autonomous-pipeline

name	autonomous-pipeline
version	0.1.1
description	Run the whole engineering lifecycle end-to-end from one request — spec → plan → build → simplify → test → review → performance → ship — as a single autonomous pass with ONE human approval (the plan) and hard-gated escalation for anything irreversible. It chains the auto-* phase skills, carries a durable pipeline checkpoint so a stop/crash resumes at the exact phase and task it left off, watches the signal between phases (a phase that misses its gate is recorded blocked and surfaces in the register — fail closed, no false-green downstream), and returns a verified findings register at the end. Autonomous: it AUTO-FIXES the confirmed findings in a bounded converge-loop (never asking permission to fix), returning only the residual it can't converge within budget or a fix needing an irreversible/ambiguous decision. This is the top-level "maximise autonomous agents" entry point. Composes every auto-* phase plus checkpoint-resume, budget-guard, watch-and-act, and adversarial-verify.
allowed-tools	["Skill","Agent","Workflow","AskUserQuestion","Bash","Read","Write","Edit","Grep","Glob"]
disable-model-invocation	true
user-invocable	true
effort	high
argument-hint	<feature request to take from idea to shipped> (or 'resume' to continue a run)
arguments	["request"]
when_to_use	Use when the user wants "prompt → spec'd, planned, built, cleaned, tested, reviewed, tuned, shipped" in one autonomous run rather than invoking each phase by hand. Do NOT use for a single phase (invoke that auto-* skill directly), or for a quick one-off edit. It spawns many agents across many rounds over the whole lifecycle — it is explicit-user-only.

This is the most powerful and the most dangerous skill here — the full lifecycle, unattended. It inherits every phase's guardrails and adds pipeline-level ones. Non-negotiable: 1. ONE HUMAN GATE (plan approval); irreversible/ambiguous/unfixable situations from ANY phase still STOP and ask. Approval authorizes the plan, not surprises. 2. AUTO-FIX TO CONVERGENCE — BOUNDED, NEVER INFINITE. Autonomous means the pipeline FIXES what it finds; it NEVER hands you a list of confirmed blockers and asks permission to fix them. After review it runs a bounded fix converge-loop (`converge-loop` + `budget-guard`): fix the confirmed findings, regression-test, re-review, repeat until the register is clean. It STOPS and returns the STILL-OPEN residual ONLY when a termination condition fires — the fix budget/round-cap is exhausted without convergence, no-progress/thrash is detected, or a fix needs a genuinely irreversible/ambiguous human decision (that one escalates). Exhausted ≠ converged: a loop that spent its budget returns the open items, never a fabricated green. 3. PHASE GATES FAIL CLOSED. A phase that doesn't reach its bar (build blocked, red validate, unverified review, a died agent) is recorded `blocked` — never `done` — its items go to the register and `converged` is false, so a resume re-enters it and no phase ever hands a FALSE-GREEN forward. The run is ONE forward pass through the phases: rather than hard-stop mid-run (a Workflow can't ask the user), downstream phases still execute over whatever integrated and collect their findings (except auto-simplify, which needs a stable base and is skipped when the build is incomplete). Those findings feed the bounded auto-fix converge-loop (rule 2); only what it can't converge, or a fix needing an irreversible/ambiguous human decision, is returned open. Never fabricate a phase's clean verdict to keep the pipeline moving. 4. DURABLE, RESUMABLE. A pipeline checkpoint records the current phase + each phase's state; a stop/crash resumes at the exact phase/task, skipping completed work — never restarting from spec. 5. BUDGET THE WHOLE RUN. Declare a pipeline-level budget/escalation contract (`budget-guard`) on top of each phase's. The lifecycle is long; an unbounded pipeline is a runaway. 6. HONEST END STATE. Report what actually shipped, which gates ran, and the open findings — caveated by which phases the user chose to skip. Never present a pipeline with a dead gate or a red end-state as done.

Autonomous Pipeline

Overview

One request in, shipped-quality work out — by running the eight phases as one governed pass. The pipeline is not magic autonomy; it's disciplined autonomy: a single approval, fail-closed gates between phases, a durable checkpoint, a whole-run budget, and an honest findings register at the end. It removes the human stepping between phases, not the verification each phase enforces.

Phase order and gates

 auto-spec → auto-plan →[APPROVAL]→ auto-build → auto-simplify → auto-test → auto-review → auto-performance → auto-ship
    │            │                      │             │              │            │              │                │
 testable    acyclic DAG           all tasks     behavior-      green,       verified      measured, no      gates green,
   spec      + self-reviewed       integrated,   preserving     meaningful   findings      correctness       rollback ready,
             covers spec           tests green   cleanups       coverage     (no false +)  regression        human sign-off

Each arrow is a fail-closed gate: a phase that misses its bar is recorded blocked (never done) and its items surface in the register so converged is false and a resume re-enters it — no phase hands a false-green downstream. (The pass runs forward and collects findings rather than halting mid-run; the register, not a mid-run stop, is what carries an unmet gate to the user.) The user may configure which optional phases run (simplify/performance/go-live can be skipped); build/test are not skippable. (This order follows spec→plan→build first; simplify runs against the build's test safety net, test then hardens coverage.)

Runtime backends — canonical deterministic coordinator vs. legacy Workflow

The unattended stretch runs on ONE of two backends. They are NOT peers: one is the canonical runtime, the other a Claude-only compatibility shim.

• CANONICAL — the deterministic coordinator CLI (scripts/pipeline.mjs). A zero-dependency Node program: node autonomous-pipeline/scripts/pipeline.mjs approve|start|resume|status|authorize. This is the runtime the Codex adapter launches and the one Claude should prefer for a real run. The decisive property: no model prompt owns Git, the checkpoint, the phase gates, or the convergence decision — the coordinator library (scripts/lib/) does, deterministically. Model agents (native or Codex) appear ONLY as sandboxed, capability-free per-task engineers; Git integration (git-integration.mjs, git-workspaces.mjs), the locked checkpoint store, the fail-closed phase engine, the budget ledger, the capability-gated authorization, and the convergence conjunction (pipeline-state.mjs) are all machinery, not prose an LLM can talk its way past. A BLOCKED required gate HARD-STOPS downstream execution here (fail-closed): the run returns status:blocked / converged:false and no later phase runs.
  • capture-intake.mjs --config <config> --scope <draft> — separate pre-plan helper that creates the write-once authority. It is not a sixth run verb because it executes before spec/plan exist.
  • approve --plan <canonical.json> --config <run-config.json> — validates the base is approval-ready, independently compares intake→plan, inits the durable run + immutable budget, enters the prepared window, and mints the ONE-USE, intake/plan/config-hash-bound capability. This IS the recorded human approval. A human MUST sit between approve (mint) and start (consume) — the coordinator can never auto-chain the gate.
  • start --run <id> — runs every preflight refusal (intake/plan/base/config drift, wrong target, dirty tree) and consumes the one-use approval BEFORE a single executor spawns, then drives build → post-build phases and publishes ONLY as a fast-forward after the explicit convergence conjunction + a durable finalize done. resume --run <id> continues from durable state (never erasing spend, never re-consuming the approval); status --run <id> is a read-only snapshot; authorize --run <id> --action <ship|deploy|publish|remote-merge> halts a converged run and mints a fresh, action-scoped capability for one irreversible step (a plan approval never satisfies an action). See references/cli-contract.md (grammar + exit codes), references/budget-contract.md (the immutable termination set), and references/authorization-contract.md (capability-gated approval + irreversible actions).
• LEGACY (Claude-only) — references/pipeline-workflow.js. A compatibility backend invoked via the Claude Code Workflow tool. The Codex adapter cannot select it (a Workflow needs the Claude Code runtime). Because a Workflow cannot ask the user or hard-pause mid-run, it does ONE FORWARD PASS collecting findings rather than hard-stopping on a blocked gate — an honestly-different shape documented in references/pipeline-state.md. Prefer the canonical CLI; reach for the Workflow only on a Claude-only install where launching the CLI is not an option.

Phase 0: Intake — request, config, budget, mode

• Ultracode precheck (parallel-effort mode — WARN, never block; runs on new runs AND resumes). Build → review → verify fan out across many CONCURRENT agents (the Workflow backend + the Phase 1 concurrency caps). That concurrency only materializes at the session's top runtime effort level, ultracode — a harness mode (ultrathink-style), NOT the static effort: high in this skill's frontmatter. Check it: Claude Code surfaces the state in your session context (a system-reminder noting ultracode on/off; the Workflow tool being your standing default is the tell). If you cannot confirm it is on, tell the user in one line — "For the fastest run, enable ultracode, the max effort level for parallel work, so the build fans out across parallel agents: /effort ultracode, set the effort level to its max, or include ultracode in your request. Optional — without it the pipeline still completes with the SAME gates, checkpoints and findings register, just sequentially (slower)." Then proceed either way; never gate the run on this.
• Detect new run vs resume ($request = "resume" / a pipeline checkpoint id → resume; skip intake, load the checkpoint, continue at the recorded phase).
• New run: capture the request; ask the FEW configuration questions (AskUserQuestion): which optional phases to run (simplify, performance, go-live/ship-deploy), and any budget/scope steer. Keep it light.
• Make the selected scope binding before spec — as an independent artifact, not a plan field alone. Choose the run id/config first. Expand a named selection (for example, Full MVP = PRD §13.1) into an intake draft {run, selection, selectedScope:[{id,title,source}]}. Then run node <skill-dir>/scripts/capture-intake.mjs --config <absolute-run-config.json> --scope <absolute-intake-draft.json> --json. It atomically writes the canonical, write-once snapshot to <stateDir>/intake/<run>.json; an identical recapture is idempotent and any changed recapture is refused. Do this before auto-spec/auto-plan and pass that snapshot path/content to both. If the initial itemization itself must change, start a new run id (a later per-id reduction remains an acknowledged scopeDrops[] record). The snapshot — not the spec or plan's copy — is the scope authority.
• Codex delegation (D14 — offer ONLY if detected). Probe for a Codex integration (command -v codex, or a codex-type subagent in your available agents). If — and only if — one is present, offer the user a choice to delegate any of three roles to Codex: build (the per-task engineer), review (the slice + dimension reviewers), verify (the adversarial finding-verifiers). Pass their choice as delegate: { build|review|verify: 'native'|'codex' }. If Codex is NOT detected, do not mention it — every role runs native. (At run time, a codex role with no integration DEGRADES to native and the degradation is recorded in the register — never a silent skip.)
• Note which specialists are actually installed — the subagent types available to you and the domain skills in your available-skills list. auto-plan routes each task to the best fit BY DESCRIPTION (a Next.js task → whatever React/SSR specialist exists, whatever it's named), and you pass that installed set to the Workflow as availableAgents so a plan-assigned name that isn't present here degrades to a general engineer (recorded in missingAgents) instead of hard-failing. Never route on a guessed name.
• Verify a git work tree + working branch and declare the pipeline budget-guard contract. The canonical coordinator creates its checkpoint only at approval, after independently comparing plan to intake. The legacy backend creates its checkpoint after plan approval as described below.

Success criteria: run mode determined; write-once intake snapshot captured before spec; ultracode precheck surfaced (or confirmed on); phase config + budget set; git preflight passed.

Phase 1: Run the lifecycle (one approved pass)

The skill owns the human-facing front half (intake, spec, plan, and the single approval); a backend owns the unattended stretch. Pick the backend by install: Codex (and any Claude run that prefers determinism) uses the canonical CLI — after the approval is RECORDED (pipeline.mjs approve, which mints the one-use capability), a human confirms and the run is launched with pipeline.mjs start --run <id>; Git, checkpoints, gates and convergence are the coordinator's, never a prompt's (see Runtime backends above). The steps below describe the legacy Claude Workflow path (references/pipeline-workflow.js), which the Codex adapter cannot select:

1. spec → plan run first, in-session, by FOLLOWING their contracts (they may ask questions, so they stay outside the Workflow). Compose them by CONTRACT, not by a programmatic Skill() call: auto-spec, auto-plan, auto-map and auto-learn are disable-model-invocation skills (expensive, explicit- invocation only), and the docs are explicit that dmi blocks programmatic invocation — so a Skill(auto-spec) from here would fail. Instead read the installed skill's SKILL.md (find it under .claude/skills/, .agents/skills/, or the plugin root) and execute its phases directly; if it isn't installed, apply the methodology inline. Pass the independent intake snapshot path/content to both phases; auto-plan runs its validator with --intake <snapshot>. At the SINGLE approval gate, render SCOPE COVERAGE: N of M selected-scope items covered and list every uncovered id. A general plan approval never authorizes a drop: ask for and record a separate, unambiguous acknowledgement for each proposed drop id, update the plan, re-run its gate, and only then ask for the plan approval.
2. Create the checkpoint (checkpoint-resume's scripts/checkpoint.mjs init) — the Workflow sandbox has no filesystem access, so the skill creates the status file before launch. Pass --required-phases "build,test,review,auto_learn,auto_map" --require-validation so the store itself refuses a premature done. Pass --launch '{"scriptPath":"<pipeline-workflow.js>","args":{…the full launch args including intakePath and intakeScope…}}' so the exact relaunch recipe is persisted IN the status file — then run-status.mjs --resume can reconstruct the resume with no session memory.
3. Launch references/pipeline-workflow.js via the Workflow tool with full args:
   • required: root, workingBranch, validate (the whole-workspace end-state gate), planPath (the approved DAG plan), intakePath (the write-once snapshot), intakeScope (its parsed complete object), approved: true, statusFile, checkpointCli (absolute path to checkpoint-resume/scripts/checkpoint.mjs — the status-writer agents call it).
   • routing/quality: availableAgents (the installed specialist set from Phase 0 — each task's plan-assigned agent/reviewer is honored when present, degrades to general when not, recorded in missingAgents), allowGeneralFallback (default true — degrade a missing specialist rather than crash), and planValidator (absolute path to auto-plan/scripts/validate-plan.mjs — the DETERMINISTIC DAG gate preflight runs; pass it so a cyclic/mis-ordered plan can't pass on model judgment. Without it, preflight falls back to an LLM plan check).
   • budget/config: the optional-phase config ({simplify, performance, shipPrep}), budgetFloor (default 60000 — stop-and-report at a phase boundary once the run dips below it), the concurrency caps (maxBuildParallel, maxParallel, maxFix per-task, maxFixRounds for the Phase-2 register converge-loop), and delegate (D14 — the per-role Codex choice). It executes build → simplify → test → review → performance → ship-prep with fail-closed gates (a phase agent that died = a gate failure in the register; skipped ≠ clean), the DAG walk with worktree isolation and bounded fix loops (each engineer/reviewer routed to its task's specialist), and per-task checkpoint writes. It hard-throws without approved: true — the human gate cannot be bypassed. Its return field workflowConverged describes only this ordinary pass; its final converged remains false because the outer skill still owns whole-register remediation and the required closeout.
4. AROUND the workflow (before launch / after it returns), use watch-and-act to gate on external signals — e.g. CI green on the pushed branch before offering a fix round. (A Workflow cannot invoke skills mid-run.)
5. Any escalation (unfixable/ambiguous/irreversible) surfaces in the returned register and PAUSES the pipeline; on resolution, re-invoke — the checkpoint resumes at the exact phase/task.

Querying a run (any time, from any session): node <checkpoint-resume>/scripts/run-status.mjs renders the newest run — phases, per-task progress, the open register, and the resume command — READ- ONLY, so it's safe to run while the pipeline is in flight. --list shows all runs; --resume emits the exact Workflow call to continue. This is how the user checks "where's my run at?" without touching it.

Native /goal framing (Claude Code): for a fully unattended run, set the session goal to the pipeline's Output Contract before launching — /goal pins the done-condition ("selected scope is fully covered; the whole actionable register is empty; every gate and final validation passed; durable auto_learn and auto_map receipts are done") and the platform's independent verifier model checks it, so the actor never grades itself. See converge-loop's references/native-goal-loop.md for the full termination-set → /goal compilation.

See references/pipeline-state.md for the state machine, per-phase gate conditions, and the handoff contract.

Success criteria: each ordinary phase reached its success bar before the next began; the checkpoint reflects progress; escalations reached the user, not a guessed-through continuation. This phase alone never claims final lifecycle convergence.

Phase 2: Fix to convergence (bounded auto-loop)

• Build ONE whole-run actionable register from every current source: build/blocked units, simplify, test, review, performance, ship-prep, integration, final validation, and closeout. Severity never makes a defect non-actionable. Exclude only a pure informational observation or a selected-scope drop the user explicitly acknowledged for that id; a selected-scope item can never be relabeled info/deferred. Dedup, then adversarial-verify the aggregate so only real items enter the fix loop.
• "Fix all" means that complete actionable register, not the findings from one review pass or one file. Run a BOUNDED fix converge-loop on it — do NOT ask permission: fix the findings (slice-scoped, each staying inside its task's write scope), regression-test, re-review, and repeat until the register clears. After every round, persist its resolutions/new findings and re-read the CURRENT durable register before deciding whether it is dry; newly exposed regressions join the same loop. The loop's termination set is explicit and declared up front (converge-loop + budget-guard): a max round cap, the whole-run budget floor, and a no-progress/thrash stop.
• STOP the loop and return the STILL-OPEN residual ONLY when a termination condition fires — budget/cap exhausted without convergence, no progress across a round, or a fix that needs an irreversible or ambiguous human decision (that one escalates and asks). Exhausted ≠ converged: report the residual as OPEN with the termination reason, never a fabricated green.

Success criteria: the complete current actionable register is driven to clean, OR the honestly-open residual is returned WITH the termination reason — and no permission question was asked about fixing confirmed findings.

Phase 2b: Close every run

1. After EVERY run, including blocked, exhausted, and aborted runs, follow the auto-learn contract by CONTRACT (it is a dmi skill: read its installed SKILL.md and execute it; do not call Skill()). Harvest the checkpoint's aggregate register, blocked units, and degradations into verified, routed learnings. Record auto_learn as running, then done only after the contract actually succeeds; missing/dead/red records blocked and remains in the actionable register.
2. After every non-aborted run, follow auto-map only after auto_learn is done. Refresh and verify the disclosure-tiered context map, then record the durable auto_map receipt. A blocked auto_learn leaves auto_map unrun/upstream-blocked; neither receipt may be inferred from report prose.
3. Re-read the approved plan and durable checkpoint. Refuse final done unless every selected-scope id is task-mapped or separately acknowledged as dropped, the whole actionable register is empty, final validation is green, and both required closeout receipts are done. Only then call checkpoint.mjs finalize <file> done; otherwise finalize needs_attention with the exact residual.

Success criteria: closeout is attempted in order and durably receipted; a real run cannot report converged/done without both receipts. An aborted run still learns and reports that map was inapplicable, never that it ran.

Phase 3: Report

Read the pipeline checkpoint and report end-to-end (see Output Contract): what each phase produced, which gates ran vs. were skipped (by user config), what shipped, and the open register. Fail closed — a run with a dead gate, red end-state, uncovered scope, actionable register item, or missing closeout receipt is not "done".

Success criteria: an honest, phase-by-phase account; the durable checkpoint reflects the final state.

Common Rationalizations

Rationalization	Reality
"It's autonomous, so just return the findings and let the user fix them."	Autonomous means it FIXES what it finds — it never asks permission to fix confirmed blockers. It auto-fixes to convergence.
"Fix all meant the 19 review findings; the other register can be follow-up."	Fix all means the entire current actionable register across every phase and severity. Only information or a separately approved scope drop is outside it.
"Auto-fix means loop until everything's perfect."	Auto-fix is BOUNDED: a max round cap + the run budget + a no-progress stop. It converges, or returns the open residual honestly. Unbounded looping is the multi-hour-grind failure mode; the termination set prevents it.
"Build came back with blocked tasks but let's just run review anyway."	A phase that didn't meet its bar hands a false-green downstream. Gates fail closed — pause or escalate.
"The user approved the plan, so I can deploy too."	Plan approval ≠ deploy approval. Irreversible steps in any phase still need explicit sign-off.
"Resume by re-running from spec, it's cleaner."	That redoes finished work and can diverge from what shipped. Resume from the checkpoint at the recorded phase.
"Report it as shipped — most of it worked."	Partial is not done. Report what shipped, which gates ran, and the open register honestly.
"Learn/map are useful follow-ups; the product is already done."	They are required closeout phases. Without both durable receipts the run is not converged/done.
"Skip the budget, the phases have their own."	The lifecycle is long; phase budgets don't bound the whole. Declare a pipeline budget too.

Red Flags

• The auto-fix converge-loop running WITHOUT a declared termination set (max rounds + budget + no-progress) — it must be bounded, or it's the multi-hour-grind failure mode.
• Returning confirmed, mechanically-fixable blockers UNFIXED and asking the user whether to fix them — autonomous means fix them.
• Reporting a low-severity/non-review defect outside the fix loop, or deferring a selected-scope item.
• A downstream phase started while the upstream phase had unresolved blockers.
• A deploy/irreversible step taken on the strength of the plan approval alone.
• A resume that restarted from spec and redid integrated work.
• "Shipped" reported while a gate didn't run or the end-state validate is red.
• converged:true or done with an uncovered selected-scope id or without both auto_learn and auto_map durable receipts.
• No pipeline-level budget declared for a full lifecycle run.

Guardrails

• One approval gate (the plan); every irreversible/ambiguous/unfixable situation still escalates.
• Auto-fix the confirmed register to convergence, BOUNDED by a termination set (max rounds + budget + no-progress); return only the residual it can't converge or a fix needing an irreversible/ambiguous decision — never ask permission to fix confirmed findings.
• Treat "fix all" as the current whole-run actionable register, re-read after each round; severity/source never exempts a defect, and selected scope cannot be deferred without its own user-approved drop.
• Phase gates fail closed; never pass a false-green downstream.
• Durable resume from the checkpoint; never restart from spec.
• Declare and enforce a pipeline-level budget.
• Report the honest end state, caveated by which phases were skipped.
• Refuse convergence on uncovered selected scope, any actionable register item, or a missing/blocked auto_learn/auto_map receipt.

When To Load References

• scripts/capture-intake.mjs — the deterministic Phase-0 write-once intake capture. Run it before loading auto-spec/auto-plan for a new pipeline run; never recreate the authority from the later plan.
• scripts/pipeline.mjs — the canonical deterministic coordinator CLI (the Codex runtime): approve|start|resume|status|authorize. Run it (after a recorded approve) instead of the Workflow whenever determinism is wanted or Codex is the host; its scripts/lib/ modules own Git, the checkpoint, the gates, and convergence. Read the three contracts below to drive it safely.
• references/cli-contract.md — the CLI's five-verb grammar, flags, and pinned exit-code table (the human-readable spec; scripts/lib/cli-contract.mjs is the enforced one). Load before scripting the CLI.
• references/budget-contract.md — the immutable termination set (the whole-run budget/no-progress/ escalation bound the coordinator enforces). Load when setting or reasoning about a run's budget.
• references/authorization-contract.md — the capability-gated plan approval + irreversible-action model (approve/authorize). Load before wiring approval or any ship/deploy/publish/remote-merge step.
• references/pipeline-workflow.js — the LEGACY Claude-only Workflow backend for the unattended stretch (build → simplify → test → review → performance → ship-prep, forward-pass, checkpointed). The Codex adapter cannot select it. Launch via the Workflow tool with full args after the plan approval; edit + relaunch (same scriptPath) to iterate.
• references/pipeline-state.md — the phase state machine, per-phase gate conditions, the phase-to-phase handoff contract, the checkpoint v2 schema, and the canonical-hard-stop vs. legacy-forward-pass divergence. Load when wiring or resuming a run.
• The phase skills — auto-spec, auto-plan, auto-build, auto-simplify, auto-test, auto-review, auto-performance, auto-ship — each runs its own phase to its own bar.
• checkpoint-resume (skill) — the durable pipeline + per-phase state.
• budget-guard (skill) — the whole-run budget + escalation contract.
• watch-and-act (skill) — wait on CI/deploy signals between phases.
• adversarial-verify (skill) — verify the returned findings register.

Output Contract

Report:

1. the run config — which phases ran vs. skipped; the working branch; the single approval recorded; the intake snapshot path/hash; and SCOPE COVERAGE: N of M with covered, explicitly dropped, and UNCOVERED ids
2. per phase: outcome + gate status (met its bar / blocked / skipped); which specialists the build actually routed to (specialistsUsed) and any plan-assigned specialist that was absent here and so ran generic (missingAgents — surface it so the user can install the missing agent/skill)
3. ship-prep artifacts produced (changelog + PR body draft — OPENING the PR / deploying is the user's explicitly-gated step) and the end-state validate result (honest)
4. the fix-loop outcome — sources/count of the whole actionable register, the count AUTO-FIXED to convergence, informational observations/explicit scope drops reported separately, and the STILL-OPEN residual (if any) with its termination reason (budget/cap exhausted, no-progress, or an escalated irreversible/ambiguous fix) — never an unfixed register presented as a menu of choices
5. the pipeline checkpoint path (durable, resumable record) + the one-liner to query it any time (run-status.mjs for a rendered view, run-status.mjs --resume for the relaunch call)
6. closeout receipts — auto_learn and auto_map each ran/done or the exact blocker; never report converged/done unless both are durably done