Agent skill · data analytics · github
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'.
Why this skill is useful
Adds a comprehensive multi-phase quality engineering workflow that includes automated testing, code review protocols, and bug tracking that the AI cannot generate on its own.
What it needs
Requires python installed locally. About 12k tokens when loaded. Last updated 2026-08-07. 37,534 stars on the source repository.
What this skill does
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.referencedocsingest <target> to walk referencedocs/ — cite/ files produce quality/formaldocsmanifest.json records; top-level files are loaded as Tier 4 context via referencedocsingest.loadtier4context(<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/citationsemanticcheck.json. Write regression tests for net-new findings. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills github/quality-playbook