dart-ultrawork

Use this skill in Codex to run the DART dart-ultrawork workflow. The editable workflow source lives in .claude/commands/; this file is its generated adapter in the shared .agents/skills/ catalog.

Invocation

• Claude Code/OpenCode: /dart-ultrawork <arguments>
• Codex: $dart-ultrawork <arguments>

Treat the text after the skill name as $ARGUMENTS. When the workflow references $1, $2, etc., map those to the positional values supplied by the user.

Command Body

Start a team-scale or autonomous DART task: $ARGUMENTS

Required Reading

@AGENTS.md @docs/ai/principles.md @docs/ai/north-star.md @docs/ai/orchestration.md @docs/ai/sessions.md @docs/ai/verification.md @docs/dev_tasks/README.md

Load additional owners only when the matching phase needs them:

• placement or cleanup: docs/information-architecture.md;
• numbered-plan selection or packet state: docs/plans/dashboard.md;
• version control, changelog, tools, or review: docs/onboarding/{contributing,changelog,ai-tools}.md.

Arguments

$ARGUMENTS is a task brief plus optional mode flags:

• mode=interview: ask one up-front batch of critical questions.
• mode=brief: treat provided context as sufficient unless escalation applies.
• mode=resume: start from the existing docs/dev_tasks/<task>/ project home and run the session-start protocol before changing files.
• interview=skip: skip maintainer questions only when the brief already answers all consequential decisions.

The brief may be prose or a structured TASK / CONTEXT block. Extract north star, deliverable, acceptance criteria, constraints, risks, references, paths, issues/PRs/branches, commands, and first step when present.

Workflow

You are the orchestrator, supervisor, and steerer for the entire task: you decompose, delegate, review, and keep evidence honest. Workers implement. Follow the orchestrator/executor contract and packet sizing rules in docs/ai/orchestration.md. Use dart-new-task instead when the work is a bounded single-session task and the user did not ask for autonomous project handling.

1. Session start and current reality - Locate the project home. For DART autonomous projects this is docs/dev_tasks/<task>/, not a parallel project directory. If it exists, read its README.md, RESUME.md, and any autonomous sidecars such as decisions.md, verification.md, or progress-log.md; then verify checkout state, current branch, and any branch/PR evidence named by the docs. Run pixi run ai-doctor when setup, discovery, instruction, agent, or hook state is uncertain. If the docs are stale, update the handoff/current-reality note before relying on them. If no project home exists and the task is multi-session, team-scale, design-heavy, risky, or explicitly autonomous, create docs/dev_tasks/<task>/ before implementation. If prior work exists elsewhere, first absorb or summarize it into the project home.
2. Understand and scout - Restate the north star, final deliverable, acceptance criteria, quality bar, non-goals, constraints, risks, and target branch line (DART 7 main, DART 6 LTS, or both). Scout the territory first with named docs/code, read-only searches, a dart-analyze pass, the Codex dart_scout profile, or focused reference review; draft a candidate decomposition privately before asking anything.
3. Interview decisions; self-resolve uncertainties - Ask at most one up-front batch of critical questions. Escalate before destructive operations, history rewrites, irreversible migrations, meaningful cost, security/credential/secret handling, legal or privacy-sensitive decisions, major product-direction choices not covered by the brief, conflicts with stated constraints, or any assumption whose wrong answer could cause significant harm. If input is unavailable, choose the safest reversible path, document the assumption, and continue only with non-blocked work. Then split consequential unknowns:
   • Maintainer decisions: preference, scope, public API, release, quality-bar, or roadmap calls that evidence cannot settle. Ask the human now in one batched interview (focused questions with 2-4 concrete options each, recommendation first). Do not start large work while a consequential decision is open. Skip this discretionary interview when mode=brief; also skip when interview=skip and the prompt already answers everything consequential. In both cases, still follow the escalation rules above.
   • Evidence-resolvable uncertainties: anything a focused A/B test, benchmark, throwaway spike, reference lookup, or blind-spot review can settle. Do not ask the human; schedule these as spike/research packets and record the method and result as evidence (see "Discovering unknowns before committing" in docs/ai/orchestration.md).
4. Create or refresh the tracking surface - Populate the project home with value, north star, deliverable, scope, non-goals, assumptions, risks, acceptance evidence, gates, dependencies, milestone, next actions, and blockers. Claim-dependent physics/simulation, collision/contact, model, GUI, and visual-example work routes through dart-verify-sim: require a text oracle plus assessed visual/debug evidence, or a justified replacement. Keep RESUME.md as the handoff; add decisions.md, verification.md, and progress-log.md sidecars when they improve resumability or evidence.
5. Set the goal contract - Express done-when as verifiable outcomes (files, tests, gates, artifacts). When the session supports a goal or stop-hook mode (for example /goal in Claude Code), set it to this contract so orchestration cannot stop early or loop forever. Stop once the acceptance criteria are satisfied, verification is recorded, docs are current, known gaps are documented, and unnecessary work has been removed or deferred. Every delegated packet gets its own contract: GOAL (one sentence), DONE WHEN (verifiable), EVIDENCE (what to record), RISKS, and NEXT STEP.
6. Decompose and route to workers - Cut work packets per docs/ai/orchestration.md, then route by docs/ai/README.md. For Codex, use dart_scout for read-only territory mapping, built-in workers or dart-execute-packet for bounded implementation, dart_reviewer for independent acceptance review, and dart_release_auditor for branch adaptation. Without team tooling, execute packets sequentially. Use parallel writers only when file ownership is explicit and disjoint.
7. Run the autonomous work/review cycle - For each meaningful chunk: plan, execute, verify, then run an independent/specialized review lane. Treat review findings as hypotheses: investigate, fix or record no-fix evidence, clean up, re-verify, and re-review. A packet is not done until the current post-fix state has at least two clean review passes recorded.
8. Supervise and steer - Monitor progress; unblock, reassign, or re-cut packets on scope mismatch. Workers return Task, Summary, Files changed, Evidence/tests, Risks, and Recommended next step. Use another tool, an independent session, or the bounded specialist profiles when available; fall back to role-separated local review only when unavailable. Root-cause failures and fold newly discovered unknowns back into step 3.
9. Update docs at each stopping point - Every meaningful cycle updates the project home: README.md for status/plan/risks, RESUME.md for the next fresh-session handoff, decisions.md if decisions changed, verification.md if checks ran or gaps were found, and progress-log.md for completed chunks when that sidecar exists. Keep docs current enough for a zero-context session to resume without hidden chat memory.
10. Version-control and closeout - Keep commits and PRs coherent: separate feature work, bug fixes, refactors, docs, experiments, and AI-infra changes when practical; review the diff, remove unrelated changes, make the changelog decision, and run pixi run lint before commits. Run task-specific gates from docs/ai/verification.md, record evidence per packet, and complete the principle audit. A project is complete only when the north star and acceptance criteria are met, verification evidence is recorded, docs are current, known gaps are documented, unnecessary work is removed or deferred, and final state is summarized in RESUME.md or a durable owner. Promote durable artifacts out of docs/dev_tasks/<task>/ and remove the folder in the completing PR. GitHub mutations (push, PR, comments, re-triggers) only with explicit maintainer/user approval.

Kickoff Prompt Template

Canonical fresh-session prompt. TASK, context, and Done when are per-task; reuse the Logistics block verbatim.

TASK: <one-sentence objective>

<Per-task context: constraints, quality bar, must/never rules, and pointers
to the docs, code, branches, or references that define the territory.>

Done when:
- <verifiable outcome: a file, test, gate, benchmark, or artifact>
- <verifiable outcome>

Logistics:
- Run /dart-ultrawork with this task. You are the orchestrator,
  supervisor, and steerer: decompose, delegate, review, and keep evidence
  honest.
- Interview first: ask the maintainer only the consequential decisions that
  evidence cannot settle; resolve everything else yourself and record the
  evidence.
- Use docs/dev_tasks/<task>/ as the project home. Keep README.md,
  RESUME.md, and any decisions.md / verification.md / progress-log.md
  sidecars current enough for a zero-context session to resume.
- Prioritize correctness over speed, but stop once acceptance criteria are
  satisfied, verification is recorded, docs are current, and known gaps are
  documented. Manage resources responsibly; avoid endless exploration and
  low-value polishing.
- Route well-defined implementation packets to bounded workers with GOAL /
  DONE WHEN / EVIDENCE. Use team mode only when available and file ownership
  can stay disjoint. Keep authoring and review separate; use the strongest
  available reasoning for critical decisions, hard failures, and research
  synthesis.
- Set goal mode to Done-when; use Claude `/goal Run /dart-ultrawork with:
  <real prompt>` or Codex `/goal $dart-ultrawork <real prompt>`. Treat Claude
  goal text beginning `ulw:` or `ultrawok:` as shorthand for the same canonical
  `/dart-ultrawork` workflow, not as separate capabilities.
- Read docs/ai/principles.md, docs/ai/north-star.md,
  docs/ai/orchestration.md, and docs/ai/sessions.md before starting.
- Review loop: use specialized reviewers when available, investigate findings,
  and require two clean passes on the current state before done.
- Verification first: task-specific gates and dart-verify-sim evidence when
  applicable; pixi run lint before commits; GitHub mutations only with approval.

Output

• Interview record, uncertainty-resolution evidence, and project-home path
• Packet list, routing, goal contracts, gates, and review-loop status
• Per-packet evidence, GUI/demo artifacts when relevant, and updated docs
• Principle audit, cleanup status, and approved external mutations