define-loop — author a Loop

A Loop is a cron with a memory and a gate: a trigger, a playbook, a ledger of work items that survives across fires, ship-ready outputs held for a person, and a governor watching its health. Reach for one when the ask is "keep doing this kind of work", not "do this".

Intake, work, and judge turns use the loop's existing owner/scope authorization, credentials, command policy, and security posture. They run without surface tools or addressed delivery. Intake reads candidates; work prepares outputs; the judge inspects the work and executes the declared checks. Final external actions belong in the approved ship stage. The review gate controls loop output shipping; it is not a read-only sandbox for arbitrary shell commands or API calls in earlier stages.

A cron is enough when each fire is independent and nothing needs reviewing. Prefer the cron; a loop earns its complexity only when work items persist, must not be worked twice, and produce artifacts someone should see before they go out.

The one rule: never arm a loop you have not watched work. Stage 2 is not optional.

Stage 1 — Interview

Get these, in the user's own words. Ask about them one or two at a time, not as a form.

Writing the success condition

Free text, and it's the most load-bearing sentence in the loop. A good one has:

• one measurable end state — a test result, a linked PR, an empty queue
• a stated check — how the agent proves it ("npm test exits 0", "the PR shows CI green")
• the constraints that matter — what must not change on the way ("touches only the implicated module")
• a bound — "or park with a diagnosis after 5 turns", so a hopeless item stops burning money

The Sentry issue has a linked PR whose tests pass and CI is green, the fix touches only the implicated module, or park with a diagnosis after 5 turns.

Add successChecks for anything a command can settle (npm test, npm run lint). The judge runs each check and reports its command and result. A missing or failed reported check prevents completion even when the judge claims success. Check results are model-reported, not an independent deterministic execution attestation.

Stage 2 — Shadow run (never skip)

Work one real item end to end, in this conversation, with the user watching. No loop record exists yet.

1. Pull one real candidate from the source.
2. Do the whole job to the ship line — write the actual PR or the actual reply — and stop.
3. Show the user the finished artifact. Ask directly: would you have sent this?
4. Fix what they flag. Repeat on a second item if the first needed real correction.

Then write the playbook from what you just did — the steps that actually worked, the dead ends worth skipping, the exact success condition you'd have wanted. A playbook written before the shadow run is a guess; one written after is a procedure.

Stage 3 — Emit

Create the loop with the loop tool: name, purpose, playbook, success condition, checks, ship actions, caps, trigger. Every ship action starts at hold no matter what was asked for — auto is earned in stage 4, never granted at creation.

State back to the user, in one message: what it will do, when it fires, what it will produce, what it will hold for them, and how to pause it.

Stage 4 — Supervised fires

Watch the first few fires with the user.

• Review the held outputs together the first time.
• When a slice of work has been shipped unchanged several times running, offer graduation: "always ship open_pr labelled lint-fix from this loop". That writes a standing grant and flips only that slice to auto. If the org disables always-grants, graduation is unavailable — say so plainly instead of half-doing it.
• If outputs keep coming back returned, the playbook is wrong, not the reviewer. Fix the playbook; a high return rate is the loudest signal a loop isn't working.

Editing a live loop

Playbook edits are versioned with provenance, from chat or the UI — same path, one history. "Skip payments-service tickets from now on" is a playbook edit, so make it one: propose the diff, get agreement, write it. Never let a loop carry behavior that isn't readable in its playbook.

Refuse to arm

Say no, and say why, when:

• the user can't state a stable per-item id (work will repeat)
• the success condition has no measurable end state (nothing can judge it)
• auto is requested at creation on an externally-visible action (that's stage 4's job)
• the shadow run never produced an artifact the user would actually send