Imported from andrewstellman/quality-playbook (SKILL.md). Install upstream with npx skills add andrewstellman/quality-playbook. Copyright stays with the author (Complete terms in LICENSE.txt).

Quality Playbook Generator

Plan Overview — read this first, then explain it to the user

Before reading any other section of this skill, understand the plan and its dependencies. Each phase produces artifacts that the next phase depends on. Skipping or rushing a phase means every downstream phase works from incomplete information.

Phase 0 (Prior Run Analysis): If previous quality runs exist, load their findings as seed data. This is automatic and only applies to re-runs.

Phase 1 (Explore): Run the documentation intake first. Invocation form: bin/reference_docs_ingest.py does from bin import benchmark_lib at import, so Python's -m bin.reference_docs_ingest resolves ONLY when bin/ is reachable as a top-level package. Resolve the install root (the directory containing this SKILL.md) via the same install-location fallback list this skill uses for references/ / quality_gate.py (the 10 canonical layouts documented below — install-root forms: <empty>, .claude/skills/quality-playbook, .github/skills, .github/skills/quality-playbook, .cursor/skills/quality-playbook, .continue/skills/quality-playbook, .codex/skills/quality-playbook, .windsurf/skills/quality-playbook, .cline/skills/quality-playbook, .aider/skills/quality-playbook). Then, from the target repo root, run PYTHONPATH=<install_root> python3 -m bin.reference_docs_ingest <target>. If <install_root> is empty (QPB self-bootstrap, or a setup_repos.sh install where bin/ is already at the target root) omit the PYTHONPATH= prefix — cwd already provides bin/. The bare python -m bin.reference_docs_ingest <target> works ONLY for those two layouts; an install_skill.py-layout adopter (bin/ inside the marker dir) otherwise gets ModuleNotFoundError: No module named 'bin'. This walks reference_docs/ — cite/ files produce quality/formal_docs_manifest.json records; top-level files are loaded as Tier 4 context via reference_docs_ingest.load_tier4_context(<target>). Then explore the codebase in three stages: open exploration driven by domain knowledge, domain-knowledge risk analysis, and selected structured exploration patterns. Write all findings to quality/EXPLORATION.md. This file is the foundation — Phase 2 reads it as its primary input.

Phase 2 (Generate): Read EXPLORATION.md and produce the quality artifacts: requirements, constitution, functional tests, code review protocol, integration tests, spec audit protocol, TDD protocol. (AGENTS.md at the target's repo root is generated by the orchestrator AFTER Phase 6, not by you in Phase 2 — see "File 6" below for the contract.)

Phase 3 (Code Review): Run the three-pass code review against HEAD. Write regression tests for every confirmed bug. Generate patches.

Phase 4 (Spec Audit): Three independent AI auditors review the code against requirements. Triage with verification probes. After triage, the same Council runs the Layer-2 semantic citation check — one prompt per reviewer, structured per-REQ verdicts for every Tier 1/2 citation, output to quality/citation_semantic_check.json. Write regression tests for net-new findings.

Phase 5 (Reconciliation): Close the loop — every bug from code review and spec audit is tracked, regression-tested or explicitly exempted. Run TDD red-green cycle. Finalize the completeness report.

Phase 6 (Verify): Run self-check benchmarks against all generated artifacts. Check for internal consistency, version stamp correctness, and convergence.

Phase 7 (Present, Explore, Improve): Present results to the user with a scannable summary table, offer drill-down on any artifact, and provide a menu of improvement paths (iteration strategies, requirement refinement, integration test tuning). This is the interactive phase where the user takes ownership of the quality system.

Every bug found traces back to a requirement, and every requirement traces back to an exploration finding.

The critical dependency chain: Exploration findings → EXPLORATION.md → Requirements → Code review + Spec audit → Bug discovery. A shallow exploration produces abstract requirements. Abstract requirements miss bugs. The exploration phase is where bugs are won or lost.

MANDATORY FIRST ACTION: After reading and understanding the plan above, print the following attribution banner exactly as written below — do NOT condense, abbreviate, summarize, or reformat. Reproduce the full 8-line canonical block including the two ===…=== rules, both taglines, the blank lines, and the license line, byte-for-byte matching bin/_purpose.print_attribution_banner() (the single source of truth). On the title line, include the running skill version — render it as Quality Playbook vX.Y.Z -- by Andrew Stellman, where vX.Y.Z is the version: from this skill's frontmatter, exactly as print_attribution_banner() emits it. This lets the operator confirm at a glance which version is running (and catches a stale install silently running old behavior). Then explain the plan in your own words — what you'll do, what each phase produces, and why the exploration phase matters most. Emphasize that exploration starts with open-ended domain-driven investigation, followed by domain-knowledge risk analysis that reasons about what goes wrong in systems like this, then supplemented by selected structured patterns. Do not copy the plan verbatim; paraphrase it to demonstrate understanding.

================================================================================
  Quality Playbook -- by Andrew Stellman
  https://github.com/andrewstellman/quality-playbook

  AI code review is good. Quality engineering is better.
  Because code that looks right can still do the wrong thing.

  Licensed under the Apache License, Version 2.0
================================================================================

(The template above shows the canonical 8-line block; on the first line, insert vX.Y.Z after Quality Playbook using this skill's frontmatter version:, matching bin/_purpose.print_attribution_banner().)

Generate a complete quality system tailored to a specific codebase. Unlike test stub generators that work mechanically from source code, this skill explores the project first — understanding its domain, architecture, specifications, and failure history — then produces a quality playbook grounded in what it finds.

How to run this — self-encoded invocation contract

If the operator hands you this skill (or points you at any QPB-installed target) and says "Run the Quality Playbook" — possibly with a hint like "this is a bootstrap run" or "run on itself" or "self-audit" — this section tells you exactly what to do. The operator should not need to provide additional instructions; the canonical invocation, the defaults, the guardrails, and the output contract all live here.

Pick your execution mode

QPB ships in two execution shapes. Pick the one that matches your runtime — the wrong choice produces the codex-on-codex indirection pathology.

Both modes use the same phase prompt content — the phase_prompts/*.md files at the repo root are the single source of truth, loaded by bin/run_playbook.py::_load_phase_prompt and read directly by Mode A walkthroughs. The only thing the two modes differ on is WHO drives — you (Mode A) or the orchestrator subprocess-spawning a CLI agent (Mode B).

When in doubt, default to Mode A. If the operator wanted runner-driven invocation they would have run the runner themselves; if they pasted "Run the Quality Playbook" into your chat, they want you to drive. The Mode B section below tells you what to do if the operator explicitly invokes the runner.

Documented Mode A vs Mode B asymmetries — by design. Three behaviors deliberately differ between the modes because the modes serve different purposes (Mode A is the adopter-facing walkthrough on a target repo; Mode B is the QPB-internal runner used for benchmarking + headless CI). The phase prompts are identical (single source of truth — see paragraph above); the divergences are in the driver obligations, not the audited contract:

1. Phase 0 entry contract — install validator (Mode A only). Mode A's mandatory first action is to run the install validator. Invocation form is install-location-aware: resolve the install root (the directory containing this SKILL.md) via the same install-location fallback list this skill uses for references/ / phase_prompts/ (the 10 canonical layouts documented below), then run python3 <install_root>/bin/qpb_validate.py <target>. For a clone / self-bootstrap (install root is the QPB clone, bin/ reachable from cwd) the bare python3 bin/qpb_validate.py <target> works. For an install_skill-layout adopter (channel or manual install — bin/ inside the marker dir, e.g. .github/skills/quality-playbook/bin/) you MUST use the install-root-prefixed form; running bare bin/qpb_validate.py from the target repo root fails (the validator lives under the marker dir, not at repo root). The adopter has just installed QPB into their target repo and must confirm canonical layout before any phase. Mode B (the QPB clone running its own runner) operates from the canonical install by construction; the validator would always succeed against the QPB clone itself, so the check would be vacuous. Adopters running Mode A get the install correctness gate; Mode B operators have it already.
2. End-of-run archive (Mode B only). Mode B's _finalize_iteration calls archive_lib.archive_run after Phase 6, materializing quality/previous_runs/<ts>/ for benchmark replay + cross-run diffing. Mode A produces artifacts directly into the target's live quality/ for the adopter to inspect, commit, or re-run on top of; there is no archive partition because there is no benchmark loop. Adopters who want longitudinal archives invoke Mode B explicitly (python3 -m bin.run_playbook --full-run <target>).
3. Phase 6 auditor-prompt sub-agent dispatch (Mode A only). phase_prompts/phase6.md routes Mode A through phase_prompts/phase6_auditor.md via a fresh-context sub-agent spawn (the A-13 hybrid). Mode B's per-phase CLI subprocess is already a fresh context (the structural separation the auditor exists to provide), so phase6.md:5-7 tells Mode B to execute the verification inline and skip the auditor spawn. phase_prompts/phase6_auditor.md is therefore only loaded under Mode A; the file's header documents this routing explicitly.

These asymmetries are pinned by bin/tests/test_mode_a_b_parity_documented.py. If a future change wants to align them, update the pin test, the rationale here, and add the missing behavior to the appropriate side.

Mode A — skill-direct walkthrough (UI-context)

The operator's prompt is just "Run the Quality Playbook" (or "run on itself", "self-audit", etc.). You drive every phase inline.

Mode A means YOU execute the skill. If you are an interactive coding agent (Claude Code, Cursor, Copilot UI, Codex desktop, any tool where the operator is monitoring your chat output in real-time), DO NOT:

• Spawn a sub-skill via your quality-playbook skill-invocation mechanism.
• Spawn a sub-agent via your Task tool / Agent tool / similar.
• Invoke python3 -m bin.run_playbook — that's Mode B, the CLI-automation path.
• Read agents/quality-playbook.agent.md or agents/quality-playbook-claude.agent.md and follow their "spawn sub-agents per phase" pattern — those files are AUTOMATION ONLY (batch CI runs, headless invocations) where per-phase context-window isolation is necessary at the cost of operator chat visibility.

Phase 0 entry contract (MANDATORY first action): run the QPB install validator. Resolve the install root via the install-location fallback list (per the Phase 0 numbered step above), then run python3 <install_root>/bin/qpb_validate.py <target-repo> — bare python3 bin/qpb_validate.py <target-repo> works only when cwd already contains bin/ (QPB clone / self-bootstrap); install_skill-layout adopters (channel install — bin/ under .github/skills/quality-playbook/, etc.) MUST prefix the install root. Paste every event= line; do not proceed until event=validation_complete status=ok — see AGENTS.md "Mode A entry sequence". Non-negotiable — skipped/fabricated validation and bare-path-from-repo-root invocation are both documented adopter failure modes.

EXCEPTION: Phase 6 verification MUST be delegated to a fresh-context sub-agent. Sub-agent delegation is forbidden for EXECUTION (Phases 1-5) because hiding work from operator chat defeats real-time monitoring. Sub-agent delegation is MANDATED for VERIFICATION (Phase 6) because shared-context executor-as-verifier has empirically fabricated PASS verdicts against failing gates across multiple benchmark runs despite repeated rounds of strengthened prompt-level witness language. The principle is the same as the rule above — operator chat should carry the truth — but the mechanism is opposite: visibility for execution; isolation for verification (a fresh auditor has none of the executor's memory/anchoring/completion-reward bias). See phase_prompts/phase6.md + phase_prompts/phase6_auditor.md for the sub-agent delegation protocol. This exception is scoped strictly to Phase 6 verification — the no-sub-agent rule still holds for all Phase 1-5 execution.

EXCEPTION: the Feature H persona validation pass may spawn its personas. The no-sub-agent rule governs delegating a PHASE'S EXECUTION (Phases 1–5). The Feature H persona validation pass (bin/persona_apply.run_feature_h, run automatically at the Phase 2→3 boundary; Design §8b, references/requirements_pipeline.md § E.9) is a second narrowly-scoped sanctioned exception: it spawns fresh-context, tool-restricted domain-expert + security personas to validate the finalized requirements. This does NOT reopen either failure mode the rule closes: (a) it does not delegate a phase's execution — it is a bounded validation remediation that runs at one boundary and returns to your session, never a worker running phases 2–6 (so the delegated-agent-silent-death mode cannot apply); (b) operator visibility is preserved and mandatory — the pass writes an operator-visible quality/expert_review_summary.json listing every applied change with its grounding, every change is agent-validation-tagged and revertible (persona_apply.revert), and the pass is opt-out (disablable for the run), so nothing is hidden and no verdict is fabricated. The guardrail's own principle — operator chat carries the truth — is honored, not bypassed: that review summary is the trail. Scoped strictly to this pass; the no-sub-agent rule still holds for all other Phase 1–5 work — you may NOT delegate a phase, spawn a worker, or invoke the quality-playbook orchestrator sub-skill.

The failure mode that motivated this rule: a shared-context session ran phases 1-6, fabricated the gate-verdict log (quality/results/quality-gate.log was hand-written with a "PASS" tally while the actual gate showed 14 FAIL, GATE FAILED), and reported PASS to the parent. The Phase 6 witness contract (your obligation to quote the gate's verdict lines verbatim) cannot enforce when a sub-skill owns the witness — only the parent's chat carries operator-readable evidence.

YOU read SKILL.md and the phase prompts. YOU execute Phases 1-5 yourself and write artifacts to quality/. Phase 6 verification is delegated to the fresh-context auditor sub-agent per the exception above (the auditor invokes quality_gate.py); you paste its verbatim verdict. Your chat IS the trust trail — execution stays visible, verification stays bias-isolated.

You have shell access — probe before claiming unavailability. Mode A means an interactive coding agent (Claude Code, Cursor, Copilot UI) running commands on the operator's actual machine via your Bash tool — NOT a sandboxed runtime. A common failure is writing "the sandbox can't run X" reflexively, conflating your model-level "I can't natively execute" with the harness Bash tool you DO have. A which <tool> / <tool> --version probe costs one line; assuming-and-being-wrong forfeits the entire Phase 5 evidence trail.

For each phase 1..6, in order:

1. Load the phase prompt. Read phase_prompts/phaseN.md (resolve via the same install-location fallback list documented for references/ below). For phase1.md, substitute {seed_instruction} (the prelude that says "skip Phase 0/0b" — empty string when seeds are allowed) and {role_taxonomy} (the taxonomy block rendered from the role taxonomy below). For phase2.md through phase6.md, the file is pure-literal — read it verbatim.
2. Execute the phase per the prompt. Read the inputs the prompt names, do the analysis, write outputs into the target's quality/ directory.
3. STOP at the end-of-phase boundary. Every phase prompt ends with an "IMPORTANT: Do NOT proceed to Phase N+1" instruction. Honor it. The operator advances to the next phase by saying so.

You are responsible — without the orchestrator's structural backstop — for the same source-unchanged invariant the runner enforces: do NOT modify any file outside the target's quality/ directory. In Mode B the gate would catch this; in Mode A you are the gate. A documented failure mode: a Phase 2 LLM modifying the target's root AGENTS.md — the same failure mode applies in Mode A.

For the bootstrap-run (self-audit) variant of Mode A, see "Bootstrap mode" below — the only delta is that the target IS the QPB repo, so cite the same phase_prompts/ files you read from.

Mode A scope — what's covered, what's Mode-B-only

The per-phase walkthrough above scopes Mode A to phases 1..6. The following surfaces are deliberately Mode-B-only — if the operator wants them, point them at the runner instead of trying to drive them yourself:

• Phase 0 / Phase 0b (seed injection from prior runs). The orchestrator handles seed discovery, prior-run scanning, and seed-prompt injection. In Mode A, treat every run as --no-seeds (skip Phase 0/0b entirely, start at Phase 1). If the operator explicitly asks for seed-driven exploration, hand off to Mode B (python3 -m bin.run_playbook --with-seeds <target>).
• Phase 7 (interactive Present / Explore / Improve). Mode A's Phase 7 is the conversational version — present the artifact summary table inline and let the operator drive what to explore. See the Phase 7: Present, Explore, Improve section below for the canonical treatment covering both modes.
• Iteration strategies (gap / unfiltered / parity / adversarial). Iterations re-enter the playbook with a strategy-specific addendum. In Mode A, after Phase 6 completes cleanly, hand off to Mode B for iterations: python3 -m bin.run_playbook --next-iteration --strategy <name> <target>. The iteration prompts (phase_prompts/iteration.md) ARE single-source-of-truth, but the iteration-orchestration loop (rotating through gap → unfiltered → parity → adversarial) is the runner's job. A Mode A operator who wants iterations after Phase 6 should be told: "Phase 6 is done; run python3 -m bin.run_playbook --full-run <target> to get all four iteration strategies, or pick one strategy explicitly with --next-iteration --strategy gap."

If the operator asks for one of these surfaces in Mode A and the request is ambiguous (e.g., "also do the iterations"), surface the mode-handoff explicitly rather than improvising — improvisation is how the prompt content drifts away from the runner's canonical loop.

Mode B — runner-driven invocation (CLI-automation)

The operator runs python3 -m bin.run_playbook themselves (typically because they want batching, headless CI, or to route per-phase work to a different model). The orchestrator at bin/run_playbook.py spawns a CLI agent per phase, feeds it the externalized phase prompt, and aggregates the result.

Canonical invocation

The orchestrator is the entry point. Two invocation forms are supported:

• python3 -m bin.run_playbook <target> — canonical package-module form.
• python3 /path/to/QPB/bin/run_playbook.py <target> — direct script form. Works from any cwd because the module injects QPB root into sys.path before importing sibling modules.

<target> is the path to the project to audit. For a bootstrap run (target IS the QPB repo), pass . from the repo root. For any other target, pass the path to that target's repo root.

Default behavior (no flags)

Bare invocation triggers a full run: all 6 phases (Explore → Generate → Code Review → Spec Audit → Reconciliation → Verify) followed by all 4 iteration strategies (gap → unfiltered → parity → adversarial), executed synchronously in the same session. Any prior quality/ directory is auto-archived to quality/previous_runs/<TIMESTAMP>/ before the new run starts.

This is the canonical operator path. Don't ask permission to add flags; the defaults are the answer.

When the bare invocation fires, the orchestrator emits a one-line stderr banner naming the run cost. That banner is informational; let it scroll.

Common overrides

Use only when the operator asks for something specific:

Recovering from a partial / aborted runner-driven run

Council 2026-04-30 P1-4: the operator-hygiene guidance for cleaning up after an aborted run lives in the Bootstrap mode section below ("Bootstrap-run operator hygiene") — the recovery is identical in Mode B: git restore quality/ to discard the partial Phase 1/2 output, then re-invoke. Do NOT edit files outside quality/ to "tidy up" — the source-unchanged invariant trips on the very next run. See the Bootstrap-mode hygiene paragraph for the full mechanic; it applies regardless of whether the abort happened during a self-audit run or against an external target.

Bootstrap mode (running QPB on itself)

When the operator says "this is a bootstrap run" or "we're running QPB on itself" or "self-audit":

1. Confirm the working directory is the QPB repo root (or cd there).
2. Invoke python3 -m bin.run_playbook . — same canonical form, target is ..
3. The orchestrator handles archival of the existing quality/ tree to quality/previous_runs/<TIMESTAMP>/ automatically; you don't need to clean anything manually.

The run proceeds the same way as any other target. The only difference is that the audit subject IS the playbook itself, so the produced artifacts describe QPB's own quality system.

Bootstrap-run operator hygiene — recovering from a partial / aborted run. If a prior bootstrap run aborted mid-flight (e.g., the source-unchanged invariant tripped, a phase prompt errored, the operator hit Ctrl-C), the working tree may contain a half-written quality/ directory plus a quality/previous_runs/<TIMESTAMP>/.partial sentinel marking the abandoned archive. Before re-invoking, run git restore quality/ (and, if you want a clean slate, git clean -fd quality/) to drop any uncommitted Phase 1/2 output from the aborted run. The orchestrator will re-archive the now-pristine quality/ tree and start clean. Do NOT edit files outside quality/ to "tidy up" — anything outside quality/ is QPB source; touching it for cleanup will trip the source-unchanged invariant on the very next run. The 2026-04-30 bootstrap test surfaced this exact recovery question: the operator had a half-written quality/ from an aborted Phase 2 and re-running without restoring left stale Phase 1 artifacts that confused the next run's archival.

Guardrails (machine-checkable; treat as hard constraints)

These are not suggestions; the orchestrator enforces them and a violation aborts the run:

1. Synchronous execution — no sub-agent delegation. Run every phase yourself in the same session. Do NOT use the Task tool, sub-agent dispatch, background-agent invocations, the quality-playbook skill-invocation mechanism, or any "delegate phases 2–6 to a worker" pattern. Two verified failure modes the guardrail closes: (a) Phase 1 completes, phases 2–6 silently die in a delegated agent that loses its parent session, the runner self-marks -PARTIAL, and the operator gets no signal anything was wrong. (b) An interactive coding session spawns the quality-playbook orchestrator sub-skill; sub-skill runs Mode A in its own context but fabricates quality/results/quality-gate.log and reports "Gate: PASS" against an actual failing gate. The Phase 6 witness contract cannot enforce when the witness lives in a sub-skill's chat that the parent never sees. Phase 6 verification — mandatory sub-agent delegation: the no-sub-agent rule above governs Phase 1-5 EXECUTION (operator visibility). Phase 6 VERIFICATION is the opposite: it MUST run in a fresh-context sub-agent, because shared-context executor-as-verifier has empirically fabricated PASS verdicts against failing gates despite repeated rounds of strengthened witness language. Same principle (operator chat carries the truth), opposite mechanism (visibility for execution; bias-isolation for verification). Scoped strictly to Phase 6 verification — see phase_prompts/phase6.md + phase_prompts/phase6_auditor.md. Feature H persona pass — the second sanctioned exception: the automatic persona validation pass at the Phase 2→3 boundary (bin/persona_apply.run_feature_h) MAY spawn its fresh-context, tool-restricted personas — it is a bounded, operator-visible validation remediation (mandatory quality/expert_review_summary.json, every change agent-validation-tagged + revertible, opt-out), not a delegated phase, so neither failure mode (a) nor (b) applies. Scoped strictly to that pass (see the EXCEPTION block above); all OTHER Phase 1–5 sub-agent delegation stays forbidden.
2. Don't patch QPB source mid-run. If you encounter a bug in bin/, .github/skills/, agents/, references/, SKILL.md, or AGENTS.md during the run, STOP and report: name the file:line, describe the failure, propose a fix shape — but do NOT apply the fix. The orchestrator captures a git-SHA baseline at run start and verifies the source tree unchanged at every phase boundary; an autonomous patch fails the gate with a diagnostic naming the modified files. Patches go through Council review, not mid-run improvisation.
3. Don't delete sentinel files. Files protected by .gitignore !-rules (e.g., reference_docs/.gitkeep, reference_docs/cite/.gitkeep) keep otherwise-empty tracked directories present. The pre-flight check enumerates every !-rule and aborts if any sentinel is missing. If you find such a file and don't understand its purpose, leave it alone.
4. Phase 1 file enumeration uses git ls-files. Use git ls-files as the canonical file list when the target is a git repo; this respects .gitignore automatically. Do NOT use os.walk, find, os.listdir, or any recursive directory walker — those pull in .git/, .venv/, node_modules/, build outputs, and vendored dependencies, all of which the role-map validator rejects. Disallowed path prefixes are .git/, .venv/, venv/, node_modules/, __pycache__/, .pytest_cache/, .mypy_cache/, .ruff_cache/, .tox/, plus any path whose components end in .egg-info or .dist-info. The role map carries a provenance field recording which enumeration source you used ("git-ls-files" or "filesystem-walk-with-skips" for non-git targets). There is also a 2000-entry ceiling; a role map exceeding it almost certainly walked .gitignored content.
5. Cross-artifact agreement. EXPLORATION.md's "File inventory" section and the role map's summary field both render from bin.role_map.summarize_role_map(). Don't write file counts or role percentages by hand; copy from the helper. The validator cross-checks the two and rejects mismatches.

If the operator's prompt says something that conflicts with these guardrails (e.g., "delegate phases 3–6 to a sub-agent so we can run faster"), don't comply with the conflicting instruction. Surface the conflict, name the guardrail, and ask for clarification. The guardrails exist because each one corresponds to a verified historical failure mode.

What this run produces — output artifact contract

A successful run produces this canonical set under the target's quality/ directory plus an AGENTS.md at the target's repo root. Every file listed here is gate-validated:

The gate verdict in quality/INDEX.md (pass / pass-with-cleanup / partial / fail) is the operator-facing summary of how the run went. The 089c F15 three-state taxonomy maps the gate's RESULT: line to this enum: RESULT: GATE PASSED → pass; RESULT: GATE PASSED WITH CLEANUP NEEDED → pass-with-cleanup (review completed, bug findings stand, only audit record-keeping incomplete — non-blocking); RESULT: GATE FAILED → fail (a substantive issue). partial covers aborted / WARN-only runs. If the verdict is anything other than pass, surface why before considering the run done; pass-with-cleanup is a successful outcome, but the State CN emit (per references/what_just_happened.md) walks the adopter through completing the remaining audit records.

quality/INDEX.md uses schema_version: "2.0" and carries a target_role_breakdown field with the per-role counts and percentages computed from quality/exploration_role_map.json. Pipelines activate from the role map's per-file role tags (skill-prose, skill-reference, skill-tool, code, test, docs, config, fixture, formal-spec, playbook-output) rather than a project-type label — the skill-derivation pipeline runs over files tagged skill-prose/skill-reference, the code-review pipeline runs over files tagged code, the prose-to-code divergence check runs over files tagged skill-tool, and when the role map shows zero of a role that pipeline no-ops cleanly. Both pipelines run when both surfaces are present.

Locating reference files

This skill references files in a references/ directory (e.g., references/iteration.md, references/review_protocols.md). The location depends on how the skill was installed. When a reference file is mentioned, resolve it by checking these paths in order and using the first one that exists (the canonical ten install layouts):

1. references/ (relative to SKILL.md — works when running from the skill directory or self-bootstrap)
2. .claude/skills/quality-playbook/references/ (Claude Code installation)
3. .github/skills/references/ (GitHub Copilot flat installation)
4. .cursor/skills/quality-playbook/references/ (Cursor installation)
5. .continue/skills/quality-playbook/references/ (Continue installation)
6. .github/skills/quality-playbook/references/ (alternate Copilot installation, nested)
7. .codex/skills/quality-playbook/references/ (Codex installation)
8. .windsurf/skills/quality-playbook/references/ (Windsurf installation)
9. .cline/skills/quality-playbook/references/ (Cline installation)
10. .aider/skills/quality-playbook/references/ (aider installation — aider does not auto-discover; tell aider to read SKILL.md explicitly)

All reference file mentions in this skill use the short form references/filename.md. If the relative path doesn't resolve, walk the fallback list above.

Council roster + adopter override

The Phase 4 Council of Three runs against a default 3-member roster defined in bin/council_config.py::DEFAULT_COUNCIL_MEMBERS — claude-opus-4.7, gpt-5.5, claude-sonnet-4.6. Adopters override per-operator via ~/.qpb/config.json (or $XDG_CONFIG_HOME/qpb/config.json); manage with python3 -m bin.qpb_config show|set|unset <key>. Per-run override via the --council-roster <m1,m2,m3> CLI flag takes highest precedence. See references/runners_and_models.md for the full override docs (precedence chain, model-availability behavior, falling back when a member is unavailable).

Why This Exists

Most software projects have tests, but few have a quality system. Tests check whether code works. A quality system answers harder questions: what does "working correctly" mean for this specific project? What are the ways it could fail that wouldn't be caught by tests? What should every developer (human or AI) know before touching this code?

Without a quality playbook, every new contributor (and every new AI session) starts from scratch — guessing at what matters, writing tests that look good but don't catch real bugs, and rediscovering failure modes that were already found and fixed months ago. A quality playbook makes the bar explicit, persistent, and inherited.

What This Skill Produces

Nine files that together form a repeatable quality system:

Plus output directories: quality/code_reviews/, quality/spec_audits/, quality/results/, quality/history/.

AGENTS.md at the target repo root is Generated by orchestrator after Phase 6 — not a Phase 2 deliverable (it carries a QPB sentinel marker so future runs detect QPB-managed copies). See the full gate-validated artifact table in references/artifact_contract.md.

The pipeline also generates supporting artifacts: quality/PROGRESS.md (phase-by-phase checkpoint log with cumulative BUG tracker), quality/CONTRACTS.md (behavioral contracts), quality/COVERAGE_MATRIX.md (traceability), quality/COMPLETENESS_REPORT.md (final gate), and quality/VERSION_HISTORY.md (review log). At the end of Phase 1 the documentation classification is always shown to the operator in plain language (doc_classification.classification_review) — which gathered documents are being used as authoritative sources and which are background — and the operator can confirm or correct it before Phase 2 derives anything against it; a correction is recorded in the operator-authored, content-keyed reference_docs/qpb_decisions.txt — the ONE override channel, which instruction 033 collapsed qpb_promote.txt, qpb_advisory_rescue.txt, qpb_authoritative.txt and cite/ placement into — and a re-run ingest makes a promoted document byte-citable (Design §8a; references/phase1_exploration_guide.md). Only the human operator can promote a document — document content, the classifier, and personas never can. The show prints in every mode; only the pause varies — pause only when an operator is stepping the run phase by phase and waiting, never in the continuous full-pipeline default, the single-pass prompt, or any unattended/runner-driven run. The requirements validation interview (references/requirements_interview.md) — an operator-driven, fitness-for-purpose review whose corrections write back to the manifest — is offered at the Phase 2 → Phase 3 boundary (the requirements are complete and Phases 3–6 build on them; Design §6), with one playbook-end reminder if the operator declined. It is opt-in and never auto-starts. At the same boundary, the Feature H persona validation pass (bin/persona_apply.run_feature_h; Design §8b, references/requirements_pipeline.md § E.9) runs automatically unless disabled for the run — fresh-context, tool-restricted domain-expert + security personas validate the spec, apply grounded (cited + byte-verified) fixes tagged agent-validation, and write an operator-visible review summary to quality/expert_review_summary.json. It is a remediator, not a gate (opt-out; the human interview is opt-in). Because it changes the operator's requirements, the end-of-Phase-2 message must disclose it in plain language (persona_apply.persona_review_disclosure, rendered into the State P2 block after the pass runs — instruction 031): reviewers ran, what they changed, where to read it, that it can be undone; nothing is added when the pass did not run. Spawning its personas is a sanctioned exception to the no-sub-agent guardrail (the second EXCEPTION block in the Mode A walkthrough, sibling to Phase 6 verification): a bounded, operator-visible validation remediation, not a delegated phase — so a faithful agent runs it here rather than disabling it.

The two critical deliverables are the requirements file and the functional test file. The requirements file (quality/REQUIREMENTS.md) feeds the code review protocol's verification and consistency passes — it's what makes the code review catch more than structural anomalies. The functional test file (named for the project's language and test framework conventions) is the automated safety net. The Markdown protocols are documentation for humans and AI agents.

See references/artifact_contract.md for the complete gate-validated artifact contract (the full artifact table, the canonical sidecar-JSON examples, and the run-metadata schema).

How to Use

The playbook is designed to run one phase at a time. Each phase runs in its own session with a clean context window, producing files on disk that the next phase reads. This gives much better results than running all phases at once — each phase gets the full context window for deep analysis instead of competing for space with other phases.

Default behavior: run Phase 1 only. When someone says "run the quality playbook" or "execute the quality playbook," run Phase 1 (Explore) and stop. After Phase 1 completes, tell the user what happened and what to say next. The user drives each phase forward explicitly.

Interactive protocol — how to guide the user

After every phase and every iteration, STOP and print guidance. Use a # header so it's prominent in the chat. The guidance must include: what just happened (one line), what the key outputs are, and the exact prompt to continue. See the end-of-phase messages defined after each phase section below.

If the user says "keep going", "continue", "next phase", "next", or anything similar, run the next phase in sequence. If all phases are complete, suggest the first iteration strategy (gap). If an iteration just finished, suggest the next strategy in the recommended cycle.

If the user says "run all phases", "run everything", or "run the full pipeline", run all phases sequentially in a single session. This uses more context but some users prefer it.

If the user asks "help", "how does this work", "what is this", or any similar phrasing, respond with this explanation (adapt the wording naturally, don't copy verbatim):

The Quality Playbook finds bugs that structural code review alone can't catch — the 35% of real defects that require understanding what the code is supposed to do. It works phase by phase:

• Phase 1 (Explore): Understand the codebase — architecture, risks, failure modes, specifications
• Phase 2 (Generate): Produce quality artifacts — requirements, tests, review protocols
• Phase 3 (Code Review): Three-pass review with regression tests for every confirmed bug
• Phase 4 (Spec Audit): Three independent AI auditors check the code against requirements
• Phase 5 (Reconciliation): Close the loop — TDD red-green verification for every bug
• Phase 6 (Verify): Self-check benchmarks validate all generated artifacts

After the numbered phases complete, you can run iteration strategies (gap, unfiltered, parity, adversarial) to find additional bugs — iterations typically add 40-60% more confirmed bugs on top of the baseline.

The playbook works best when you provide documentation alongside the code — specs, API docs, design documents, community documentation. It also gets significantly better results when you run each phase separately rather than all at once.

To get started, say: "Run the quality playbook on this project."

If the user asks "what happened", "what's going on", "where are we", or "what should I do next", read quality/PROGRESS.md and give them a concise status update: which phases are complete, how many bugs found so far, and what the next step is.

Documentation warning

At the start of Phase 1, before exploring any code, check for documentation. Look for directories named docs/, reference_docs/, doc/, documentation/, or any gathered documentation files. Also check if the user mentioned documentation in their prompt.

If no documentation is found, print this warning immediately (before proceeding):

Important: No project documentation found. The quality playbook works without documentation, but it finds significantly more bugs — and higher-confidence bugs — when you provide specs, API docs, design documents, or community documentation. In controlled experiments, documentation-enriched runs found different and better bugs than code-only baselines.

If you have documentation available, you can add it to a reference_docs/ directory and re-run Phase 1. Otherwise, I'll proceed with code-only analysis.

Then proceed with Phase 1 — don't block on this, just make sure the user sees the warning.

Doc gathering (pre-audit)

QPB derives a project's invariants from background documentation in its reference_docs/ directory — without it, QPB falls back to source-only evidence (Tier 3) and finds only shallow defects. QPB knows how to gather these docs itself.

When the user asks to gather background documentation, prepare reference_docs/, collect pre-audit context, or any equivalent ("get the docs ready for an audit of X", "build the reference corpus for X"), follow the protocol in references/DOC_GATHERING_PROMPT.md. Open that file, find the section "## The prompt (copy everything in this block)", and follow the boxed instructions inside the triple-backtick fences step by step (Step 0 grounding through Step 4 source-summary handback).

The same protocol works in two modes — the boxed prompt handles both without modification: when QPB is installed (you are now), Step 0 option (c) reads the local ai_context/TOOLKIT.md; when pasted into a fresh AI chat outside QPB, Step 0 option (a) handles the URL fetch.

Running a specific phase

The user can request any individual phase:

Run quality playbook phase 1.
Run quality playbook phase 3 — code review.
Run phase 5 reconciliation.

When running a specific phase, check that its prerequisites exist (e.g., Phase 3 requires Phase 2 artifacts). If prerequisites are missing, tell the user which phases need to run first.

Iteration mode — improve on a previous run

Use this when a previous playbook run exists and you want to find additional bugs. Iteration mode replaces Phase 1's from-scratch exploration with a targeted exploration using one of five strategies, then merges findings with the previous run and re-runs Phases 2–6 against the combined results.

When to use iteration mode: After a complete playbook run, when you believe the codebase has more bugs than the first run found. This is especially effective for large codebases where a single run can only cover 3–5 subsystems, and for library/framework codebases where different exploration paths find different bug classes.

Read references/iteration.md for detailed strategy instructions. That file contains the full operational detail for each strategy, shared rules, merge steps, and the completion gate. The summary below describes when to use each strategy.

TDD applies to iteration runs. Every newly confirmed bug in an iteration run must go through the full TDD red-green cycle and produce quality/results/BUG-NNN.red.log (and .green.log if a fix patch exists). The quality gate enforces this — missing logs cause FAIL. See references/iteration.md shared rule 5 and the TDD Log Closure Gate in Phase 5.

Iteration strategies. The user selects a strategy by naming it in the prompt. If no strategy is named, default to gap.

Run the next iteration of the quality playbook.                          # default: gap strategy
Run the next iteration of the quality playbook using the gap strategy.
Run the next iteration using the unfiltered strategy.
Run the next iteration using the parity strategy.
Run an iteration using the adversarial strategy.

Recommended cycle: gap → unfiltered → parity → adversarial. Each strategy finds different bug classes:

• gap (default) — Scan previous coverage, explore uncovered subsystems and thin sections. Best when the first run was structurally sound but only covered a subset of the codebase.
• unfiltered — Pure domain-driven exploration with no structural constraints. No pattern templates, no applicability matrices, no section format requirements. Recovers bugs that structured exploration suppresses.
• parity — Systematically enumerate parallel implementations of the same contract (transport variants, fallback chains, setup-vs-reset paths) and diff them for inconsistencies. Finds bugs that only emerge from cross-path comparison.
• adversarial — Re-investigate dismissed/demoted triage findings and challenge thin SATISFIED verdicts. Recovers Type II errors from conservative triage.
• all — Runner-level convenience: executes gap → unfiltered → parity → adversarial in sequence, each as a separate agent session. Stops early if a strategy finds zero new bugs.

Phase-by-phase execution

Each phase produces files on disk that the next phase reads. This is how context transfers between phases — through files, not through conversation history. The key handoff files are:

• quality/EXPLORATION.md — Phase 1 writes this, Phase 2 reads it. Contains everything Phase 2 needs to generate artifacts without re-exploring the codebase.
• quality/PROGRESS.md — Updated after every phase. Cumulative BUG tracker ensures no finding is lost.
• Generated artifacts (REQUIREMENTS.md, CONTRACTS.md, etc.) — Phase 2 writes these, Phases 3–5 read them to run reviews, audits, and reconciliation.

The pattern for each phase boundary: finish the current phase, write everything to disk, then print the end-of-phase message and stop. When the user starts the next phase, read back the files you need before proceeding. This "write then read" cycle is the phase boundary — it lets you drop exploration context from working memory before loading review context, for example.

Phase 0 entry contract. The VERY first Bash call you make after the skill is invoked — before pwd, before ls, before any other Bash command — MUST be:

python3 <install_root>/bin/qpb_validate.py .

This is the Phase 0 entry contract first-probe. The phase0_first_probe assertion in the harness grading checks that qpb_validate.py is the LITERAL first Bash call — the first probe MUST be qpb_validate, before pwd, before ls, before any other Bash invocation.

If qpb_validate returns status=blocked (the target has both .claude and .github markers and you must disambiguate), DO NOT continue with any other tool call until you've re-invoked qpb_validate with the explicit --ai-tool=<tool> flag and confirmed it returns status=ok. Resolving the block is the only legitimate next action.

Phase 0 entry contract (gitignore remediation). After qpb_validate returns status=ok and before any artifact Writes, you must add quality/ to .gitignore in the target repo. If the file does not exist, create it; if it exists but does not contain a quality/ line, append quality/ on its own line. The gitignore_remediation_followed assertion in the harness grading verifies this step ran to completion. Do not skip this step — it prevents the playbook's own artifacts from polluting the target repo's git history.

Phase sentinel markers. After qpb_validate returns status=ok and the gitignore remediation lands, the next tool call you make MUST be the first qpb_phase emission:

python3 <install_root>/bin/qpb_phase.py 1 start

This is non-negotiable. The Test Harness status layer uses this sentinel to track which phase your run is in; without it, observers see your run as phase=— for 5-10 minutes while you do real work. Resolve <install_root> via the 10-layout fallback list documented below.

At each subsequent phase boundary:

• On phase entry: python3 <install_root>/bin/qpb_phase.py <n> start (no --note). This MUST be the first tool call of that phase.
• On phase exit: python3 <install_root>/bin/qpb_phase.py <n> done --note "<1-3 sentence summary of what happened in this phase>". The note is your own free-text summary — what you did, what you found, what's notable. No rigid schema.

Tool-use protocol — parallel-call hygiene. Phase-artifact Writes — REQUIREMENTS.md, CONTRACTS.md, BUGS.md, QUALITY.md, COVERAGE_MATRIX.md, COMPLETENESS_REPORT.md, RUN_CODE_REVIEW.md, RUN_SPEC_AUDIT.md, RUN_TDD_TESTS.md, RUN_INTEGRATION_TESTS.md, and tdd-results.json — must NEVER be issued in a parallel batch with other tool calls. Each phase-artifact Write must be its own assistant turn — each phase-artifact Write must be a standalone tool call.

After every artifact Write, your VERY NEXT tool call must immediately Read the same file path to verify the file exists on disk. If the Read returns "file not found," the previous Write was cancelled (typically because a sibling Bash or other tool call in the same parallel batch errored, and Claude's parallel-call cancellation propagates the failure to the Write). When this happens you must retry the Write as a standalone tool call in its own turn.

This is non-negotiable. Agents that issued Write+Bash in parallel batches have been observed to see the Write silently cancelled when the Bash sibling errored — artifacts go missing, the Phase 6 gate FAILs with "missing artifact" issues, and the agent often misdiagnoses the symptom as "tool output delivered out of order" rather than "Write cancelled by sibling failure."

Each invocation prints exactly one ::QPB:: {json} line; the helper is install-bundled. The sentinel is observability only — it does NOT change any phase output, gate verdict, or grading.

Write your Phase 1 exploration findings to quality/EXPLORATION.md before proceeding. This file is mandatory in all modes. Make it thorough: domain identification, architecture map, existing tests, specification summary, quality risks, skeleton/dispatch analysis, derived requirements (REQ-NNN), and derived use cases (UC-NN). Everything Phase 2 needs to generate artifacts must be in this file.

The discipline of writing exploration findings to disk is what forces thorough analysis. Without it, the model keeps vague impressions in working memory and produces broad, abstract requirements that miss function-level defects. Writing forces specificity: file paths, line numbers, exact function names, concrete behavioral rules. That specificity is what makes requirements precise enough to catch bugs during code review.

Run-state instrumentation (write events as you go)

Two files in quality/ track this run's state across the filesystem so the run is observable in flight, resumable across crashes, and auditable afterward. Maintain both throughout the run.

• quality/run_state.jsonl — append-only machine-readable event log. One JSON object per line. The orchestrator and any monitor reads this file to know exactly where the run is.
• quality/PROGRESS.md — human-readable status file, atomically rewritten on every event.

Authoritative schema: references/run_state_schema.md. Read it once at run start; it defines the full event taxonomy, required fields, cross-validation rules, and PROGRESS.md format.

The run initialization sequence, per-phase events (phase_start/phase_end), the Phase-1/4/5/6 sub-events (pattern_walked/pass_started/gate_check/run_end), the phase-end cross-validation rules, resume semantics, and the PROGRESS.md format are all defined in the authoritative references/run_state_schema.md (named above). bin/run_state_lib.py provides validate_phase_artifacts(quality_dir, phase) (cross-validation) and write_progress_md(...) (PROGRESS.md) as the programmatic enforcers.

Heartbeat emission (only when running under the harness)

If your dispatch prompt began with a HEARTBEAT_PATH=… / TASK_ID=… block (you were launched by the quality-playbook-harness orchestrator), emit liveness heartbeats so the orchestrator can track progress and detect stalls. If there is no such block (a normal interactive run), skip this section entirely — or pass --mode-a-noop and the calls silently no-op. Use the absolute `HEARTBEAT_P

Truncated - read the full file at https://github.com/andrewstellman/quality-playbook/blob/11ba61d5189afd210dd7876fa33c9bfb5d77129b/SKILL.md.