AI SummaryA quality assessment skill that runs automated validation, AI-based evaluation, and risk scoring on SpecWeave increments to make informed quality gate decisions. Developers using SpecWeave will benefit from automated quality checks and structured decision-making.
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to install the "qa" skill in my project. Please run this command in my terminal: # Install skill into your project (2 files) mkdir -p .claude/skills/qa && curl --retry 3 --retry-delay 2 --retry-all-errors -o .claude/skills/qa/SKILL.md "https://raw.githubusercontent.com/majiayu000/claude-skill-registry/main/skills/data/qa/SKILL.md" && curl --retry 3 --retry-delay 2 --retry-all-errors -o .claude/skills/qa/metadata.json "https://raw.githubusercontent.com/majiayu000/claude-skill-registry/main/skills/data/qa/metadata.json" Then restart Claude Code (or reload the window in Cursor) so the skill is picked up.
Description
Run quality assessment on a SpecWeave increment with risk scoring and quality gate decisions
Usage
`bash /sw:qa <increment-id> [options] `
/sw:qa - Quality Assessment Command
IMPORTANT: You MUST invoke the CLI specweave qa command using the Bash tool. The slash command provides guidance and orchestration only.
Purpose
Run comprehensive quality assessment on an increment using: • ✅ Gate 1: Rule-based validation (130+ automated checks) • ✅ Gate 2: LLM-as-Judge (AI quality assessment with chain-of-thought reasoning) • ✅ Gate 3: Risk scoring (BMAD Probability × Impact quantitative assessment) • ✅ Quality gate decisions (PASS/CONCERNS/FAIL)
LLM-as-Judge Pattern
This command implements the LLM-as-Judge pattern - an established AI/ML evaluation technique where an LLM evaluates outputs using structured reasoning. How it works: ` ┌─────────────────────────────────────────────────────────────┐ │ LLM-as-Judge Gate │ ├─────────────────────────────────────────────────────────────┤ │ Input: spec.md, plan.md, tasks.md │ │ │ │ Process: │ │ 1. Chain-of-thought analysis (7 dimensions) │ │ 2. Evidence-based scoring (0-100 per dimension) │ │ 3. Risk identification (BMAD P×I formula) │ │ 4. Formal verdict (PASS/CONCERNS/FAIL) │ │ │ │ Output: Structured quality report with: │ │ - Blockers (MUST fix) │ │ - Concerns (SHOULD fix) │ │ - Recommendations (NICE to fix) │ └─────────────────────────────────────────────────────────────┘ ` Why LLM-as-Judge? • Consistency: Applies uniform evaluation criteria • Depth: Catches nuanced issues humans might miss • Speed: ~30 seconds vs hours of manual review • Documented reasoning: Explains WHY something is an issue
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