratchpad for you to jot down user preferences, or working notes.

Philosophy

To learn at a deep level, the user needs three things:

• Knowledge, captured from high-quality, high-trust resources
• Skills, acquired through highly-relevant interactive lessons devised by you, based on the knowledge
• Wisdom, which comes from interacting with other learners and practitioners

Before the RESOURCES.md is well-populated, your focus should be to find high-quality resources which will help the user acquire knowledge. Never trust your parametric knowledge.

Some topics may require more skills than knowledge. Learning more about theoretical physics might be more knowledge-based. For yoga, more skills-based.

Fluency vs Storage Strength

You should be careful to split between two types of learning:

• Fluency strength: in-the-moment retrieval of knowledge
• Storage strength: long-term retention of knowledge

Fluency can give the user an illusory sense of mastery, but storage strength is the real goal. Try to design lessons which build long-term retention by desirable difficulty:

• Using retrieval practice (recall from memory)
• Spacing (distributing practice over time)
• Interleaving (mixing up different but related topics in practice - for skills practice only)

Lessons

A lesson is the main thing you produce — the unit in which knowledge and skills reach the user. Each lesson is one self-contained HTML file, saved to ./lessons/ and titled 0001-<dash-case-name>.html where the number increments each time.

A lesson should be beautiful — clean, readable typography and layout — since the user will return to these later to review. Think Tufte.

The lesson should be short, and completable very quickly. Learners' working memory is very small, and we need to stay within it. But each lesson should give the user a single tangible win that they can build on. It should be directly tied to the mission, and should be in the user's zone of proximal development.

If possible, open the lesson file for the user by running a CLI command.

Each lesson should link via HTML anchors to other lessons and reference documents.

Each lesson should recommend a primary source for the user to read or watch. This should be the most high-quality, high-trust resource you found on the topic.

Each lesson should contain a reminder to ask followup questions to the agent. The agent is their teacher, and can assist with anything that's unclear.

Assets

Lessons are built from reusable components, stored in ./assets/: stylesheets, quiz widgets, simulators, diagram helpers — anything a second lesson could reuse.

Reuse is the default, not the exception. Before authoring a lesson, read ./assets/ and build from the components already there. When a lesson needs something new and reusable, write it as a component in ./assets/ and link to it — never inline code a future lesson would duplicate.

A shared stylesheet is the first component every workspace earns: every lesson links it, so the lessons look like one consistent course rather than a pile of one-offs. As the workspace grows, so should the component library.

The Mission

Every lesson should be tied into the mission - the reason that the user is interested in learning about the topic.

If the user is unclear about the mission, or the MISSION.md is not populated, your first job should be to question the user on why they want to learn this.

Failing to understand the mission will mean knowledge acquisition is not grounded in real-world goals. Lessons will feel too abstract. You will have no way of judging what the user should do next.

Missions may change as the user develops more skills and knowledge. This is normal - make sure to update the MISSION.md and add a learning record to capture the change. Confirm with the user before changing the mission.

Zone Of Proximal Development

Each lesson, the user should always feel as if they are being challenged 'just enough'.

The user may specify an exact thing they want to learn. If they don't, figure out their zone of proximal development by:

• Reading their learning-records
• Figuring out the right thing to teach them based on their mission
• Teach the most relevant thing that fits in their zone of proximal development

Knowledge

Lessons should be designed around a skill the user is going to learn. The knowledge in the lesson should be only what's required to acquire that skill. You teach the knowledge first, then get the user to practice the skills via an interactive feedback loop.

Knowledge should first be gathered from trusted resources. Use RESOURCES.md to keep track of them. Lessons should be littered with citations - links to external resources to back up any claim made. This increases the trustworthiness of the lesson.

For acquiring knowledge, difficulty is the enemy. It eats working memory you need for understanding.

Skills

If knowledge is all about acquisition, skills are about durability and flexibility. Make the knowledge stick.

For skill acquisition, difficulty is the tool. Effortful retrieval is what builds storage strength. Skills should be taught through interactive lessons. There are several tools at your disposal:

• Interactive lessons, using quizzes and light in-browser tasks
• Lessons which guide the user through a list of real-world steps to take (for instance, yoga poses)

Each of these should be based on a feedback loop, where the user receives feedback on their performance. This feedback loop should be as tight as possible, giving feedback immediately - and ideally automatically.

For quizzes, each answer should be exactly the same number of words (and characters, if possible). Don't give the user any clues about the answer through formatting.

Acquiring Wisdom

Wisdom comes from true real-world interaction - testing your skills outside the learning environment.

When the user asks a question that appears to require wisdom, your default posture should be to attempt to answer - but to ultimately delegate to a community.

A community is a place (online or offline) where the user can test their skills in the real world. This might be a forum, a subreddit, a real-world class (budget permitting) or a local interest group.

You should attempt to find high-reputation communities the user can join. If the user expresses a preference that they don't want to join a community, respect it.

Reference Documents

While creating lessons, you should also create reference documents. Lessons can reference these documents - they are useful for tracking raw units of knowledge useful across lessons.

Lessons will rarely be revisited later - reference documents will be. They should be the compressed essence of the lesson, in a format designed for quick reference.

Some learning topics lend themselves to reference:

• Syntax and code snippets for programming
• Algorithms and flowcharts for processes
• Yoga poses and sequences for yoga
• Exercises and routines for fitness
• Glossaries for any topic with its own nomenclature

Glossaries, in particular, are an essential reference. Once one is created, it should be adhered to in every lesson.

NOTES.md

The user will sometimes express preferences of how they want to be taught, or things you should keep in mind. This is the place to record those preferences, so you can refer back to them when designing lessons or working with the user.

This skill takes the current conversation context and codebase understanding and produces a spec (you may know this document as a PRD). Do NOT interview the user — just synthesize what you already know.

The issue tracker and triage label vocabulary should have been provided to you — run /setup-matt-pocock-skills if not.

Process

1. Explore the repo to understand the current state of the codebase, if you haven't already. Use the project's domain glossary vocabulary throughout the spec, and respect any ADRs in the area you're touching.

2. Sketch out the seams at which you're going to test the feature. Existing seams should be preferred to new ones. Use the highest seam possible. If new seams are needed, propose them at the highest point you can. The fewer seams across the codebase, the better - the ideal number is one.

Check with the user that these seams match their expectations.

3. Write the spec using the template below, then publish it to the project issue tracker. Apply the ready-for-agent triage label - no need for additional triage.

To Tickets

Break a plan, spec, or conversation into a set of tickets — tracer-bullet vertical slices, each declaring the tickets that block it.

The issue tracker and triage label vocabulary should have been provided to you — run /setup-matt-pocock-skills if not.

Process

1. Gather context

Work from whatever is already in the conversation context. If the user passes a reference (a spec path, an issue number or URL) as an argument, fetch it and read its full body and comments.

2. Explore the codebase (optional)

If you have not already explored the codebase, do so to understand the current state of the code. Ticket titles and descriptions should use the project's domain glossary vocabulary, and respect ADRs in the area you're touching.

Look for opportunities to prefactor the code to make the implementation easier. "Make the change easy, then make the easy change."

3. Draft vertical slices

Break the work into tracer bullet tickets.

Give each ticket its blocking edges — the other tickets that must complete before it can start. A ticket with no blockers can start immediately.

Wide refactors are the exception to vertical slicing. A wide refactor is one mechanical change — rename a column, retype a shared symbol — whose blast radius fans across the whole codebase, so a single edit breaks thousands of call sites at once and no vertical slice can land green. Don't force it into a tracer bullet; sequence it as expand–contract. First expand: add the new form beside the old so nothing breaks. Then migrate the call sites over in batches sized by blast radius (per package, per directory), each batch its own ticket blocked by the expand, keeping CI green batch to batch because the old form still exists. Finally contract: delete the old form once no caller remains, in a ticket blocked by every migrate batch. When even the batches can't stay green alone, keep the sequence but let them share an integration branch that all block a final integrate-and-verify ticket — green is promised only there.

4. Quiz the user

Present the proposed breakdown as a numbered list. For each ticket, show:

• Title: short descriptive name
• Blocked by: which other tickets (if any) must complete first
• What it delivers: the end-to-end behaviour this ticket makes work

Ask the user:

• Does the granularity feel right? (too coarse / too fine)
• Are the blocking edges correct — does each ticket only depend on tickets that genuinely gate it?
• Should any tickets be merged or split further?

Iterate until the user approves the breakdown.

5. Publish the tickets to the configured tracker

Publish the approved tickets. How depends on the tracker /setup-matt-pocock-skills configured — the tickets are the same either way, only the shape of the blocking edges changes:

• Local files → write one tickets.md in the repo root, all tickets in dependency order (blockers first), each with its "Blocked by" listing the titles it depends on. Use the file template below.
• A real issue tracker (GitHub, Linear, …) → publish one issue per ticket in dependency order (blockers first) so each ticket's blocking edges can reference real identifiers. Use the platform's native blocking / sub-issue relationship where it has one; otherwise set each ticket's "Blocked by" to the blocking issues. Apply the ready-for-agent triage label unless instructed otherwise — the tickets are agent-grabbable by construction.

Do NOT close or modify any parent issue.

In either form, avoid specific file paths or code snippets — they go stale fast. Exception: if a prototype produced a snippet that encodes a decision more precisely than prose can (state machine, reducer, schema, type shape), inline it and note briefly that it came from a prototype. Trim to the decision-rich parts — not a working demo, just the important bits.

Work the frontier one ticket at a time with /implement, clearing context between tickets.

Triage

Move issues on the project issue tracker through a small state machine of triage roles.

If this repo treats external pull requests as a request surface (see the issue-tracker config), triage covers them too: a PR is an issue with attached code — same roles, same states, same machine, with a few deltas marked "for a PR" below. Resolve a bare #42 to an issue or PR per the tracker config.

Every comment or issue posted to the issue tracker during triage must start with this disclaimer:

> *This was generated by AI during triage.*

Reference docs

• AGENT-BRIEF.md — how to write durable agent briefs
• OUT-OF-SCOPE.md — how the .out-of-scope/ knowledge base works

Roles

Two category roles:

• bug — something is broken
• enhancement — new feature or improvement

Five state roles:

• needs-triage — maintainer needs to evaluate
• needs-info — waiting on reporter for more information
• ready-for-agent — fully specified, ready for an AFK agent
• ready-for-human — needs human implementation
• wontfix — will not be actioned

For a PR, the same states read against the attached code: ready-for-agent means a brief is attached and an agent should take the next step on the diff; ready-for-human means it's ready for a human to merge.

Every triaged issue should carry exactly one category role and one state role. If state roles conflict, flag it and ask the maintainer before doing anything else.

These are canonical role names — the actual label strings used in the issue tracker may differ. The mapping should have been provided to you - run /setup-matt-pocock-skills if not.

State transitions: an unlabeled issue normally goes to needs-triage first; from there it moves to needs-info, ready-for-agent, ready-for-human, or wontfix. needs-info returns to needs-triage once the reporter replies. The maintainer can override at any time — flag transitions that look unusual and ask before proceeding.

Invocation

The maintainer invokes /triage and describes what they want in natural language. Interpret the request and act. Examples:

• "Show me anything that needs my attention"
• "Let's look at #42" (issue or PR)
• "Move #42 to ready-for-agent"
• "What's ready for agents to pick up?"

Show what needs attention

Query the issue tracker and present three buckets, oldest first:

1. Unlabeled — never triaged.
2. needs-triage — evaluation in progress.
3. needs-info with reporter activity since the last triage notes — needs re-evaluation.

When PRs are in scope, include external PRs in these buckets and tag each line [PR] or [issue]. Discovery surfaces only external PRs (the tracker config defines who counts as external) — a collaborator's in-flight PR is not triage work. This filter is discovery-only; an explicitly named PR is always triaged regardless of author.

Show counts and a one-line summary per item. Let the maintainer pick.

Triage a specific issue or PR

1. Gather context. Read the full issue or PR (body, comments, labels, author, dates; for a PR, the diff too). Parse any prior triage notes so you don't re-ask resolved questions. Explore the codebase using the project's domain glossary, respecting ADRs in the area. Run two checks against the codebase: (a) redundancy — search for an existing implementation of the requested behavior by domain concept (not just the request's wording), and report where you looked. If found, it's an already-implemented wontfix (step 5). (b) prior rejection — read .out-of-scope/*.md and surface any that resembles this request.

2. Recommend. Tell the maintainer your category and state recommendation with reasoning, plus a brief codebase summary relevant to the request — including whether it's already implemented. Wait for direction.

3. Verify the claim. Before any grilling, check that the claim holds up. For a bug, reproduce it from the reporter's steps. For a PR, confirm the diff does what it claims — check it out, run the relevant tests or commands. Report what happened: confirmed (with code path), failed, or insufficient detail (a strong needs-info signal). A confirmed verification makes a much stronger agent brief.

4. Grill (if needed). If the request needs fleshing out, run the /grilling and /domain-modeling skills together — grill it into shape one question at a time, sharpening domain terms and updating CONTEXT.md/ADRs inline as decisions land.

5. Apply the outcome:

   • ready-for-agent — post an agent brief comment (AGENT-BRIEF.md).
   • ready-for-human — same structure as an agent brief, but note why it can't be delegated (judgment calls, external access, design decisions, manual testing).
   • needs-info — post triage notes (template below).
   • wontfix — close, with the comment depending on why:
     • Already implemented — the change already exists in the codebase. Point to where it lives; do not write to .out-of-scope/ (that KB is for rejected requests, not built ones).
     • Rejected (bug) — polite explanation, then close.
     • Rejected (enhancement) — write to .out-of-scope/, link to it from a comment, then close (OUT-OF-SCOPE.md).
   • needs-triage — apply the role. Optional comment if there's partial progress.

Quick state override

If the maintainer says "move #42 to ready-for-agent", trust them and apply the role directly. Confirm what you're about to do (role changes, comment, close), then act. Skip grilling. If moving to ready-for-agent without a grilling session, ask whether they want to write an agent brief.

Needs-info template

## Triage Notes

**What we've established so far:**

- point 1
- point 2

**What we still need from you (@reporter):**

- question 1
- question 2

Capture everything resolved during grilling under "established so far" so the work isn't lost. Questions must be specific and actionable, not "please provide more info".

Resuming a previous session

If prior triage notes exist on the issue or PR, read them, check whether the reporter has answered any outstanding questions, and present an updated picture before continuing. Don't re-ask resolved questions.

Ubiquitous Language

Extract and formalize domain terminology from the current conversation into a consistent glossary, saved to a local file.

Process

1. Scan the conversation for domain-relevant nouns, verbs, and concepts
2. Identify problems:
   • Same word used for different concepts (ambiguity)
   • Different words used for the same concept (synonyms)
   • Vague or overloaded terms
3. Propose a canonical glossary with opinionated term choices
4. Write to UBIQUITOUS_LANGUAGE.md in the working directory using the format below
5. Output a summary inline in the conversation

Output Format

Write a UBIQUITOUS_LANGUAGE.md file with this structure:

# Ubiquitous Language

## Order lifecycle

| Term        | Definition                                              | Aliases to avoid      |
| ----------- | ------------------------------------------------------- | --------------------- |
| **Order**   | A customer's request to purchase one or more items      | Purchase, transaction |
| **Invoice** | A request for payment sent to a customer after delivery | Bill, payment request |

## People

| Term         | Definition                                  | Aliases to avoid       |
| ------------ | ------------------------------------------- | ---------------------- |
| **Customer** | A person or organization that places orders | Client, buyer, account |
| **User**     | An authentication identity in the system    | Login, account         |

## Relationships

- An **Invoice** belongs to exactly one **Customer**
- An **Order** produces one or more **Invoices**

## Example dialogue

> **Dev:** "When a **Customer** places an **Order**, do we create the **Invoice** immediately?"
> **Domain expert:** "No — an **Invoice** is only generated once a **Fulfillment** is confirmed. A single **Order** can produce multiple **Invoices** if items ship in separate **Shipments**."
> **Dev:** "So if a **Shipment** is cancelled before dispatch, no **Invoice** exists for it?"
> **Domain expert:** "Exactly. The **Invoice** lifecycle is tied to the **Fulfillment**, not the **Order**."

## Flagged ambiguities

- "account" was used to mean both **Customer** and **User** — these are distinct concepts: a **Customer** places orders, while a **User** is an authentication identity that may or may not represent a **Customer**.

Rules

• Be opinionated. When multiple words exist for the same concept, pick the best one and list the others as aliases to avoid.
• Flag conflicts explicitly. If a term is used ambiguously in the conversation, call it out in the "Flagged ambiguities" section with a clear recommendation.
• Only include terms relevant for domain experts. Skip the names of modules or classes unless they have meaning in the domain language.
• Keep definitions tight. One sentence max. Define what it IS, not what it does.
• Show relationships. Use bold term names and express cardinality where obvious.
• Only include domain terms. Skip generic programming concepts (array, function, endpoint) unless they have domain-specific meaning.
• Group terms into multiple tables when natural clusters emerge (e.g. by subdomain, lifecycle, or actor). Each group gets its own heading and table. If all terms belong to a single cohesive domain, one table is fine — don't force groupings.
• Write an example dialogue. A short conversation (3-5 exchanges) between a dev and a domain expert that demonstrates how the terms interact naturally. The dialogue should clarify boundaries between related concepts and show terms being used precisely.

Re-running

When invoked again in the same conversation:

1. Read the existing UBIQUITOUS_LANGUAGE.md
2. Incorporate any new terms from subsequent discussion
3. Update definitions if understanding has evolved
4. Re-flag any new ambiguities
5. Rewrite the example dialogue to incorporate new terms

A loose idea has arrived — too big for one agent session, and wrapped in fog: the way from here to the destination isn't visible yet. Wayfinding is about finding that way, not charging at the destination. This skill charts the way as a shared map on the repo's issue tracker, then works its tickets one at a time until the route is clear.

The destination varies per effort, and naming it is the first act of charting — it shapes every ticket. It might be a spec to hand off and iterate on, a decision to lock before planning starts, or a change made in place like a data-structure migration. The map is domain-agnostic — engineering work, course content, whatever fits the shape.

Plan, don't do

Wayfinder is planning by default: each ticket resolves a decision, and the map is done when the way is clear — nothing left to decide before someone goes and does the thing. The pull to just do the work is usually the signal you've reached the edge of the map and it's time to hand off. An effort can override this in its Notes — carrying execution into the map itself — but absent that, produce decisions, not deliverables.

Refer by name

Every map and ticket is an issue, so it has a name — its title. In everything the human reads — narration, the map's Decisions-so-far — refer to it by that name, never by a bare id, number, or slug. A wall of #42, #43, #44 is illegible; names read at a glance. The id and URL don't vanish — a name wraps its link — but they ride inside the name, never stand in for it.

The Map

The map is a single issue on this repo's issue tracker, labelled wayfinder:map — the canonical artifact. Its tickets are child issues of the map.

The map is an index, not a store. It lists the decisions made and points at the tickets that hold their detail; a decision lives in exactly one place — its ticket — so the map never restates it, only gists it and links.

Where the map, its child tickets, blocking, and frontier queries physically live is tracker-specific. The issue tracker should have been provided to you — run /setup-matt-pocock-skills if not. Consult the tracker doc's "Wayfinding operations" section for how this repo expresses them. If no tracker has been provided, default to the local-markdown tracker.

The map body

The whole map at low resolution, loaded once per session. Open tickets are not listed — they are open child issues, found by query.

## Destination

<what reaching the end of this map looks like — the spec, decision, or change this effort is finding its way to. One or two lines; every session orients to it before choosing a ticket.>

## Notes

<domain; skills every session should consult; standing preferences for this effort>

## Decisions so far

<!-- the index — one line per closed ticket: enough to judge relevance, then zoom the link for the detail the ticket holds -->

- [<closed ticket title>](link) — <one-line gist of the answer>

## Not yet specified

<!-- see "Fog of war": in-scope fog you can't ticket yet; graduates as the frontier advances -->

## Out of scope

<!-- see "Out of scope": work ruled beyond the destination; closed, never graduates -->

Tickets

Each ticket is a child issue of the map; the tracker's issue id is its identity. Its body is the question, sized to one 100K token agent session:

## Question

<the decision or investigation this ticket resolves>

Each ticket carries a wayfinder:<type> label — one of research, prototype, grilling, task (see Ticket Types).

A session claims a ticket by assigning it to the dev driving the map, first, before any work, so concurrent sessions skip it. That assignee is the claim: an open, unassigned ticket is unclaimed.

Blocking uses the tracker's native dependency relationship — essential because it renders the frontier visually in the tracker's own UI, so the human sees what's takeable without opening the map. Only a tracker that lacks native blocking falls back to a body convention. A ticket is unblocked when every ticket blocking it is closed; the frontier is the open, unblocked, unclaimed children — the edge of the known.

The answer isn't part of the body — it's recorded on resolution (see Work through the map). Assets created while resolving a ticket are linked from the issue, not pasted in.

Ticket Types

Every ticket is either HITL — human in the loop, worked with a human who speaks for themselves — or AFK, driven by the agent alone. A HITL ticket only resolves through that live exchange; the agent never stands in for the human's side of it (a grilling agent that answers its own questions has broken this).

• Research (AFK): Reading documentation, third-party APIs, or local resources like knowledge bases. Creates a markdown summary as a linked asset. Use when knowledge outside the current working directory is required.
• Prototype (HITL): Raise the fidelity of the discussion by making a cheap, rough, concrete artifact to react to — an outline, a rough take, a stub, or UI/logic code via the /prototype skill. Links the prototype as an asset. Use when "how should it look" or "how should it behave" is the key question.
• Grilling (HITL): Conversation via the /grilling and /domain-modeling skills, one question at a time. The default case.
• Task (HITL or AFK): Manual work that must happen before a decision can be made — nothing to decide, prototype, or research, but the discussion is blocked until it's done. Signing up for a service so its API can be judged, provisioning access, moving data so its shape can be seen. This is the one type that does rather than decides — and it earns its place by unblocking a decision, not by delivering the destination. The agent drives it alone where it can (AFK); otherwise it hands the human a precise checklist (HITL). Resolved when the work is done; the answer records what was done and any resulting facts (credentials location, new URLs, row counts) later tickets depend on.

Fog of war

The map is deliberately incomplete: don't chart what you can't yet see. Beyond the live tickets lies the fog of war — the dim view of decisions and investigations you can tell are coming but can't yet pin down, because they hang on questions still open. Resolving a ticket clears the fog ahead of it, graduating whatever's now specifiable into fresh tickets — one at a time, until the way to the destination is clear and no tickets remain.

The map's Not yet specified section is where that dim view is written down: the suspected question, the area to revisit later. It's the undiscovered frontier toward the destination — everything here is in scope, just not sharp enough to ticket. Write as loosely or as fully as the view allows; it doubles as a signpost for collaborators reading where the effort is headed.

Fog or ticket? The test is whether you can state the question precisely now — not whether you can answer it now.

• Ticket when the question is already sharp — even if it's blocked and you can't act on it yet.
• Not yet specified when you can't yet phrase it that sharply. Don't pre-slice the fog into ticket-sized pieces: it's coarser than a ticket, and one patch may graduate into several tickets, or none, once the frontier reaches it.

Not yet specified excludes what's already decided (Decisions so far), what's already a live ticket, and what's out of scope (the next section).

Out of scope

Fog only ever gathers toward the destination. The destination fixes the scope, so work beyond it is out of scope — it isn't fog, and it doesn't belong in Not yet specified. It gets its own Out of scope section on the map: work you've consciously ruled out of this effort. Scope, not sharpness, lands it here.

Out-of-scope work never graduates — the frontier stops at the destination — so it returns only if the destination is redrawn, and then as a fresh effort, not a resumption.

Ruling something out of scope is a scoping act, not a step on the route. When a ticket that already exists turns out to sit past the destination — mis-scoped in while charting, or exposed by a resolution — close it (a closed ticket is unambiguously off the frontier) and leave one line in the Out of scope section: the gist plus why it's out of scope, linking the closed ticket. It stays out of Decisions so far, which records the route actually walked — a scope boundary isn't a step on it.

Invocation

Two modes. Either way, never resolve more than one ticket per session.

Chart the map

User invokes with a loose idea.

1. Name the destination. Run a /grilling and /domain-modeling session to pin down what this map is finding its way to — the spec, decision, or change. The destination fixes the scope, so it's settled first.
2. Map the frontier. Grill again, breadth-first this time: fan out across the whole space rather than deep on any one thread, surfacing the open decisions and the first steps takeable now. If this surfaces no fog — the way to the destination is already clear, the whole journey small enough for one session — you don't need a map. Stop and ask the user how they'd like to proceed.
3. Create the map (label wayfinder:map): Destination and Notes filled in, Decisions-so-far empty, the fog sketched into Not yet specified.
4. Create the tickets you can specify now as child issues of the map — then wire blocking edges in a second pass (issues need ids before they can reference each other). Wiring sorts them into the frontier and the blocked; everything you can't yet specify stays in the fog — the Not yet specified section.
5. Stop — charting the map is one session's work; do not also resolve tickets.

Work through the map

User invokes with a map (URL or number). A ticket is optional — without one, you pick the next decision, not the user.

1. Load the map — the low-res view, not every ticket body.
2. Choose the ticket. If the user named one, use it. Otherwise take the first frontier ticket in order. Claim it: assign it to yourself before any work.
3. Resolve it — zoom as needed: fetch the full body of any related or closed ticket on demand; invoke the skills the ## Notes block names. If in doubt, use /grilling and /domain-modeling.
4. Record the resolution: post the answer as a resolution comment, close the issue, and append a context pointer to the map's Decisions-so-far.
5. Add newly-surfaced tickets (create-then-wire); graduate any fog the answer has made specifiable, clearing each graduated patch from Not yet specified so it lives only as its new ticket. If the answer reveals a ticket — this one or another — sits beyond the destination, rule it out of scope rather than resolving it on the route. If the decision invalidates other parts of the map, update or delete those tickets.

The user may run unblocked tickets in parallel, so expect other sessions to be editing the tracker concurrently.

Wizard

A wizard is a bash script that walks a human, step by step, through a manual procedure that's tedious to do by hand and tedious to re-explain to an AI every time. It opens each URL, says exactly what to click and copy, captures the values, writes them where they belong (.env, GitHub secrets), confirms at every stage, and shows how much is left. It might configure third-party services, run a one-off migration, or move the project from one state to another.

The delightful UX is already solved by template.sh — progress with time-remaining, confirmation gates, cross-platform URL opening (including WSL), hidden secret entry, idempotent .env upserts, gh secret/gh variable writes, and a closing summary. Your job is only to scope the procedure and author its stages. The library above the STAGES marker is identical in every wizard; that consistency is the point — never hand-edit it.

A wizard is ephemeral by default — built for one run, saved to a scratch or scripts/ path, deleted when the job's done. Commit it only when the user wants a repeatable setup path that should live in the repo.

Process

1. Scope the procedure

Work out every manual step the human must take and every value that gets captured along the way. Read the repo first — don't ask cold:

• For setup: .env, .env.example, .env.*, README, docker-compose*, framework config, and .github/workflows/* (every secrets.* / vars.* reference is a value the wizard must produce).
• For a migration or transition: the current state, the target state, and the irreversible actions between them.

Then show the user the ordered list of stages and the values each produces, and confirm — they may add, drop, or reorder.

Done when: every stage is named in order, and for each captured value you know (a) where the human gets it, (b) where it's written (.env, a GitHub secret, both, or nowhere — some stages are pure actions), and (c) whether it's secret (hidden entry) or public.

2. Map each stage's journey

For each stage, write the precise path a human follows: which URL to open, what to do there, where a value is shown, which variable it fills — e.g. "Dashboard → Developers → API keys → Reveal test key → copy". Where you don't actually know the current UI or the exact command, say so and ask the user or check the docs — never invent steps that may not exist.

Done when: every stage traces to concrete instructions a stranger could follow.

3. Author the wizard

Copy template.sh to the target path. Replace the example stage with one stage per step, in dependency order. Use the library helpers — stage, say/step, open_url, ask/ask_secret, write_env, set_secret/set_var, pause/confirm — and set TOTAL_STAGES and TOTAL_MINUTES to honest estimates (this drives the time-remaining display).

Hold the bar the template sets: open the URL before asking for its value, use ask_secret for anything secret, write_env every persisted value, set_secret only the values CI actually needs, and confirm before any irreversible action. Each stage clears the screen so only the current step is visible — keep a stage to one focused task so nothing the human needs scrolls away. Don't touch the library above the marker.

4. Verify and hand off

• bash -n <script>; run shellcheck if available.
• chmod +x <script>.
• Don't run it end-to-end yourself — it opens browsers and blocks on human input. Trace it statically instead: every value from step 1 is captured and lands where step 1 said, and every set_secret name exactly matches a secrets.* reference in CI.
• Tell the user how to run it. If it's a repeatable setup path, commit it and link it from the README so the next person runs the script instead of asking an AI.

A skill exists to wrangle determinism out of a stochastic system. Predictability — the agent taking the same process every run, not producing the same output — is the root virtue; every lever below serves it.

Bold terms are defined in GLOSSARY.md; look them up there for the full meaning.

Invocation

Two choices, trading different costs:

• A model-invoked skill keeps a description, so the agent can fire it autonomously and other skills can reach it (you can still type its name too). It contributes to context load — the description sits in the window every turn. Mechanics: omit disable-model-invocation, and write a model-facing description with rich trigger phrasing ("Use when the user wants…, mentions…").
• A user-invoked skill strips the description from the agent's reach: only you, typing its name, can invoke it — and no other skill can. Zero context load, but it spends cognitive load: you are the index that must remember it exists. Mechanics: set disable-model-invocation: true; the description becomes human-facing — a one-line summary, trigger lists stripped.

Pick model-invocation only when the agent must reach the skill on its own, or another skill must. If it only ever fires by hand, make it user-invoked and pay no context load.

When user-invoked skills multiply past what you can remember, that piled-up cognitive load is cured by a router skill: one user-invoked skill that names the others and when to reach for each.

Writing the description

A model-invoked description does two jobs — state what the skill is, and list the branches that should trigger it. Every word increases context load, so a description earns even harder pruning than the body:

• Front-load the skill's leading word — the description is where it does its invocation work.
• One trigger per branch. Synonyms that rename a single branch are duplication — "build features using TDD … asks for test-first development" is one branch written twice. Collapse them; keep only genuinely distinct branches.
• Cut identity that's already in the body. Keep the description to triggers, plus any "when another skill needs…" reach clause.

Information hierarchy

A skill is built from two content types — steps and reference — that mix freely: a skill can be all steps, all reference, or both. The core decision is which to use and where each sits on the information hierarchy, a ladder ranked by how immediately the agent needs the material:

1. In-skill step — an ordered action in SKILL.md, the primary tier: what the agent does, in order. Each step ends on a completion criterion, the condition that tells the agent the work is done. Make it checkable (can the agent tell done from not-done?) and, where it matters, exhaustive ("every modified model accounted for", not "produce a change list") — a vague criterion invites premature completion.
2. In-skill reference — a definition, rule, or fact in SKILL.md, consulted on demand. Often a legitimately flat peer-set (every rule of a review on one rung) — a fine arrangement, not a smell. This skill is all reference.
3. External reference — reference pushed out of SKILL.md into a separate file, reached by a context pointer, loaded only when the pointer fires. (Spans disclosed reference — a sibling file like GLOSSARY.md, still part of the skill — through fully external reference that lives outside the skill system and any skill can point at.)

A demanding completion criterion drives thorough legwork — the digging the agent does within the work — whether the skill has steps or not, since "every rule applied" binds flat reference just as "every step done" binds a sequence.

Push too little down and the top bloats; push too much and you hide material the agent actually needs. That tension is the whole decision.

Progressive disclosure is the move down the ladder — out of SKILL.md into a linked file — so the top stays legible. Mechanics: a linked .md file in the skill folder, named for what it holds (this skill discloses its full definitions to GLOSSARY.md). Some skills are used in more than one way, and each distinct way is a branch — different runs taking different paths through the skill. Branching is the cleanest disclosure test: inline what every branch needs, and push behind a pointer what only some branches reach. A context pointer's wording, not its target, decides when and how reliably the agent reaches the material.

Where the ladder decides how far down a piece sits, co-location decides what sits beside it once there: keep a concept's definition, rules, and caveats under one heading rather than scattered, so reading one part brings its neighbours with it.

When to split

Granularity is how finely you divide skills, and each cut spends one of the two loads, so split only when the cut earns it. Two cuts:

• By invocation — split off a model-invoked skill when you have a distinct leading word that should trigger it on its own, or another skill must reach it. You pay context load for the new always-loaded description, so that independent reach has to be worth it.
• By sequence — split a run of steps when the steps still ahead (a step's post-completion steps) tempt the agent to rush the one in front of it (premature completion). Keeping them out of view encourages the agent to do more legwork on the current task.

Pruning

Keep each meaning in a single source of truth: one authoritative place, so changing the behaviour is a one-place edit.

Check every line for relevance: does it still bear on what the skill does?

Then hunt no-ops sentence by sentence, not just line by line: run the no-op test on each sentence in isolation, and when one fails, delete the whole sentence rather than trim words from it. Be aggressive — most prose that fails should go, not be rewritten.

Leading words

A leading word is a compact concept already living in the model's pretraining that the agent thinks with while running the skill (e.g. lesson, fog of war, tracer bullets). Repeated throughout the text (though not necessarily - a strong leading word might only be needed once), it accumulates a distributed definition and anchors a whole region of behaviour in the fewest tokens, by recruiting priors the model already holds.

It serves predictability twice. In the body it anchors execution: the agent reaches for the same behaviour every time the word appears. In the description it anchors invocation: when the same word lives in your prompts, docs, and code, the agent links that shared language to the skill and fires it more reliably.

Hunt for opportunities to refactor skills to use leading words. A triad spelled out at three sites (duplication), a description spending a sentence to gesture at one idea — each is a passage begging to collapse into a single token. Examples include:

• "fast, deterministic, low-overhead" -> tight — one quality restated across a phase — into a single pretrained word (a tight loop).
• "a loop you believe in" -> red — converts a fuzzy gate into a binary observable state (the loop goes red on the bug, or it doesn't).

You win twice over: fewer tokens, and a sharper hook for the agent to hang its thinking on. Assume every skill is carrying restatements that leading words retire — go find them.

Failure modes

Use these to diagnose issues the user may be having with the skill.

• Premature completion — ending a step before it's genuinely done, attention slipping to being done. Defence, in order: sharpen the completion criterion first (cheap, local); only if it is irreducibly fuzzy and you observe the rush, hide the post-completion steps by splitting (the sequence cut).
• Duplication — the same meaning in more than one place. Costs maintenance and tokens, and inflates a meaning's prominence on the ladder past its real rank.
• Sediment — stale layers that settle because adding feels safe and removing feels risky. The default fate of any skill without a pruning discipline.
• Sprawl — a skill simply too long, even when every line is live and unique. Hurts readability and maintainability and wastes tokens. The cure is the ladder: disclose reference behind pointers, and split by branch or sequence so each path carries only what it needs.
• No-op — a line the model already obeys by default, so you pay load to say nothing. The test: does it change behaviour versus the default? A weak leading word (be thorough when the agent is already thorough-ish) is a no-op; the fix is a stronger word (relentless), not a different technique.
• Negation — steering by prohibition backfires: don't think of an elephant names the elephant and makes it more available, not less. Prompt the positive — state the target behaviour so the banned one is never spoken; keep a prohibition only as a hard guardrail you can't phrase positively, and even then pair it with what to do instead.

remocn

Copy-paste components for Remotion videos. Components install via shadcn and land in components/remocn/ — you own the code.

Installation

Prerequisites: a Remotion project (npx create-video@latest).

# Add any component (namespaced shadcn registry)
shadcn add @remocn/blur-out-up

# Component lands at components/remocn/blur-out-up.tsx

@remocn/<name> is the canonical namespaced form (configured under registries in components.json). The plain registry URL https://remocn.dev/r/<name>.json also works.

Dependencies install automatically

Many components pull others via registryDependencies — shadcn installs them transitively. For example, shadcn add @remocn/typewriter also pulls @remocn/remocn-ui and @remocn/caret.

• @remocn/remocn-ui is the shared core lib (timeline-fold hook, theme context, color math). Most UI Primitives depend on it. You rarely install it directly.

Two tiers

remocn has two kinds of components — they have different APIs:

• Animation tier (remocn) — text animations, transitions, backgrounds, UI-block sims, brand/social cards, full compositions. Frame-driven. Shared props: speed (time multiplier), and for text: fontSize, color, fontWeight.
• UI Primitives (remocn-ui) — timeline-driven shadcn-style primitives (button, dialog, select, command-menu, tooltip…). State-based props (state, style, variant, theme). No speed prop. Built on @remocn/remocn-ui.

Component categories

Pick by what you're building. The catalog is split one file per component under references/components/. Start at references/components/index.md — a router table grouped by these categories with a Use for / Avoid for signal per component. Scan it, pick candidates, then open only the references/components/<name>.md files you need (full props, example, all use / don't-use notes). Don't read every file.

Component patterns

Conventions differ by tier — don't assume animation-tier props on a primitive.

Animation tier (remocn)

• Named Props interface per component (e.g. BlurOutUpProps).
• speed?: number — global time multiplier (default 1), applied as frame * speed.
• Text components: fontSize, color, fontWeight.
• Transitions: lowercase factories (e.g. whipPan(props)) returning a TransitionPresentation — pass to TransitionSeries.Transition via presentation, pace with linearTiming / springTiming.
• className?: string on the root.

UI Primitives (remocn-ui)

• State-based, not speed-based: state (e.g. "open" / "closed"), style, variant, size, theme?: Partial<RemocnTheme>.
• The opened/closed/active state is a pure function of the timeline (keyframed presets).
• Compose modal-layer primitives (dialog, alert-dialog, drawer) with a trigger element — see each component's example.

Animation API

import { interpolate, spring, useCurrentFrame, useVideoConfig } from "remotion";

const opacity = interpolate(frame, [0, 30], [0, 1], { extrapolateRight: "clamp" });
const scale = spring({ fps, frame, config: { damping: 12, mass: 1, stiffness: 100 } });

// Deterministic randomness (NEVER Math.random())
import { random } from "@remotion/random";
const jitter = random(`seed-${frame}`);

Composition structure

import { Sequence, Series } from "remotion";

<Sequence from={30} durationInFrames={60}>
  <Typewriter text="npm install remocn" />
</Sequence>

<Series>
  <Series.Sequence durationInFrames={60}><SceneA /></Series.Sequence>
  <Series.Sequence durationInFrames={60}><SceneB /></Series.Sequence>
</Series>

Canvas & timing

• Canvas standard: 1280×720 @ 30fps. Components are laid out for it.
• Budget each Sequence around the component's natural length — the Length column in components/index.md (and each file's Natural length). Under-budgeting clips the animation; over-budgeting leaves dead air.
• Tone matching: each catalog entry carries a vibe tag (tech/premium/data/clean/ playful/social) — pick components whose vibe fits the brand.
• Palette & fonts: stay within the library's tokens (references/design.md → tokens) so your own elements don't clash.

Design defaults — avoid AI-slop

Your own additions (text, scene chrome, cards — not the prebuilt components) stay restrained: default tracking, sentence case, solid text color, subtle 1px elevation — no decorative letter-spacing, ALL-CAPS, gradient text-fills, or glow shadows. Never strip these traits from a component whose essence is the effect (tracking-in, social-card gradients, designed elevation).

Full do/avoid examples + design tokens: references/design.md. Motion quality (timing, anticipation, staging, easing): references/motion-principles.md.

Gotchas (remocn-specific)

• Terminal scroll is instant — step-function translateY, never spring/ease the scroll.
• overflow: hidden on split layouts — prevents content breakage during width animations.
• Cursor blink is deterministic — Math.floor(frame / 15) % 2 === 0, not intervals.
• Static files go in public/ — load via staticFile('cursor.svg'), not imports.
• Social cards render offline — avatarUrl="" / coverUrl="" fall back to gradients; no fetch.

General Remotion rules (no Math.random(), no setInterval, animate transform not top/left, load fonts before render) live in the remotion-best-practices skill.

Composing a video

Don't dump components — compose one story. When asked to build a full video ("make a product demo", "changelog video", "intro for my landing"):

1. Decide the strategy — ready template vs compose from components vs build a new component. See references/anatomy.md §1.
2. Follow the beats — a product demo is Hook → Positioning → Product reveal → Features → Proof → CTA (last two optional). See references/anatomy.md §2.
3. Use the recipe — references/archetypes/index.md routes to per-archetype builds: content contract (infer → ask → placeholder), duration variants, beat→component slots, and a worked <TransitionSeries> skeleton.
4. Pick each beat's component from references/components/index.md; match the vibe tag to the brand and budget its Sequence per Canvas & timing above.
5. Check the quality bar — one accent, sentence-case kinetic type, real content, no glow halos, no feature-list enumeration. See references/anatomy.md §3.

Reference

• references/anatomy.md — composing a full video: strategy (template/compose/new), the product-demo beats, and the good-vs-slop quality bar.
• references/archetypes/index.md — router to per-archetype build recipes (product-demo flagship + changelog, feature-announcement, oss-showcase, cli-tool-demo, testimonial-reel, year-in-review, pricing-reveal, logo-bumper): content contract, duration variants, beat→slot map.
• references/components/index.md — router table (all components, grouped by category, with Use for / Avoid for). Open references/components/<name>.md for one component's full props, example, and use / don't-use notes.
• references/design.md — anti-slop design defaults (do/avoid) + design tokens (palette, fonts, canvas).
• references/motion-principles.md — motion-design principles adapted to remocn + Remotion.
• references/anti-patterns.md — common generation mistakes and their fixes.

Run a /grilling session.

Run a /grilling session, using the /domain-modeling skill.

Run a /grilling session.

Run a /grilling session, using the /domain-modeling skill.