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Use when running a PRD end-to-end autonomously - catchup, plan, work, and a review-rework loop running consensus, blind, and doubt lenses every cycle. Triggers on "autopilot", "run autopilot", "autopilot status", "drain backlog".
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | run-autopilot |
| description | Use when running a PRD end-to-end autonomously - catchup, plan, work, and a review-rework loop running consensus, blind, and doubt lenses every cycle. Triggers on "autopilot", "run autopilot", "autopilot status", "drain backlog". |
| argument-hint | [<prd-filename> | status] |
Orchestrate the full PRD lifecycle: catchup → design → plan-tasks → work → review-rework loop (consensus + blind + doubt lenses, every cycle) → done.
Makes autonomous decisions backed by research (dependencies, recurring issues, API/schema changes when PRD-driven) and pauses only for critical security, requirements ambiguity, or blocking decisions.
Run all phases in sequence without stopping. After each phase completes, immediately update state and proceed to the next phase. Do not pause between phases, do not summarize progress, do not wait for user input - unless the phase explicitly says PAUSE or STOP. Completing a sub-skill invocation (/catchup, /plan-tasks, /work, /review-work-completion, etc.) is NOT a stopping point. It is an intermediate step. Continue.
/run-autopilot — auto-select PRD (wip first, then backlog), run full cycle/run-autopilot <prd-filename> — full cycle with specific PRD/run-autopilot status — print current dashboard, no action/run-autopilot review-batch — interactively review a finished batch's deferred itemsIf invoked with status, read dev/local/autopilot/state.json, print phase/cycle/task summary, and stop.
If invoked with review-batch, load the newest dev/local/autopilot/deferred/{batch_id}-deferred.json and run the "Batch-End Review" presentation (Phase 9) against it — chunked by PRD, wait for user decisions, execute "fix now" items. No state changes; the batch is already closed. Stop when all chunks are reviewed (or the user says stop).
All autopilot artifacts live under dev/local/autopilot/, organized by type:
dev/local/autopilot/
state.json # current cycle state
last-session.log # wrapper-teed output of the last headless session
reports/{batch_id}-report.md # batch audit report
deferred/{batch_id}-deferred.json # unresolved items across PRDs
State file: dev/local/autopilot/state.json — see references/state-schema.md for schema.
Create dev/local/autopilot/ and subdirectories if missing. Initialize state file at PRD selection. Update state at every phase transition. Autopilot also keeps a per-PRD decision audit log at dev/local/reviews/<prd-base>-audit.md, rendered once at Phase 9 finalize from the state.json decision arrays (autonomous_decisions, deferred_decisions, doubts). state.json is the single in-run source of truth — decisions are NOT mirrored to audit.md incrementally per decision. Each rendered entry carries a source label (autonomous, deferred, or doubt); cycle/phase context goes in the entry body so the Phase 9 decisions.md projection can filter autonomous entries by label. Entry format, the Phase 9 render procedure, and the projection live in references/audit-log-format.md.
Invariant: every state mutation that advances phase SHOULD also set next_phase to the same value. The three gates are build | review | done (plus paused). build is ONE session: selection, catchup, design, planning, and work all run under phase: "build" with no mid-build handoff. The review surface runs in its own fresh session; blind and doubt scrutiny are LENSES inside every review cycle (Blake and Bob in review-work-completion's roster), not separate phases — reviewers get isolated contexts by construction (subagent prompts, external CLIs). Legacy blind/doubt phase values in pre-00015 state files map to review on resume. Per-task implementor tiering inside /work is unchanged. The authoritative resume signal is phase + phases_completed; build sub-step skipping is by ARTIFACT (capsule freshness, design-doc-exists, tasks-exist, all-done), not by phases_completed membership. This resume decision (phase + phases_completed + artifact checks → next step) is encoded canonically in scripts/resume_target.py, which scripts/test_autopilot_resume.py imports — editing the resume logic there flips a test red rather than silently drifting from this prose.
Durable artifacts are the paper trail of a run; they outlive the batch and must survive every cleanup. Disposable artifacts are transient scaffolding. Cleanup — including the user CLAUDE.md "clean up temp files" mandate — defers to this contract: "temp" means the disposable list below and nothing else. Never delete a directory wholesale when it holds durable artifacts; delete disposables by name.
dev/local/prds/done/ and its PRDs, dev/local/reviews/ (per-cycle review files, blind/doubt reviews, and the <prd-base>-audit.md audit renders), dev/local/designs/ (per-PRD design docs), dev/local/autopilot/reports/ (batch reports), dev/local/autopilot/deferred/ (the {batch_id}-deferred.json records), dev/local/autopilot/loop-metrics.jsonl (the per-session loop metrics, accumulates across batches), and dev/local/project-capsule.md.dev/local/tmp/, dev/local/autopilot/last-session.log, dev/local/autopilot/pause-requested, dev/local/autopilot/state.json (at batch end only — in loop mode the wrapper archives it to reports/{batch_id}-state-final.json; see Phase 9), and dev/local/autopilot/replan-context.md.Batch end and any cleanup step enumerate the disposable list explicitly; they never rm a durable path or a directory that contains one. This is what keeps a completed PRD's review trail intact after the batch closes (the warden-00011 plugin repos lost autopilot/ and reviews/ to an over-broad cleanup).
When /run-autopilot is invoked and dev/local/autopilot/state.json exists with batch.completed_prds, this is a continuation after a session restart. Preserve batch.completed_prds (including batch.id) and proceed to Phase 0 to pick the next PRD.
Delete state.pause_reason from state.json if present — unconditional, on every invocation, NOT gated on batch.completed_prds. A new session means any pause is being resumed; pause_reason belongs only to an unresolved pause and is not overwritten by normal progression, so it must be cleared here or it halts the resumed PRD's next hand-off. Cap-pause detection is unaffected — it keys on cap_pause_reason, which this delete does not touch.
All interaction with a running autoclaude batch happens at session boundaries — the only safe point:
tail -f dev/local/autopilot/last-session.log — the wrapper tees the live event log of the running headless turn there.touch dev/local/autopilot/pause-requested — the wrapper consumes the marker at the next session boundary, notifies "paused by operator", and stops the loop with state intact./run-autopilot in a normal interactive session — resume-by-artifact reads the same state.json, and full interactive semantics (questions, PAUSEs) apply because $_AUTOPILOT_LOOP is unset. Restart autoclaude afterwards to go unattended again.claude --resume <session-id> (the id is in the init event at the top of last-session.log) reopens a finished headless conversation for questioning. Harmless by construction: sessions are disposable; state.json plus the artifacts are the only orchestration contract, so a resumed chat cannot fork the loop.A running headless turn is never interrupted except by the wrapper's wall-clock cap (_AUTOPILOT_SESSION_MAX, default 7200s).
tasks_total and tasks_completed are maintained solely by the update-pidash-tasks.py PostToolUse sync hook (registered on TaskUpdate in settings.json), which recomputes both from the state.tasks snapshot on every TaskUpdate — tasks_total = len(tasks), tasks_completed = count(status == "completed"). The model does NOT query TaskList to mirror counts at each state update; that ceremony is gone. Keep state.tasks accurate at phase transitions (the snapshot the hook reads) and the counts follow automatically, keeping the dashboard progress bar live.
TaskList is per-session storage (~/.claude/tasks/<session-id>/). Every fresh autopilot session — handoff to a review surface, restart after a context-cap rotation, or any manual re-invocation — starts with an empty TaskList even when state.tasks carries the full snapshot from the prior session. Phase skipping (planning, work) and per-task model dispatch both rely on TaskList, so without rehydration the new session operates with no task tracker at all.
Run this hydration before any phase invokes /work or queries TaskList for routing — specifically: Phase 2 (before the skip-rule check), Phase 3 (before /work), Phase 6 (before rework /work).
Load if empty. Query TaskList. If it returns any tasks, no-op (already populated this session). Otherwise read state.tasks from dev/local/autopilot/state.json; if absent or empty, no-op (nothing to hydrate). Otherwise, for each entry in declared order (do NOT reorder), TaskCreate(subject: name), passing model / estimated_tokens / est_context_peak / attempts / qwen_eligible / qwen_excluded_reason straight through as metadata when present — /work reads metadata.model (PRD 00025) and metadata.qwen_eligible (PRD 00031/00019); the rest keep the round-trip lossless. TaskCreate assigns ids sequentially from 1, aligning with state.tasks[].id by construction. Then TaskUpdate(status: ...) each entry to its recorded status (in_progress / completed; pending is the TaskCreate default, skip it). The attempts array round-trips as-is — the hydration never inspects its rows, so any per-attempt field (implementor, preflight_outcome, self_deslop, future fields) survives the snapshot → hydration → TaskGet cycle intact.
Idempotency: if a phase re-enters this sub-step on the same session (e.g. Phase 6 after Phase 3), the TaskList-non-empty check short-circuits. Safe to call as a precondition on every /work entry point.
The user can run pidash (from buvis-gems) in a separate terminal pane to watch progress in real time. It watches dev/local/autopilot/state.json automatically — no action needed from autopilot beyond keeping the state file updated.
Print a banner at each phase transition:
── AUTOPILOT ── PRD: {prd-name} ── Phase: {PHASE} ──────────────────
── AUTOPILOT ── PRD: {prd-name} ── Phase: REVIEW ── Cycle {n} ─────
── AUTOPILOT ── PAUSED ── {n} issue(s) need your decision ──────────
── AUTOPILOT ── PRD: {prd-name} ── DONE ── {n} cycles ─────────────
Before anything else — before the abort handlers and before PRD selection — create every directory the run will move files into. Idempotent, so it is safe on every invocation (fresh repo, resume, or mid-batch). Run it as its own Bash call:
mkdir -p dev/local/prds/backlog dev/local/prds/wip dev/local/prds/done dev/local/prds/stalled dev/local/reviews dev/local/tmp dev/local/autopilot/reports dev/local/autopilot/deferred
This guarantees the very first mv (backlog -> wip below, and later wip -> done at Phase 9, wip -> stalled in recovery) always has a destination directory, so a move can never silently mis-place a PRD for want of a target folder. mv does not create destinations; without this step a move into a missing dir renames the PRD to a stray file and the run continues unaware (the warden-00011 failure mode).
Before anything else, read dev/local/autopilot/state.json and check stall_reason:
stall_reason.stalled is "subagent_prompt_overrun" — the previous session's work aborted from a hook. The PRD is not broken; one task was scoped too big. Follow references/recovery.md → "Work-phase abort: replan procedure", then STOP (the next session re-enters build at planning). This is the one surviving replan path.stall_reason.stalled is "escalation_exhausted" — Phase 6 owns this inline; seeing it at Phase 0 means a crash landed mid-stall-move. Follow references/recovery.md → "Crash recovery: escalation_exhausted seen at Phase 0", then fall through to Normal PRD selection.state.phase == "paused" AND state.cap_pause_reason is set (the previous session's Phase 5 cap-pause behavior fired). The capped PRD is still in dev/local/prds/wip/; do NOT treat it as fresh PRD selection. Follow references/recovery.md → "Cap-Pause Resume Handler" — it presents the recorded unresolved findings and cycle count via the AskUserQuestion tool and branches on resume/abandon.state.cap_rotations has a new entry but none of the above holds — the previous session hit the Work-turn context cap and the cap hook rotated to a fresh session. The cap hook recorded the rotation (appended cap_rotations, reset the in-flight task to pending, set next_phase: "build"); that session then ended its turn and the loop wrapper relaunched on the non-empty next_phase. NOT a replan. A cap_rotations entry is informational only and needs no special handling here: fall through to Normal PRD selection, which resumes build by artifact (capsule fresh → skip catchup; tasks exist → skip planning; /work continues at the first non-completed task — the rotated task, now reset to pending).stall_reason value nor the cap-pause condition phase == "paused" + cap_pause_reason) — continue with Normal PRD selection below.dev/local/prds/wip/ or dev/local/prds/backlog/. If found in backlog, mv to wip/; then verify the move: confirm the PRD file now exists in dev/local/prds/wip/. If it does not, the mv failed — set state.phase = "paused" and state.next_phase = "paused", write state.pause_reason = {"site": "mv_verify", "detail": "<source, destination, and the mv error>"}, PAUSE naming the source, the destination, and the mv error; do not continue.dev/local/prds/wip/:
00XXX- prefix), announce
b. If wip is empty, check dev/local/prds/backlog/:mv to wip/; then verify the move: confirm the PRD now exists in dev/local/prds/wip/, else set state.phase = "paused" and state.next_phase = "paused", write state.pause_reason = {"site": "mv_verify", "detail": "<source, destination, and the mv error>"}, and PAUSE naming the source, the destination, and the mv error (do not continue)batch in state file if not already present: id: "<yyyymmddHHMM>" (current timestamp), mode: "autopilot", completed_prds: []. Batch-identity rollover: if state.batch IS already present but the surviving state represents a genuinely closed batch — state.phase == "done" AND state.next_phase == "" (empty) (batch end ran but state.json was not deleted, e.g. an abnormal exit before the Stop hook fired) — do NOT inherit the dead batch's id: mint a FRESH batch.id (a new <yyyymmddHHMM> timestamp) and reset completed_prds: []. This is the fix for the stale-id reuse the forensics found, where a batch.id minted weeks earlier kept being inherited across genuinely separate batches. Both conditions are required: only the batch-end "No more PRDs" branch (Phase 9 step 10) sets next_phase: "", so the empty next_phase is what distinguishes a genuinely closed batch from a transient mid-PRD phase: "done" — Phase 9 step 2 sets phase: "done" with next_phase: "done" BEFORE the verified wip -> done move, so a move that fails and PAUSEs (or a crash in steps 3-9) leaves phase == "done" with next_phase == "done" (non-empty), which must NOT roll over (doing so would wipe the in-progress batch's completed_prds and mint a spurious id). A normal in-progress resume (phase is build/review — or a legacy blind/doubt, which maps to review — a paused handled by an abort handler above, or any phase == "done" whose next_phase is still non-empty) preserves batch.id unchanged.--- line, parse the YAML block between the opening --- and the next ---. Look for catchup:. Accepted values: run, skip, force. Anything else (other value, malformed YAML, missing frontmatter, absent catchup: field) → default to run. Write the resulting value to state.catchup_mode. Also look for rework_cap: in the same YAML block. Accepted values: positive integer (or a string that parses cleanly as a positive integer). Anything else (non-integer string, negative/zero, absent field, malformed YAML, missing frontmatter) → default to 3. Write the resulting integer to state.rework_cap. Also look for design: in the same YAML block. Accepted values: run, skip. Anything else (other value, malformed YAML, missing frontmatter, absent design: field) → default to run. Write the resulting value to state.design_mode. Also look for design_gate: in the same block: on an exact user match write "user" to state.design_gate; otherwise (absent field, any other value) leave state.design_gate absent. Also look for doubt_reviewer: in the same YAML block. Accepted values: codex, fable. Anything else (other value, malformed YAML, missing frontmatter, absent doubt_reviewer: field) → default to codex. Write the resulting value to state.doubt_reviewer (read by the review phase: fable adds Eve to the review batch as a fifth lens; codex runs the standard roster). Also look for pause_on_ambiguity: in the same YAML block (PRD 00017): an exact true → write state.pause_on_ambiguity = true; anything else (absent, other value, malformed) → treat as false (leave the field absent). In loop mode a true makes a requirements ambiguity STALL the PRD instead of being resolved by assumption — it never pauses the batch (see Phase 2). On a malformed-frontmatter fallback, log a one-line warning ("autopilot: PRD frontmatter malformed; defaulting catchup_mode=run, rework_cap=3, design_mode=run, doubt_reviewer=codex") and continue — never crash Phase 0 on a frontmatter problem. PRD frontmatter is the source of truth for catchup behavior; once Phase 0 has set catchup_mode, do not re-parse the PRD. Mode semantics: run honors the batch-cache check in Phase 1; skip bypasses catchup entirely; force ignores the batch cache and re-runs full catchup regardless of recency. The rework_cap value is consumed by the Phase 5 decision gate's cap check (out of scope here; that's a separate task).dev/local/project-capsule.md if it exists. This contains PRD progress and operational context from previous sessions. Use it to inform work in this session.batch field── AUTOPILOT ── PRD {n}: {prd-name} ─────────────────────────────
{n} = len(batch.completed_prds) + 1Skip if: the batch cache is fresh (the batch-cache freshness check below holds — the capsule is already current), OR state.catchup_mode == "skip". The skip is by ARTIFACT (capsule freshness), not phases_completed membership.
When skipped via catchup_mode == "skip": do not invoke /catchup. Set state.catchup_mode = "skipped" (so subsequent re-entries also skip), keep phase: "build" and next_phase: "build", and proceed to Phase 1.5 (Design).
Otherwise, decide between full catchup and delta refresh using the batch cache.
The capsule (dev/local/project-capsule.md) is the persisted output of catchup: invariants, architecture decisions, GitHub state, project memories. Subsequent phases and their subagents read the capsule when they need that context — not TaskList, not state.json. So between PRDs in the same batch on the same branch, the heavy gather phase of /catchup (full diff, blast radius, reverse deps, GitHub state) produces output that's already accurate; re-running it costs ~60-95s and ~50K tokens per PRD with no information gain.
state.batch.catchup_completed_at (ISO 8601) and state.batch.catchup_head_sha (current branch HEAD when last full catchup completed) record the cache. Skip the full catchup and run a delta refresh when ALL of the following hold:
state.catchup_mode != "force" — PRD frontmatter catchup: force overrides the cache.state.batch.catchup_completed_at is present AND less than 4 hours old.state.batch.catchup_head_sha matches the current git rev-parse HEAD on the active branch.If any condition fails → full catchup: invoke /catchup. After completion, write state.batch.catchup_completed_at = <now> and state.batch.catchup_head_sha = <current HEAD>. These fields persist across PRDs in the batch (Phase 9 step 10 preserves them).
If all conditions hold → delta refresh (no /catchup invocation):
dev/local/prds/wip/ (the active set has changed since last catchup; new PRDs may have entered, old ones moved to done/).dev/local/project-capsule.md with the current PRD list (use the same format Phase 9 step 8 uses). Leave Key Invariants, Architecture Decisions, Component Boundaries, GitHub State, Project Health, and Project Memories untouched — those reflect batch-stable knowledge.── AUTOPILOT ── catchup: delta refresh (cache <Xm> old, HEAD <sha7>) ──After either path completes, proceed to Phase 1.5 (Design). Stay on phase: "build" and next_phase: "build"; do NOT add anything to phases_completed (build sub-steps skip by artifact, not by membership).
---\ncatchup: skip\n--- → state.catchup_mode = "skip". Phase 1 records mode skipped and advances to planning (still phase: "build").---\ncatchup: force\n--- → state.catchup_mode = "force". Phase 1 ignores the batch cache and runs full /catchup.state.catchup_mode = "run". Phase 1 honors the batch cache (delta refresh when fresh, full catchup otherwise).---\ncatchup: invalid\n--- → state.catchup_mode = "run", warning logged.---\ncatchup\n--- (malformed YAML) → state.catchup_mode = "run", warning logged.---\nrework_cap: 5\n--- → state.rework_cap = 5. Phase 5 cap check allows 5 review cycles before pausing.---\nrework_cap: abc\n--- → state.rework_cap = 3, warning logged. Invalid value falls back to the default.rework_cap field → state.rework_cap = 3 (default). Phase 5 cap check allows 3 review cycles before pausing.---\ndoubt_reviewer: fable\n--- → state.doubt_reviewer = "fable".---\ndoubt_reviewer: opus\n--- → state.doubt_reviewer = "codex", warning logged. Unrecognized value falls back to the default.doubt_reviewer field → state.doubt_reviewer = "codex" (default), no warning (absence is not malformed).Between catchup (Phase 1) and planning (Phase 2), in the SAME build session. Design turns the PRD's requirements (the WHAT) into a reviewed implementation design doc (the HOW) before tasks are planned. This is a BUILD-GATE SUB-STEP: state.phase stays "build", there is no new phase enum value, no phases_completed entry, and no session handoff. The skip is by ARTIFACT (the design doc), exactly like catchup's capsule-freshness skip.
Let <prd-stem> = state.prd with its trailing .md removed. The design doc artifact path is dev/local/designs/<prd-stem>-design.md.
Skip if state.design_mode == "skip": do not invoke /design-solution. Set state.design_mode = "skipped", leave state.design_doc unset, and proceed to Phase 2.
Skip if the artifact already exists: when dev/local/designs/<prd-stem>-design.md is already on disk (a manual /design-solution run earlier, or a work-abort replan re-entering the build gate). Log a one-line reuse note (── AUTOPILOT ── design: reusing existing <prd-stem>-design.md ──), set state.design_doc to that path, then run the empty-review-log gate (defined below) on this artifact-reuse path — an existing doc from a manual or aborted run is exactly where a skipped review hides — and proceed to Phase 2 only if the gate passes; do NOT re-invoke the skill. This artifact-based skip is what lets work-abort replans reuse the design with no extra logic.
Otherwise, run design:
/design-solution with the wip PRD path (dev/local/prds/wip/<state.prd>).state.design_doc to the artifact path it printed (dev/local/designs/<prd-stem>-design.md). Log the design decision (chosen approach + any unresolved non-blockers from the doc's ## Review log) to state.autonomous_decisions under the existing audit label autonomous — do NOT add a design audit label (the audit-log label set is closed). Then run the empty-review-log gate (defined below) on this success path before proceeding to Phase 2.state.phase = "paused" and state.next_phase = "paused", write state.pause_reason = {"site": "sub_skill_fail", "detail": "design-solution failed with open findings"}, report the open findings, and do NOT proceed to planning.Empty-review-log gate (both continue paths — the success path above AND the artifact-reuse path). Before advancing to Phase 2 from either continue path, verify the design doc's ## Review log actually holds at least one reviewer dispatch summary line: a silently-skipped review leaves it empty, and nothing else checks that the review ran. design_mode == "skip" bypasses the empty-review-log gate entirely (no doc exists by design). Otherwise bind DESIGN_DOC to state.design_doc and run this exact section-scoped check (one awk, no pipe, exit-code based — it counts only pinned dispatch-summary lines that appear inside the ## Review log section, so the design doc's own example lines in ## Interfaces & contracts cannot false-pass it):
awk '/^## Review log/{f=1;next} /^## /{f=0} f && /dispatch [0-9]+ \((claude|codex|claude-fallback)\): cardinal-sin [0-9]+, blocker [0-9]+, non-blocker [0-9]+, question [0-9]+/{hit=1} END{exit !hit}' "$DESIGN_DOC"
## Review log — the review never ran) → treat as a sub-skill failure: set state.phase = "paused" and state.next_phase = "paused", write state.pause_reason = {"site": "sub_skill_fail", "detail": "design doc has empty ## Review log (review never ran)"}, and do NOT proceed to planning. Surface the remedy: delete the design doc and let Phase 1.5 regenerate it.The check is deterministic (the pinned awk above), NOT a model judgment.
Design gate (state.design_gate == "user"): after a successful design (or an artifact reuse), and only when state.design_gate == "user", PAUSE before planning — present the design doc summary plus any unresolved non-blockers from ## Review log via AskUserQuestion, and proceed to Phase 2 only after the user answers. This is a mid-turn AskUserQuestion PAUSE; it does not end the turn, so no phase change is needed.
After design completes (run, skipped, or reused), stay on phase: "build" and next_phase: "build"; do NOT add anything to phases_completed (build sub-steps skip by artifact, not by membership). Then proceed to Phase 2.
---\ndesign: skip\n--- → state.design_mode = "skip". Phase 1.5 records mode skipped and advances to planning (still phase: "build").design: field → state.design_mode = "run". Phase 1.5 invokes /design-solution unless the design doc already exists (then it reuses it).---\ndesign: invalid\n--- → state.design_mode = "run", warning logged.---\ndesign_gate: user\n--- → state.design_gate = "user". Phase 1.5 PAUSEs for user review after a successful design, before planning.design_gate field → state.design_gate absent; no gate pause.First, run the "Hydrate TaskList from state.tasks" sub-step (defined in State Management above). This is mandatory — the skip rule below depends on it. Initial planning has nothing to hydrate (no-ops on empty state.tasks) and replan clears state.tasks deliberately (also no-ops); the case the hydration covers is a resumed PRD whose prior session populated state.tasks and handed off (e.g., a context-cap rotation into a fresh session). Without the hydration, the skip rule below sees an empty TaskList and mistakenly re-runs /plan-tasks.
Skip if: TaskList returns any pending or completed tasks (tasks already exist). Evaluate this after the hydration step above completes. This skip is by ARTIFACT (tasks exist), not phases_completed membership.
Before invoking /plan-tasks, check for dev/local/autopilot/replan-context.md. If present, this is a replan triggered by a Phase 0 abort handler. Pass the file to /plan-tasks (see plan-tasks/SKILL.md "Replan mode") so it scopes to remaining work and uses the tighter ≤75K per-task budget. /plan-tasks deletes the file after successful planning.
If replan-context.md is absent, run /plan-tasks normally — first-pass planning for a fresh PRD.
Invoke /plan-tasks with the selected PRD.
PAUSE site - requirements clarification. When /plan-tasks pauses autopilot with a requirements-ambiguity or clarification question:
state.autonomous_decisions (label autonomous) so the Phase 9 audit render captures it. Do not write audit.md here.$_AUTOPILOT_LOOP set, PRD 00017): never end the batch on an ambiguity. Resolve it by the simplest safe assumption (the user's own global rule), record it in state.autonomous_decisions as {"type": "assumed-ambiguity", "question": ..., "assumption": ...}, AND mirror the same record into the batch deferred JSON so batch-end review shows it under "assumptions made". Exception: when the PRD frontmatter set pause_on_ambiguity: true (parsed at Phase 0 step 4), do not guess — stall the PRD instead (references/recovery.md → "Loop-mode stall procedure", site: "clarification") and continue the batch. A premise failure is never resolved by assumption either — it always stalls (see /work's premise gate when present)./plan-tasks exits non-zero with state.stall_reason.stalled == "oversized_task" when a task cannot be split below the per-task budget. When this happens, do NOT proceed to Phase 3 — follow references/recovery.md → "plan-tasks stall: oversized task" (deletes orphan tasks, moves the PRD to dev/local/prds/stalled/, advances to the next PRD).
Other outcomes from /plan-tasks:
stall_reason (or with a stall_reason.stalled value other than "oversized_task"): treat as a sub-skill failure. PAUSE and report the error per the "Sub-skill invocation fails" entry in the Error Handling table — also write state.pause_reason = {"site": "plan_tasks_fail", "detail": "<one-line error>"} alongside phase="paused". Do NOT proceed to Phase 3 or move the PRD.After completion, query TaskList and update state: stay on phase: "build" and next_phase: "build", write the tasks snapshot (see Phase 3 for format; the sync hook maintains tasks_total/tasks_completed). Do NOT add anything to phases_completed. Flow continues DIRECTLY into Phase 3 (work) in this same session — there is no planning→work handoff.
Skip if: All tasks completed, none pending. Evaluate after running the hydration sub-step (below) — otherwise a fresh session sees TaskList empty and mistakenly treats "no pending" as "all done". This skip is by ARTIFACT (all tasks done), not phases_completed membership. When all tasks are done, the build is complete → hand off to the review session (see below).
First, run the "Hydrate TaskList from state.tasks" sub-step (defined in State Management above). This is the critical entry point for the post-context-cap-rotation session path.
Before invoking /work, query TaskList and write the full tasks snapshot to dev/local/autopilot/state.json:
tasks: array of {"id": "<task-id>", "name": "<title>", "status": "pending|in_progress|completed", ...metadata} for EVERY task. The snapshot must preserve every field plan-tasks or Phase 6 may have written — at minimum: model (when set by plan-tasks tier classifier or Phase 6 escalation), attempts (the per-attempt log; see "Attempt logging" in /work), estimated_tokens and est_context_peak (when plan-tasks recorded a budget estimate), qwen_eligible and qwen_excluded_reason (when plan-tasks classified qwen eligibility — the Phase 9 Implementor Mix render reads them, PRD 00019). Stripping these on snapshot would break the hydration round-trip (subsequent sessions read them back into TaskList metadata) and lose Phase 6's tier-escalation history across the handoff. Treat the snapshot as merge-preserving over state.tasks[i], not a three-field replacement.tasks_total/tasks_completed are NOT written here — the update-pidash-tasks.py sync hook recomputes both from this tasks snapshot on every TaskUpdate (see "Task Counts" above).
Include the task id field — the update-pidash-tasks.py PostToolUse hook on TaskUpdate matches on it (via taskId) to sync status changes and recompute counts. This is mandatory.
Capture work_start_sha before dispatching /work, but only if it is unset for the current PRD (state.work_start_sha absent or empty). Run git rev-parse HEAD and write the resulting SHA to state.work_start_sha. If it is already set, do NOT re-capture — a cap-rotation (or any other build re-entry on resume) re-enters the build gate with pending tasks, and the existing value marks the true PRD start; re-capturing the HEAD-at-rotation would shrink the review diff (work_start_sha..HEAD) to post-rotation commits only. This bounds the commit range work_start_sha..HEAD that this PRD's /work dispatches produce — review-work-completion uses it as the full-review diff range, so the doubt lens sees the PRD's whole work range. Capture happens once per PRD (the unset guard enforces this across cap-rotations and resumes), before /work runs. Phase 9 step 10 clears work_start_sha on the PRD-to-PRD reset, so the guard is per-PRD correct: each PRD in a multi-PRD batch captures fresh, so ranges never overlap.
Capture repo_root in the same step. Run git rev-parse --show-toplevel in the work repo and write the absolute path to state.repo_root. Usually this equals the project root, but when the work repo is nested under a non-git project root (e.g. ~/.claude/skills/run-autopilot under ~/.claude), the review session needs it to run git (diff gathering, head_sha capture) in the right repo. Bare-repo-backed project root (the project root has no .git of its own because it is tracked by a bare repo with a separate work-tree, e.g. ~/.claude under the ~/.buvis bare repo with work-tree $HOME): a plain git rev-parse --show-toplevel from the project root FAILS, so do NOT default repo_root to the project dir (that silently mis-records it every PRD). Record the bare repo's work-tree root instead — git --git-dir=<bare-git-dir> --work-tree=<work-tree> rev-parse --show-toplevel (for ~/.buvis-backed $HOME this resolves to /Users/<you>).
Invoke /work skill. It runs until all tasks complete.
After completion, query TaskList again and update state: set phase: "review" and next_phase: "review", write the updated tasks snapshot (the sync hook maintains tasks_total/tasks_completed). Do NOT add anything to phases_completed here — the build gate leaves no membership marker; review-loop convergence is the only phases_completed entry.
After the build completes (all tasks done), do NOT continue into Phase 4 in the same session. The review phases (4, 7, 8) each spawn multiple cloud reviewers and need a clean context window. Hand off by ending the turn (the Session Loop contract):
state.next_phase to "review" (the phase the next session will run). The next session resumes at this phase.── AUTOPILOT ── PRD: {prd-name} ── Build complete ──────────────────
── AUTOPILOT ── handing off to fresh session for reviews ───────────
/review-work-completion, /review-blindly, or /review-with-doubt in this session, even if context budget appears sufficient.End the turn. In loop mode the session is headless (claude -p): the process exits at turn end, the wrapper reads state.json (non-empty next_phase: "review") and launches a fresh session, which resumes at Phase 4 and re-enters build only if an artifact is missing (capsule stale, no tasks, or tasks pending). Outside the loop, ending the turn leaves the session interactive and the same resume logic applies on the next manual invocation.
Skip the entire review-rework loop if: "review" is in phases_completed — the loop already converged in a prior session and handed off (see "Hand off to the finalize session" in Phase 5). Skip Phases 4, 5, and 6, and resume directly at Phase 9.
Skip this cycle's review if: A review file exists in dev/local/reviews/ for the current cycle (filename pattern {prd-name}-review-{cycle}.md).
Invoke /review-work-completion skill. Every cycle runs ALL lenses (its roster, PRD 00015): Alice (consensus), Blake (blind, PRD-only), Bob (doubt rubric R1-R5 + de-slop; Claude fallback when codex is down), Carl (UI, optional), Quinn (local qwen, advisory weight — unique findings create no tasks; optional, active only when the qwen preflight completion probe passes), plus Eve as a fifth lens when state.doubt_reviewer == "fable". The skill's consolidation records state.doubts_rubric_verdicts from Bob's rubric lines (replaced each cycle; the final cycle's verdicts are what Phase 9 renders).
After completion, stay on phase: "review" and next_phase: "review" (the decision gate is part of the review surface).
The cap is a gate on REWORK, not on Phase 5 itself. First, read the review output (see "Read the review output" further below). If it converged (no unresolved findings remain), the cap is irrelevant — proceed directly to Outcomes "No issues found" → finalize hand-off. The PRD success metric "passes review within three cycles is completely unaffected" requires this: a clean cycle-3 convergence at cap=3 must reach the finalize session, not cap-pause.
Otherwise (unresolved findings remain), before evaluating the Safety Checks table below, check whether the review-rework cycle cap has been reached.
Read state.cycle (starts at 1; the number of the review cycle just completed) and state.rework_cap (the effective cap, set by Phase 0 from PRD frontmatter — default 3; see Phase 0 step 4).
Rework is allowed while cycle < cap; when cycle >= cap AND rework would otherwise be dispatched, the gate pauses instead of reworking.
Worked example, cap 3:
1 < 3 → rework → cycle 2.2 < 3 → rework → cycle 3.3 >= 3 → pause, no 4th rework.Cap 5 yields five review cycles before the pause (cycles 1-4 → rework, cycle 5 → pause).
When the cap is hit AND the review did not converge (state.cycle >= state.rework_cap AND unresolved findings remain), branch on loop mode (PRD 00017):
$_AUTOPILOT_LOOP set) — cap-out defers, never pauses. Any unresolved CRITICAL finding → stall the PRD (follow references/recovery.md → "Loop-mode stall procedure", site: "cap_critical") and continue the batch. Otherwise (all unresolved findings ≤ high): append each to deferred_decisions as {"type": "cap-overflow", "issue": ..., "severity": ..., "consensus": ...}, proceed to the finalize hand-off as converged-with-deferrals, and make the banner name the deferral count (── cap reached: {k} findings deferred to batch end ──). Stop polishing, not the batch.When the cap is NOT hit (or the review converged with no findings), continue with the normal Outcomes flow below.
Executed only when the Cap check above fired on the INTERACTIVE branch (state.cycle >= state.rework_cap AND the review did not converge AND $_AUTOPILOT_LOOP is unset — loop mode defers/stalls instead, per the Cap check). This sub-section is the ONLY writer of the cap_pause_reason state field; it is a separate top-level field from stall_reason (which has different shapes — oversized_task, subagent_prompt_overrun, escalation_exhausted — and a different lifecycle).
Collect unresolved findings. Read the current review-cycle output (the same review file Phase 4 produced) and gather every finding that has not yet been resolved by an earlier cycle. Format each finding minimally — at least {"issue": <description>, "severity": <"critical"|"high"|"medium"|"low">, "consensus": <"N/M">} — additional fields are allowed.
Write cap_pause_reason to state.json. Merge (do NOT replace siblings) the field:
"cap_pause_reason": {
"cycle": <state.cycle>,
"cap": <state.rework_cap>,
"unresolved_findings": [ ... ]
}
Set state.phase and state.next_phase. Both become "paused". The Phase 0 Cap-Pause Resume Handler (a separate task) is what clears these on resume.
Best-effort dashboard hint (optional). MAY set state.needs_attention = true. This is a hint only — needs_attention is dashboard-only state cleared by the clear-pidash-attention.py PostToolUse hook on the next tool call, so it does NOT survive and MUST NOT be relied on as the pause indicator. The authoritative cap-pause signal is phase == "paused" PLUS cap_pause_reason being set (NOT needs_attention).
The pause halts the loop by state alone. Step 3 set state.phase = "paused"; the loop wrapper's decision table maps a paused state to "notify the user and stop the loop", leaving state.json intact. The user re-invokes /run-autopilot to handle the pause.
Print the cap-pause banner:
── AUTOPILOT ── PRD: {prd-name} ── CAP PAUSE (cycle {n}/{cap}) ─────
── {k} unresolved findings — see state.json cap_pause_reason ───────
── re-invoke /run-autopilot to resume or abandon ───────────────────
Substitute {prd-name} from state.prd (strip the .md extension), {n} from state.cycle, {cap} from state.rework_cap, and {k} from len(unresolved_findings).
STOP. Do NOT proceed to Phase 6. Do NOT continue into Classification, Outcomes, or the finalize hand-off sub-section. The paused state.json is the durable signal that this PRD is awaiting user action; the Phase 0 Cap-Pause Resume Handler picks it up on the next /run-autopilot invocation.
Read the review output. Categorize each finding using references/decision-framework.md.
| Condition | Action |
|---|---|
| >10 follow-up tasks from review | Interactive: PAUSE (scope alarm — ask user before proceeding). Loop mode (PRD 00017): keep the top 10 by severity, defer the rest to the batch deferred JSON as {"type": "scope-overflow", ...}, log one line, and continue — mirroring the doubt >5 overflow rule |
| Issue count not decreasing vs previous cycle | LOG and continue — a steady cycle is not a failure; the Phase 5 rework cap is the backstop |
| Same issue reappearing after previous fix | Route to research-then-decide Protocol B |
| A reviewer or sub-skill errored transiently during this review cycle | LOG and continue using the reviewers/sub-skills that succeeded (graceful degradation - e.g. a quota-exhausted reviewer is skipped, per the review skill). PAUSE only if the cycle cannot complete at all (no reviewer produced parseable output). A single transient error must not break an unattended run. |
Auto-fix (proceed without asking):
Research-then-decide (run protocol, then auto-fix or defer):
references/decision-framework.mdExecute the research protocol. If verdict is "proceed", treat as auto-fix. If verdict is "escalate", defer to batch end. Log with full research field in either case.
Defer to batch end (log, don't PAUSE):
PAUSE (present to user, block progress):
10 follow-up tasks from review (scope alarm)
Log every decision in the state file (autonomous_decisions or deferred_decisions); note the review cycle (state.cycle) in the entry's Decision text. The Phase 9 audit render reads these arrays — do not write audit.md here.
dev/local/autopilot/deferred/{batch_id}-deferred.json, proceed to Phase 6 with auto-fixable items onlyWhen the review-rework loop has converged (the "No issues found" outcome above), do NOT continue into Phase 9 in this session:
"review" to phases_completed — the marker Phase 4 reads to skip the whole review-rework loop on resume.phase and next_phase to "done". The next session runs Phase 9.── AUTOPILOT ── PRD: {prd-name} ── review-rework loop complete ─────
── AUTOPILOT ── handing off to finalize session ────────────────────
next_phase: "done" and launches a fresh session, which skips Phases 4-6 via the loop-level skip in Phase 4 and runs Phase 9. Outside the loop the same resume logic applies on the next manual invocation.Session model: Phase 6 runs in the same session as Phase 4 (the review surface). The per-task tier escalation in /work step 3 (dispatching each task as a separate Agent call at metadata.model) means the actual rework implementation runs at the escalated tier (haiku/sonnet/opus) regardless of the outer session. No separate rework handoff is needed: the review session handles review quality; per-task dispatch handles implementation correctness.
Two task kinds enter this phase:
[C{cycle}] prefix): a task /work already attempted that the review phase wants re-done. These are retries — escalate the model tier per the rule below.[D{cycle}] prefix): brand-new tasks created from decision gate resolutions. These are first-pass work, not retries — they default to sonnet (no escalation applies). Apply the /plan-tasks Tier classifier here too if you have the inputs (PRD slice, files-touched estimate); otherwise default sonnet.Both prefixes use the current cycle number. Both kinds dispatch through the same rework-mode /work invocation — see "Dispatch rework" below for how each gets its tier set and queued.
Run the "Hydrate TaskList from state.tasks" sub-step (defined in State Management above) BEFORE the "Escalate review-flagged tasks by tier" section's TaskUpdate calls. The rework session almost always inherits an empty TaskList from the post-Phase-3 handoff — running escalation TaskUpdate calls against an empty tracker either errors on unknown IDs or silently no-ops, losing the status→pending transition and dashboard visibility. Hydration is the first action of Phase 6, no exceptions.
Escalation caveat — diagnose the failure before escalating. The tier chain
haiku → sonnet → opus assumes a review failure means the model wasn't capable
enough. That is often wrong. A review failure caused by a spec-transmission
gap — the implementer built a self-consistent wrong thing because the task
description never carried the PRD's exact contract (field names, enum values,
hook kind, thresholds) — is not a capability failure. Escalating the tier costs
more and does not address the cause: a stronger model fed the same thin task
description can fail the same way. Before escalating, look at the cycle's review
findings. If they are predominantly spec-misread (wrong schema, wrong API,
missing feature, wrong artifact kind) rather than implementation-quality bugs
(edge cases, perf, logic errors), the real fix is a corrected task
description — and the review's follow-up tasks should already carry the exact
contract verbatim. In that case keep the same tier; do not escalate. Record
the decision and rationale in autonomous_decisions (the Phase 9 audit render reads it). Escalate the tier only
when the prior attempt genuinely struggled on a correctly-specified task. (Root
cause of the original gap: plan-tasks must copy the PRD's contract and
acceptance criteria verbatim into each task — see plan-tasks/SKILL.md step 4.)
For each review-flagged original-plan task in the current cycle's review output:
state.tasks[i].attempts[-1] — the last /work pass's entry, written by /work Attempt logging.state.tasks[i].attempts is empty or absent (legacy-plan task with no attempt log — covered by step 3's "no prior attempt" case and the closing paragraph after step 5), skip this step entirely and proceed to step 3's next-tier computation. Otherwise, rewrite that entry's outcome to "review_flagged" (it was "completed" when /work exited; review just flagged it).haiku → sonnet → opus:
state.tasks[i].attempts empty or absent — covers both pre-PRD-00025 legacy plans and PRD-00025 tasks that crashed before the first attempt log wrote) → treat as "sonnet"; next is "opus". Metric caveat: when this branch fires for a PRD-00025 task whose actual pass ran haiku, it inflates the apparent sonnet→opus escalation rate vs the PRD's ≤2% target. The branch is rare (crash before first attempt-log write) and the conservative jump-to-opus is the right correctness choice; just don't read sonnet→opus telemetry without accounting for it."haiku" → next is "sonnet""sonnet" → next is "opus""opus" → escalation exhausted: the haiku → sonnet → opus chain has no higher tier, so the task cannot be reworked automatically. Do NOT continue to step 4 — follow references/recovery.md → "Rework escalation exhausted" (rewrites the last attempt's outcome to "rework_failed", moves the PRD to dev/local/prds/stalled/, advances to the next PRD)./work and the state snapshot stay in sync, then queue the task for rework:
TaskUpdate(taskId="<id>", metadata={"model": "<next_tier>"}) — canonical source /work reads via TaskGet (see work/SKILL.md "Per-task model dispatch").state.tasks[i].model — the snapshot the dashboard and the next review cycle read.state.rework_task_ids (create the array if absent).state.tasks[i].status back to "pending" via TaskUpdate(taskId, status="pending") so /work will iterate it again. Reverse status transitions (completed → pending) are supported — the hydration sub-step (State Management above) relies on the same mechanism to restore completed status on a fresh session, and the PostToolUse status-sync hook treats whatever TaskUpdate writes as the new ground truth.The "no prior attempt" case in step 3 covers both pre-PRD-00025 legacy plans (which lack metadata.model and attempts[] entirely) and PRD-00025 tasks that crash before the first attempt log writes (rare but possible). Both are treated as "sonnet" for the next-tier computation, so first escalation goes to "opus".
Build the rework batch from two sources:
[C{cycle}] tasks — state.rework_task_ids already contains their IDs (appended in step 4 above), and their metadata.model + state.tasks[i].model already carry the escalated tier.[D{cycle}] follow-ups — for each new task created from a decision gate resolution:
/plan-tasks Tier classifier output if the inputs are available (PRD slice, files-touched estimate); otherwise default to sonnet. Then apply the PRD default_model floor the same way /plan-tasks step 4.7 does: final_tier = max(tier, default_model) — a PRD with default_model: opus must produce opus for every [D] follow-up, never a lower tier. Reading default_model at Phase 6 runtime: re-parse the PRD frontmatter from dev/local/prds/wip/<state.prd> using the same YAML-tolerant parse Phase 0 step 4 applies for catchup:. Look for default_model:. Accepted values: haiku, sonnet, opus. Behavior matches /plan-tasks step 4.7 exactly: absent frontmatter or unset default_model: → no override (silent; classifier output passes through); malformed YAML or invalid value → no override AND log a one-line warning, then classifier output passes through; valid value → apply final_tier = max(tier, default_model). default_model is intentionally NOT persisted to state — the PRD frontmatter is the single source of truth.TaskCreate(metadata={"model": final_tier, ...}).state.rework_task_ids AND insert a merge-preserving snapshot into state.tasks[] carrying {id, name, status, model} plus any classifier-produced fields (estimated_tokens, est_context_peak) — same merge-preserving rule Phase 3 establishes for the original-plan snapshot. The dashboard sees the new task and /work rework mode iterates it.Hydration already ran at the top of Phase 6 (see "Hydrate before any TaskUpdate" above) — the rework session inherits a populated TaskList by this point, so the TaskUpdate and TaskCreate calls operate on real tasks.
After both sources are merged into rework_task_ids, update state (the sync hook maintains the task counts). Invoke /work — it reads state.rework_task_ids and enters rework mode (see work/SKILL.md "Rework-mode task filter"), processing only the listed IDs at the tier each task carries in metadata.model; non-listed completed tasks are skipped.
The work skill may parallelize independent rework tasks when superpowers:dispatching-parallel-agents is available (see work skill's "Parallel dispatch for independent rework fixes").
state.rework_task_ids (set to []).state.cycle and persist it to state.json in this step (a durable write, not an in-memory bump). The Phase 5 cap-pause gate (state.cycle >= state.rework_cap) reads state.cycle; skipping the persisted increment blinds it. On warden 00020 the review loop ran cycles 1-3 but never persisted the bump, so state.cycle stayed 1 and cap-pause never fired. This write is not optional.phase: "review" and next_phase: "review". Do NOT rewrite state.tasks here — /work already wrote attempts[] entries directly to state.tasks during rework, and a bare TaskList snapshot would strip them; the sync hook keeps tasks_total/tasks_completed current.Cross-references: references/state-schema.md (rework_task_ids, tasks[].model, tasks[].attempts, stall_reason shapes); work/SKILL.md Per-task model dispatch, Attempt logging, Rework-mode task filter.
Commit history is left as-is. Autopilot does NOT rewrite the PRD's commit history. The former cherry-pick regroup engine never pushed — it only churned local history the user re-reviewed before pushing anyway — so it was pure risk (conflict aborts, backup branches) for no shipped benefit. The user squashes/groups commits manually before pushing.
Update state: set phase: "done" and next_phase: "done"
Move PRD from wip/ to done/ (use mv, keep 00XXX- prefix); then verify the move: confirm the PRD now exists in dev/local/prds/done/. If it does not, the mv failed — set state.phase = "paused" and state.next_phase = "paused", write state.pause_reason = {"site": "mv_verify", "detail": "<source, destination, and the mv error>"}, PAUSE naming the source, the destination, and the mv error, and do not continue (do not append to completed_prds or advance to the next PRD with the PRD in the wrong folder)
Append completed PRD to batch.completed_prds in state file
Delete all tasks from the completed PRD: query TaskList, mark every task as deleted via TaskUpdate. This prevents stale tasks from triggering Phase 2's skip logic on the next PRD.
Append items to dev/local/autopilot/deferred/{batch_id}-deferred.json (create if missing). Collect from the current state file:
deferred_decisions with status "pending" or "deferred" -> type "deferred_decision" (preserve original type field if present, e.g. "doubt-overflow")doubts with status "pending" -> type "doubt"autonomous_decisions with research field -> type "autonomous_research" (for user awareness at batch end)
Each entry gets tagged with prd (filename) and cycle. Preserve the full research field when present - this is the only copy that survives state reset. Skip this step if nothing to write.
6a. Render this PRD's audit file dev/local/reviews/<prd-base>-audit.md (<prd-base> = PRD filename without .md) ONCE from the state decision arrays — this is the ONLY writer of audit.md. Write in a single pass (Write tool): a header (PRD, started/completed timestamps, counts autonomous N | deferred N | doubts N), then one entry per item in state.autonomous_decisions (label autonomous), state.deferred_decisions (deferred), and state.doubts (doubt), using the entry format in references/audit-log-format.md. When all three arrays are empty, write the header plus a single no decisions recorded line.Append PRD summary to the batch report, whose filename is built from the current state.batch.id: dev/local/autopilot/reports/{state.batch.id}-report.md (create with header if missing). Invariant — before appending, verify the target filename's id matches the batch.id held in state.batch.id; they are identical by construction. Never glob reports/*.md to choose a file, and never append to a report whose id differs from state.batch.id — a mismatch is a batch-identity error; create a fresh {state.batch.id}-report.md instead. See references/batch-report-format.md for format. Doubt Rubric Verdicts (PRD 00038): render state.doubts_rubric_verdicts one row per rule; when entries carry source (a dual-reviewer doubt_reviewer: fable run — one entry per rule per reviewer), combine both reviewers into a single row per rule — | R1 | pass (codex) / pass (fable) | (a per-reviewer fail still shows, e.g. | R3 | pass (codex) / fail (fable) |). When no entry carries source (single-reviewer / legacy state), render UNCHANGED — one verdict per rule, no source suffix (| R1 | pass |). Loop Metrics (PRD 00013): render a ### Loop Metrics subsection from dev/local/autopilot/loop-metrics.jsonl — read the lines whose prd matches state.prd AND whose batch matches state.batch.id, then report the session count, wall seconds grouped by phase_launched, and the PRD total (sum of wall_secs). When the metrics file is missing or has no matching lines (a manual run outside the loop), render no loop metrics (manual run) instead — never fail the report. See references/batch-report-format.md. Implementor Mix (PRD 00019): render an ### Implementor Mix subsection from state.tasks[] — (1) count attempts by attempts[].implementor (claude/qwen/gemini; an attempt row without the field counts as unknown); (2) list qwen preflight outcomes by counting non-null attempts[].preflight_outcome values per value; (3) count ineligible tasks per qwen_excluded_reason (tasks whose qwen_eligible is false or absent; a missing reason counts as unknown). Absent fields (legacy tasks) always count as unknown and the render never fails the report — when state.tasks[] is missing or empty, render no implementor data instead. See references/batch-report-format.md.
7b. Project autonomous decisions into dev/local/decisions.md when that opt-in file exists (skip when absent; audit.md is written either way). Follow the "decisions.md Projection" procedure in references/audit-log-format.md — it covers the qualify criterion (label autonomous + non-trivial), the row format, dedupe, and the single-source rule.
Update the Active Work section of dev/local/project-capsule.md with batch progress. Use the Edit tool to replace the Active Work section content:
## Active Work
### Batch {batch_id}
- [x] {completed PRD name} ({n} cycles)
- [x] ...for each completed PRD in batch
- [ ] {PRD name} - for each PRD still in wip/ or backlog/
Observations: {any operational gotchas useful for next iteration}
If the capsule doesn't exist yet (catchup was skipped), create a minimal one with just the Active Work section.
Print per-PRD summary:
── AUTOPILOT ── PRD: {prd-name} ── DONE ── {n} cycles ─────────────
Summary:
- Cycles: {n}
- Autonomous decisions: {count}
- Escalated decisions: {count}
- Follow-up tasks fixed: {count}
dev/local/prds/wip/*.md or dev/local/prds/backlog/*.md?Yes → reset state for next PRD: set phases_completed to [], cycle to 1, tasks_total: 0, tasks_completed: 0, clear tasks/task_aborts/cap_rotations/autonomous_decisions/deferred_decisions/review_cycles/doubts/doubts_rubric_verdicts/rework_task_ids/work_start_sha/repo_root/design_doc/design_gate/design_mode/pause_reason/cap_pause_reason (the next PRD starts a fresh plan, not a rework dispatch; cap_rotations, work_start_sha, repo_root, and doubts_rubric_verdicts are per-PRD scratch — Phase 3 of the next PRD overwrites work_start_sha and repo_root, the review phase's consolidation overwrites doubts_rubric_verdicts, but clearing here prevents stale values from surviving if the next PRD aborts before reaching those phases; the design fields are likewise per-PRD scratch — Phase 0 re-derives design_mode/design_gate from the next PRD's frontmatter and Phase 1.5 re-sets design_doc, cleared here so a skipped or aborted next PRD can't inherit them), set replan_count: 0 (it tracked the current PRD's replans; the next PRD starts fresh). Delete dev/local/autopilot/replan-context.md if it exists (defensive — plan-tasks deletes it on success, but a malformed prior session may have left it). Preserve batch field in full, including batch.catchup_completed_at and batch.catchup_head_sha — Phase 1 of the next PRD reads these to decide between full catchup and delta refresh (see Phase 1 "Batch cache check"). Set phase: "build" and next_phase: "build" (the next PRD starts the build gate at catchup). Then end the turn — in loop mode the wrapper reads the non-empty next_phase: "build" and launches a fresh session for the next PRD; outside the loop the user re-invokes /run-autopilot manually. Print:
── AUTOPILOT ── {prd-name} done ── next PRD in new session ────────
Then STOP.
No → print the batch summary below, then branch on loop mode:
$_AUTOPILOT_LOOP set) — non-interactive batch end. The deferred JSON (step 6) and the batch report (step 7) are already written. Set state.phase = "done" and state.next_phase = "" (empty; nothing more to run), notify the user with the batch counts (PRD 00017: python3 ~/.claude/hooks/notify.py --send "autopilot 📋 {repo}" "Batch done: {n} done, {m} stalled, {k} deferred. Run /run-autopilot review-batch." — count stalls from the deferred JSON's type: "stall" entries; when both m and k are zero, shorten to "Batch done: {n} PRDs."), print the summary, and END THE TURN. The wrapper reads the empty next_phase, archives state.json to reports/{batch_id}-state-final.json, and stops the loop. Do NOT run the chunked Batch-End Review here — no human is present; it runs later via /run-autopilot review-batch.state.phase = "done" and state.next_phase = "" and STOP. state.json stays on disk; the next invocation's batch-identity rollover (Phase 0 step 3) mints a fresh batch.── AUTOPILOT ── COMPLETE ───────────────────────────────────────────
Completed {n} PRDs:
1. {prd-name} ({cycles} cycles)
2. {prd-name} ({cycles} cycles)
...
Collect ALL pending items from across the batch and present them to the user. Interactively, this is mandatory if any items exist — never exit with items unpresented. In loop mode the batch ends non-interactively (see the branch above) and this presentation runs later via /run-autopilot review-batch.
Source: dev/local/autopilot/deferred/{batch_id}-deferred.json (single source of truth - all items were written here at Phase 9 step 6 of each PRD). Contains four item types:
deferred_decision - issues that failed research or were deferred for other reasonsdoubt - unresolved findings from doubt reviewdoubt-overflow - FIX/VERIFY items deferred when doubt review found >5 issues (present under UNRESOLVED DOUBTS)autonomous_research - research-backed decisions made autonomously (for user awareness)Auto-fix trivial items first. Before presenting items to the user, scan for trivial fixes that are clearly additive-only: docstring/comment improvements, test helper fixes (missing kwargs, style), formatting. Fix these silently, commit, and remove from the deferred list. Only present items that genuinely need a user decision.
Presentation format - stalls first, then chunked by PRD (PRD 00017):
Before any PRD chunk, present the STALLED PRDS block when the deferred
JSON has type: "stall" entries (omit the block entirely when none):
── BATCH REVIEW ── STALLED PRDS ({count}) ────────────────────────
1. {prd} — {site}: {detail}
resume: move back to dev/local/prds/wip/ and re-run
Also render type: "assumed-ambiguity" records under a visible
ASSUMPTIONS MADE ({count}) heading inside their PRD's chunk (each:
the question and the assumption taken) — the human reviews everything
the loop decided alone. Omit the heading when there are none.
Present items grouped by PRD, one PRD at a time. For each PRD chunk:
── BATCH REVIEW ── PRD: {prd-name} ── {n} items ──────────────────
DEFERRED DECISIONS ({count}):
1. {severity emoji} {issue description}
Cycle: {n} | Consensus: {consensus}
Context: {why this was deferred - include research evidence if available}
File: {path}
Options: fix now / create issue / ignore
2. ...
UNRESOLVED DOUBTS ({count}):
1. {severity emoji} {doubt description}
Context: {what the doubt review found and why it matters}
Options: fix now / create issue / ignore
AUTONOMOUS RESEARCH DECISIONS ({count} - for awareness):
1. {severity emoji} {issue description} -> {action taken}
Research: {evidence_summary from research field}
(No action needed unless you disagree)
Wait for user decisions on each PRD chunk before showing the next. For "fix now" items, execute the fix before continuing. For "create issue", create a GitHub issue with the context shown.
After all PRD chunks are reviewed (or user says stop), retain the deferred JSON — dev/local/autopilot/deferred/ is durable (the batch's deferred-items record per the Retention contract), so do NOT delete it. Then STOP (the interactive batch-end branch above already set phase: "done" and next_phase: "").
If the deferred JSON doesn't exist or is empty, there is nothing to present — skip the presentation and STOP.
Unattended mode runs each session headless: the autoclaude wrapper (in ~/.config/bash/plugins/development.plugin.bash) launches claude -p "/run-autopilot", the session runs exactly one turn, and the process exits at turn end. There is no signal file and no Stop-hook choreography — state.json is the entire hand-off contract.
Hand-off = write state, print banner, end the turn. After the process exits, the wrapper reads state.json and branches:
pause_reason set or phase == "paused" → notify the user with the pause detail, stop the loop, state left intact.stall_reason.stalled == "subagent_prompt_overrun" (set by /work's Subagent Dispatch Budget when an assembled subagent prompt exceeded 50K after one trim pass; /work also appends to state.task_aborts) → continue the loop; Phase 0 of the next session replans the PRD in place (PRD stays in wip/; see Phase 0 step 1's replan procedure). This is the one surviving replan path.next_phase == "" (empty) → backlog drained: the wrapper archives state.json to reports/{batch_id}-state-final.json, notifies, and stops the loop.next_phase non-empty → relaunch a fresh session, which resumes from state by artifact — capsule fresh → skip catchup, tasks exist → skip planning, /work continues at the first non-completed task.state.json missing, unreadable, or untouched by the session → usage-limit check against the captured session log (last-session.log; a live limit means sleep-until-reset and continue), else notify "died" and stop loud.A Work-turn context-cap overrun is just branch 4: autopilot_context_cap_hook.py records the rotation (appends to state.cap_rotations, resets the in-flight task to pending, sets next_phase: "build"), the turn ends, and the fresh session resumes build by artifact — NO replan.
The model's only job at a hand-off is to write state.next_phase (and phase/stall_reason) accurately, print the banner, and end the turn. The model never writes any signal and never inspects the wrapper's decision table — writing accurate state IS the hand-off. Interactive (non-loop) semantics are identical minus the wrapper: the same state writes happen, and the user re-invokes /run-autopilot manually.
End the turn only at a real hand-off. A phase is complete only when its artifacts are written and state is advanced (phases_completed updated, next_phase set). Dispatched work — Agent calls and background Bash — returns its results within the same headless turn: the harness re-invokes you with each <task-notification> before the turn can end, so dispatch, overlap independent work, wait for the results, and finish the phase. Do not end the turn to "wait for" something you dispatched.
An idle end-of-turn (phase unfinished, nothing pending) no longer thrashes anything — the wrapper relaunches and the next session resumes the phase by artifact (self-healing) — but it burns a session start, so treat it as waste, not as a mechanism.
For long Bash (builds, tests), still prefer the FOREGROUND with an explicit timeout (up to 600000 ms) so the result is in hand directly. If genuine work cannot finish in this session, that is a PAUSE (phase: "paused" + state.pause_reason = {"site": "work_incomplete", "detail": "<what could not finish>"} + end turn), not a silent idle stop.
The autoclaude wrapper exports _AUTOPILOT_LOOP=$$ before launching each headless session. Skills branch on it for loop-mode behavior only — the AskUserQuestion ban (Error Handling), git-push deferral, notify suppression in ~/.claude/hooks/notify.py. Hand-off sites do NOT check it: the state writes are the same in and out of the loop.
Review-file gate (in-session quality gate). review_coverage_hook.py stays registered on Stop: at the done hand-off, when the saved review file is missing or fails the check_review_file.py shape check (missing reviewer section, verdict, or tests line — PRD 00016), it exit-2-blocks the turn's end and feeds the gap back to the model so the review can be finished before the turn ends. Exit-2 Stop-hook blocking works in -p mode (proven by the 00014 spike, probe (c) — dev/local/tmp/00014-headless-spike.md). This is a completeness gate on review artifacts, not loop orchestration.
Wrapper sketch (the real autoclaude adds the memory circuit-breaker, the session wall-clock cap, orphan cleanup, metrics, and notifications):
while true; do
[ -f dev/local/autopilot/pause-requested ] && break # operator pause
WARDEN_UNATTENDED=1 claude -p "/run-autopilot" 2>&1 | tee dev/local/autopilot/last-session.log
# read state.json and branch: paused → stop; stall → continue (replan);
# next_phase "" → archive state, stop (drained); next_phase set → continue;
# state missing/untouched → usage-limit check, else stop loud (died)
done
There is no separate between-session de-slop pass. The standalone codex pass that
once ran from the autoclaude wrapper after every commit was removed: it was an
unconditional external call that fell silently dead when codex hit its usage
limit. De-slopping now happens inside every review cycle — Bob (codex)
carries the doubt + de-slop lens in the review-work-completion roster, with a
Claude fallback when codex is unavailable, so the lens never silently drops. If
you are checking how de-slop is wired, look at the review roster, not the
autoclaude function.
&&, |, or ; in a single Bash call. Use separate Bash tool calls instead.2>/dev/null. Handle missing files by checking existence or catching errors in the tool result.Glob or Read instead of ls where possible (e.g. to check if files exist or list directory contents).mkdir -p in its own Bash call when creating directories.| Situation | Interactive | Loop mode ($_AUTOPILOT_LOOP set, PRD 00017) |
|---|---|---|
| Sub-skill invocation fails outright (no usable result; the phase cannot proceed) | PAUSE, report which skill failed and error. A transient reviewer/sub-skill error during the review-rework cycle is the Phase 5 Safety Checks row's domain instead (graceful degradation, not a PAUSE). | Re-invoke the sub-skill ONCE; if it fails again, stall the PRD (recovery.md → "Loop-mode stall procedure", site: "sub_skill_fail") and continue the batch |
| No PRDs anywhere | STOP with message about /create-prd | Write state.phase = "done" and state.next_phase = "" first so the wrapper stops as drained, not died |
| State file corrupted | Delete it, restart from Phase 0 | Same (in-session recovery, no pause) |
| Review produces no parseable output | PAUSE, report — don't retry | Re-run the review ONCE; still unparseable → stall the PRD (site: "reviewer_fail"), continue the batch |
| All reviewers fail | PAUSE, report — partial results usable if user confirms | Re-invoke ONCE; still nothing → stall the PRD (site: "reviewer_fail"), continue the batch |
dev/local/ doesn't exist | Create it | Same |
| Task tools unavailable | STOP, report — can't operate without tasks | Same (a broken harness is not a per-PRD failure) |
| Git push fails (auth, locked signing agent, network) | Report and let the user retry | Log to deferred_decisions[], leave the commits local (the user pushes manually per Phase 9), CONTINUE — a locked signing agent on an unattended host is expected (it stalled the loop 145 min on 2026-06-15) |
mv verify fails (backlog→wip, wip→done, wip→stalled) | PAUSE per the mv-verify sites | Retry the mv ONCE after re-running mkdir -p; persistent failure is one of the two sanctioned loop stops below |
| Security-critical finding (exposed secret, vulnerability being shipped) | PAUSE | PAUSE — sanctioned loop stop #1 (set phase: "paused" + pause_reason; the wrapper notifies and halts) |
| Detected data-loss risk | PAUSE | PAUSE — sanctioned loop stop #2 (same mechanics) |
Loop mode has exactly two turn-ending PAUSEs — the two sanctioned rows above (plus the mv-retry exhaustion, which resolves into the same loud stop). Everything else stalls the PRD or defers and continues. Future edits to this table must not re-grow the loop-mode PAUSE list.
Use when designing a goal-oriented agent loop or reviewing one for runaway risk - build gate, machine-decidable goal, anti-Goodhart boundaries, five failure modes. Triggers on "design a loop", "review this loop", "will this loop run away".
Use when running OpenAI Codex via the codex CLI (copilot CLI fallback) for code analysis, refactoring, or editing. Triggers on "run codex", "codex analyze", "ask codex", "copilot with codex".
Use when running Google Gemini via the native Gemini CLI for code analysis, refactoring, or editing. Triggers on "run gemini", "gemini analyze", "ask gemini".
Use when running a local Qwen model (via llama.cpp and the pi agent) for code analysis, refactoring, or editing - local inference, no API cost. Triggers on "run qwen", "qwen analyze", "ask qwen", "local model task".
Use when running Anthropic Claude Sonnet via the native claude CLI (headless) for code analysis, refactoring, or editing. Triggers on "run sonnet", "sonnet analyze", "ask sonnet".
Use when converting a plan, design doc, brainstorming output, or discovery doc into a structured PRD saved to the backlog. Triggers on "create PRD", "create PRD from plan", "make PRD", "convert to PRD", "save plan as PRD".