Individual skill
quality-playbook
Run a complete quality engineering audit on any codebase. Derives behavioral requirements from the code, generates spec-traced functional tests, runs a three-pass code review with regression tests, executes a multi-model spec audit (Council of Three), and produces a consolidated bug report with TDD-verified patches. Finds the 35% of real defects that structural code review alone cannot catch. Works with any language. Trigger on 'quality playbook', 'spec audit', 'Council of Three', 'fitness-to-purpose', or 'coverage theater'.
skills/quality-playbook/SKILL.md
Source commit edbecfa9099c4addae1f500cca1ac067204b30fd
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Network or broad shell
- Last checked
- Nov 4, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
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Repository context
- Repository stars (not skill ratings)
- 40,891
- Repository last updated
- Nov 6, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- ai Repository topic
- github-copilot Repository topic
- prompt-engineering Repository topic
- hacktoberfest Repository topic
- agent-skills Repository topic
- agents Repository topic
- awesome Repository topic
- custom-agents Repository topic
- engineering Derived category
- security Derived category
- testing Derived category
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Read excerpt
# 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.