Quality Playbook

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 v1.5.3 documentation intake first (python -m bin.reference_docs_ingest <target> to walk 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 v1.5.3 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 message to the user, 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 v1.5.6 — by Andrew Stellman https://github.com/andrewstellman/quality-playbook

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 — v1.5.4 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 surfaced by the 2026-04-30 bootstrap test.

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.

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.

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. The 2026-04-30 bootstrap test specifically failed on 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

Council 2026-04-30 P1-3: 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). This phase is a back-and-forth dialogue with the operator about the generated artifacts; it has no pre-baked prompt in phase_prompts/. After Phase 6 in Mode A, present the artifact summary table inline (see "What this run produces" below for the file list) and let the operator drive what to explore next conversationally — that IS Phase 7. There is no orchestrator subprocess to spawn.
• 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. Always invoke it as a Python module:

python3 -m bin.run_playbook <target>

Never invoke it script-style (python bin/run_playbook.py ...). The runtime guard exits with EX_USAGE=64 because relative imports require packaged execution.

<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 cost change vs. v1.5.3 (~5–10× the legacy "Phase 1 only" default). 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.

v1.5.4 mechanics (pointer-style, not duplicating the design doc)

What's new vs. v1.5.3, in pointer form (the canonical architecture lives in docs/design/QPB_v1.5.4_Design.md Part 1):

• Phase 1 produces quality/exploration_role_map.json — per-file role tagging done AI-driven during exploration. Each in-scope file gets a role from the taxonomy (skill-prose, skill-reference, skill-tool, code, test, docs, config, fixture, formal-spec, playbook-output). The role map drives every downstream pipeline-activation decision.
• INDEX.md uses schema_version: "2.0" with a target_role_breakdown field carrying the per-role counts and percentages. The v1.5.3 target_project_type enum is retired (legacy archives stay readable).
• Pipelines activate from the role map, not from a project-type label. The four-pass 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. When the role map shows zero of a role, that pipeline no-ops cleanly. There is no Code/Skill/Hybrid trichotomy — both pipelines run when both surfaces are present (the "always-Hybrid downstream" model).
• Archive directory is quality/previous_runs/ (was quality/runs/ in v1.5.3); legacy archives at the old path remain readable.
• End-of-Phase-6 reorganization moves intermediate artifacts under quality/workspace/ so the top-level quality/ directory is dominated by canonical deliverables (REQUIREMENTS.md, BUGS.md, etc.). The gate's path resolver reads from both layouts.

You don't need to re-derive any of this in your prompt-side reasoning; the orchestrator's prompts already encode it. If you encounter a phase prompt that conflicts with the architecture summarized here, follow the phase prompt — it's the canonical source for the per-phase contract.

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, or any "delegate phases 2–6 to a worker" pattern. The B-15 failure mode is real: 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. v1.5.4 prompts forbid this explicitly.
2. Don't patch QPB source mid-run. If you encounter a bug in bin/, .github/skills/, agents/, references/, SKILL.md, schemas.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 / partial / fail) is the operator-facing summary of how the run went. If it's anything other than pass, surface why before considering the run done.

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:

1. references/ (relative to SKILL.md — works when running from the skill directory)
2. .claude/skills/quality-playbook/references/ (Claude Code installation)
3. .github/skills/references/ (GitHub Copilot flat installation)
4. .github/skills/quality-playbook/references/ (alternate Copilot installation)

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.

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/.

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). Phase 7 can additionally generate quality/REVIEW_REQUIREMENTS.md (interactive review protocol) and quality/REFINE_REQUIREMENTS.md (refinement pass protocol) for iterative improvement.

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.

Complete Artifact Contract

The quality gate (quality_gate.py) validates these artifacts. If the gate checks for it, this skill must instruct its creation. This is the canonical list — any artifact not listed here should not be gate-enforced, and any gate check should trace to an artifact listed here.

Sidecar JSON lifecycle: Write all bug writeups before finalizing tdd-results.json — the sidecar's writeup_path field must point to an existing file, not a placeholder. Similarly, run integration tests and collect results before writing integration-results.json.

Sidecar JSON Canonical Examples

quality/results/tdd-results.json — the gate validates field names, not just presence:

{
  "schema_version": "1.1",
  "skill_version": "1.5.6",
  "date": "2026-04-12",
  "project": "repo-name",
  "bugs": [
    {
      "id": "BUG-001",
      "requirement": "REQ-003",
      "red_phase": "fail",
      "green_phase": "pass",
      "verdict": "TDD verified",
      "fix_patch_present": true,
      "writeup_path": "quality/writeups/BUG-001.md"
    }
  ],
  "summary": {
    "total": 3, "confirmed_open": 1, "red_failed": 0, "green_failed": 0, "verified": 2
  }
}

verdict must be one of: "TDD verified", "red failed", "green failed", "confirmed open", "deferred". date must be ISO 8601 (YYYY-MM-DD), not a placeholder, not in the future.

quality/results/integration-results.json:

{
  "schema_version": "1.1",
  "skill_version": "1.5.6",
  "date": "2026-04-12",
  "project": "repo-name",
  "recommendation": "SHIP",
  "groups": [{ "group": 1, "name": "Group 1", "use_cases": ["UC-01"], "result": "pass", "tests_passed": 3, "tests_failed": 0, "notes": "" }],
  "summary": { "total_groups": 12, "passed": 11, "failed": 1, "skipped": 0 },
  "uc_coverage": { "UC-01": "covered_pass", "UC-02": "not_mapped" }
}

recommendation must be one of: "SHIP", "FIX BEFORE MERGE", "BLOCK". uc_coverage maps UC identifiers from REQUIREMENTS.md to coverage status.

Run Metadata

Every playbook run creates a timestamped metadata file at quality/results/run-YYYY-MM-DDTHH-MM-SS.json. This enables multi-model comparison and run history tracking.

Lifecycle: Create this file at the start of Phase 1. Update phases_completed, bug_count, and end_time as each phase finishes. The final update happens after the terminal gate.

{
  "schema_version": "1.0",
  "skill_version": "1.5.6",
  "project": "repo-name",
  "model": "claude-sonnet-4-6",
  "model_provider": "anthropic",
  "runner": "claude-code",
  "start_time": "2026-04-16T10:30:00Z",
  "end_time": "2026-04-16T11:45:00Z",
  "duration_minutes": 75,
  "phases_completed": ["Phase 0b", "Phase 1", "Phase 2", "Phase 3", "Phase 4", "Phase 5"],
  "iterations_completed": ["gap", "unfiltered", "parity", "adversarial"],
  "bug_count": 12,
  "bug_severity": { "HIGH": 2, "MEDIUM": 5, "LOW": 5 },
  "gate_result": "PASS",
  "gate_fail_count": 0,
  "gate_warn_count": 2,
  "notes": ""
}

Required fields: schema_version, skill_version, project, model, start_time. All other fields are populated as the run progresses. model should be the exact model string (e.g., "claude-sonnet-4-6", "gpt-4.1", "claude-opus-4-6"). runner identifies the tool used to execute the playbook (e.g., "claude-code", "copilot-cli", "cursor", "cowork"). duration_minutes is computed from end_time - start_time. If the model or runner cannot be determined, use "unknown".

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.

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 → advers

…(truncated)