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sw:increment-quality-judge-v2

📁 anton-abyzov/specweave 📅 10 days ago
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npx skills add https://github.com/anton-abyzov/specweave --skill sw:increment-quality-judge-v2

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opencode 1
codex 1
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Skill 文档

Increment Quality Judge v2.0

LLM-as-Judge Pattern Implementation

AI-powered quality assessment using the LLM-as-Judge pattern – an established AI/ML evaluation technique where an LLM evaluates outputs with chain-of-thought reasoning, BMAD-pattern risk scoring, and formal quality gate decisions (PASS/CONCERNS/FAIL).

LLM-as-Judge: What It Is

LLM-as-Judge (LaaJ) is a recognized pattern in AI/ML evaluation where a large language model assesses quality using structured reasoning.

┌─────────────────────────────────────────────────────────────┐
│                 LLM-as-Judge Pattern                        │
├─────────────────────────────────────────────────────────────┤
│  Input:  spec.md, plan.md, tasks.md                        │
│                                                             │
│  Process:                                                   │
│  ┌─────────────────────────────────────────────────────┐   │
│  │ <thinking>                                          │   │
│  │   1. Read and understand the specification          │   │
│  │   2. Evaluate against 7 quality dimensions          │   │
│  │   3. Identify risks (P×I scoring)                   │   │
│  │   4. Form evidence-based verdict                    │   │
│  │ </thinking>                                         │   │
│  └─────────────────────────────────────────────────────┘   │
│                                                             │
│  Output: Structured verdict with:                          │
│  • Dimension scores (0-100)                                │
│  • Risk assessment (CRITICAL/HIGH/MEDIUM/LOW)              │
│  • Quality gate decision (PASS/CONCERNS/FAIL)              │
│  • Actionable recommendations                              │
└─────────────────────────────────────────────────────────────┘

Why LLM-as-Judge works:

  • Consistency: Uniform evaluation criteria without human fatigue
  • Reasoning: Chain-of-thought explains WHY something is an issue
  • Scalability: Evaluates in seconds vs hours of manual review
  • Industry standard: Used by OpenAI, Anthropic, Google for AI evals

References:

  • “Judging LLM-as-a-Judge” (NeurIPS 2023)
  • LMSYS Chatbot Arena evaluation methodology
  • AlpacaEval, MT-Bench frameworks

IMPORTANT: This is a SKILL (Not an Agent)

DO NOT try to spawn this as an agent via Task tool.

This is a skill that auto-activates when you discuss quality assessment. To run quality assessment:

# Use the CLI command directly
specweave qa 0001 --pre

# Or use the slash command
/sw:qa 0001

The skill provides guidance and documentation. The CLI handles execution.

Why no agent? Having both a skill and agent with the same name (increment-quality-judge-v2) caused Claude to incorrectly construct agent type names. The skill-only approach eliminates this confusion.

What’s New in v2.0

  1. Risk Assessment Dimension – Probability × Impact scoring (0-10 scale, BMAD pattern)
  2. Quality Gate Decisions – Formal PASS/CONCERNS/FAIL with thresholds
  3. NFR Checking – Non-functional requirements (performance, security, scalability)
  4. Enhanced Output – Blockers, concerns, recommendations with actionable mitigations
  5. 7 Dimensions – Added “Risk” to the existing 6 dimensions

Purpose

Provide comprehensive quality assessment that goes beyond structural validation to evaluate:

  • ✅ Specification quality (6 dimensions)
  • ✅ Risk levels (BMAD P×I scoring) – NEW!
  • ✅ Quality gate readiness (PASS/CONCERNS/FAIL) – NEW!

When to Use

Auto-activates for:

  • /qa {increment-id} command
  • /qa {increment-id} --pre (pre-implementation check)
  • /qa {increment-id} --gate (quality gate check)
  • Natural language: “assess quality of increment 0001”

Keywords:

  • validate quality, quality check, assess spec
  • evaluate increment, spec review, quality score
  • risk assessment, qa check, quality gate
  • PASS/CONCERNS/FAIL

Evaluation Dimensions (7 total, was 6)

dimensions:
  clarity:
    weight: 0.18 # was 0.20
    criteria:
      - "Is the problem statement clear?"
      - "Are objectives well-defined?"
      - "Is terminology consistent?"

  testability:
    weight: 0.22 # was 0.25
    criteria:
      - "Are acceptance criteria testable?"
      - "Can success be measured objectively?"
      - "Are edge cases identifiable?"

  completeness:
    weight: 0.18 # was 0.20
    criteria:
      - "Are all requirements addressed?"
      - "Is error handling specified?"
      - "Are non-functional requirements included?"

  feasibility:
    weight: 0.13 # was 0.15
    criteria:
      - "Is the architecture scalable?"
      - "Are technical constraints realistic?"
      - "Is timeline achievable?"

  maintainability:
    weight: 0.09 # was 0.10
    criteria:
      - "Is design modular?"
      - "Are extension points identified?"
      - "Is technical debt addressed?"

  edge_cases:
    weight: 0.09 # was 0.10
    criteria:
      - "Are failure scenarios covered?"
      - "Are performance limits specified?"
      - "Are security considerations included?"

  # NEW: Risk Assessment (BMAD pattern)
  risk:
    weight: 0.11 # NEW!
    criteria:
      - "Are security risks identified and mitigated?"
      - "Are technical risks (scalability, performance) addressed?"
      - "Are implementation risks (complexity, dependencies) managed?"
      - "Are operational risks (monitoring, support) considered?"

Risk Assessment (BMAD Pattern) – NEW!

Risk Scoring Formula

Risk Score = Probability × Impact

Probability (0.0-1.0):
- 0.0-0.3: Low (unlikely to occur)
- 0.4-0.6: Medium (may occur)
- 0.7-1.0: High (likely to occur)

Impact (1-10):
- 1-3: Minor (cosmetic, no user impact)
- 4-6: Moderate (some impact, workaround exists)
- 7-9: Major (significant impact, no workaround)
- 10: Critical (system failure, data loss, security breach)

Final Score (0.0-10.0):
- 9.0-10.0: CRITICAL risk (FAIL quality gate)
- 6.0-8.9: HIGH risk (CONCERNS quality gate)
- 3.0-5.9: MEDIUM risk (PASS with monitoring)
- 0.0-2.9: LOW risk (PASS)

Risk Categories

  1. Security Risks

    • OWASP Top 10 vulnerabilities
    • Data exposure, authentication, authorization
    • Cryptographic failures

  2. Technical Risks

    • Architecture complexity, scalability bottlenecks
    • Performance issues, technical debt

  3. Implementation Risks

    • Tight timeline, external dependencies
    • Technical complexity

  4. Operational Risks

    • Lack of monitoring, difficult to maintain
    • Poor documentation

Risk Assessment Prompt

You are evaluating SOFTWARE RISKS for an increment using BMAD's Probability × Impact scoring.

Read increment files:
- .specweave/increments/{id}/spec.md
- .specweave/increments/{id}/plan.md

For EACH risk you identify:

1. **Calculate PROBABILITY** (0.0-1.0)
   - Based on spec clarity, past experience, complexity
   - Low: 0.2, Medium: 0.5, High: 0.8

2. **Calculate IMPACT** (1-10)
   - 10 = Critical (security breach, data loss, system failure)
   - 7-9 = Major (significant user impact, no workaround)
   - 4-6 = Moderate (some impact, workaround exists)
   - 1-3 = Minor (cosmetic, no user impact)

3. **Calculate RISK SCORE** = Probability × Impact

4. **Provide MITIGATION** strategy

5. **Link to ACCEPTANCE CRITERIA** (if applicable)

Output format (JSON):
{
  "risks": [
    {
      "id": "RISK-001",
      "category": "security",
      "title": "Password storage not specified",
      "description": "Spec doesn't mention password hashing algorithm",
      "probability": 0.9,
      "impact": 10,
      "score": 9.0,
      "severity": "CRITICAL",
      "mitigation": "Use bcrypt or Argon2, never plain text",
      "location": "spec.md, Authentication section",
      "acceptance_criteria": "AC-US1-01"
    }
  ],
  "overall_risk_score": 7.5,
  "dimension_score": 0.35
}

Quality Gate Decisions – NEW!

Decision Logic

enum QualityGateDecision {
  PASS = "PASS",          // Ready for production
  CONCERNS = "CONCERNS",  // Issues found, should address
  FAIL = "FAIL"           // Blockers, must fix
}

Thresholds (BMAD pattern):

FAIL if any:
- Risk score ≥ 9.0 (CRITICAL)
- Test coverage < 60%
- Spec quality < 50
- Critical security vulnerabilities ≥ 1

CONCERNS if any:
- Risk score 6.0-8.9 (HIGH)
- Test coverage < 80%
- Spec quality < 70
- High security vulnerabilities ≥ 1

PASS otherwise

Output Example

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
QA ASSESSMENT: Increment 0008-user-authentication
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Overall Score: 82/100 (GOOD) ✓

Dimension Scores:
  Clarity:         90/100 ✓✓
  Testability:     75/100 ⚠️
  Completeness:    88/100 ✓
  Feasibility:     85/100 ✓
  Maintainability: 80/100 ✓
  Edge Cases:      70/100 ⚠️
  Risk Assessment: 65/100 ⚠️  (7.2/10 risk score)

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
RISKS IDENTIFIED (3)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

🔴 RISK-001: CRITICAL (9.0/10)
   Category: Security
   Title: Password storage implementation
   Description: Spec doesn't specify password hashing
   Probability: 0.9 (High) × Impact: 10 (Critical)
   Location: spec.md, Authentication section
   Mitigation: Use bcrypt/Argon2, never plain text
   AC: AC-US1-01

🟡 RISK-002: HIGH (6.0/10)
   Category: Security
   Title: Rate limiting not specified
   Description: No brute-force protection mentioned
   Probability: 0.6 (Medium) × Impact: 10 (Critical)
   Location: spec.md, Security section
   Mitigation: Add 5 failed attempts → 15 min lockout
   AC: AC-US1-03

🟢 RISK-003: LOW (2.4/10)
   Category: Technical
   Title: Session storage scalability
   Description: Plan uses in-memory sessions
   Probability: 0.4 (Medium) × Impact: 6 (Moderate)
   Location: plan.md, Architecture section
   Mitigation: Use Redis for session store

Overall Risk Score: 7.2/10 (MEDIUM-HIGH)

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
QUALITY GATE DECISION
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

🟡 CONCERNS (Not Ready for Production)

Blockers (MUST FIX):
  1. 🔴 CRITICAL RISK: Password storage (Risk ≥9)
     → Add task: "Implement bcrypt password hashing"

Concerns (SHOULD FIX):
  2. 🟡 HIGH RISK: Rate limiting not specified (Risk ≥6)
     → Update spec.md: Add rate limiting section
     → Add E2E test for rate limiting

  3. ⚠️  Testability: 75/100 (target: 80+)
     → Make acceptance criteria more measurable

Recommendations (NICE TO FIX):
  4. Edge cases: 70/100
     → Add error handling scenarios
  5. Session scalability
     → Consider Redis for session store

Decision: Address 1 blocker before proceeding

Would you like to:
  [E] Export blockers to tasks.md
  [U] Update spec.md with fixes (experimental)
  [C] Continue without changes

Workflow Integration

Quick Mode (Default)

User: /sw:qa 0001

Step 1: Rule-based validation (120 checks) - FREE, FAST
├── If FAILED → Stop, show errors
└── If PASSED → Continue

Step 2: AI Quality Assessment (Quick)
├── Spec quality (6 dimensions)
├── Risk assessment (BMAD P×I)
└── Quality gate decision (PASS/CONCERNS/FAIL)

Output: Enhanced report with risks and gate decision

Pre-Implementation Mode

User: /sw:qa 0001 --pre

Checks:
✅ Spec quality (clarity, testability, completeness)
✅ Risk assessment (identify issues early)
✅ Architecture review (plan.md soundness)
✅ Test strategy (test plan in tasks.md)

Gate decision before implementation starts

Quality Gate Mode

User: /sw:qa 0001 --gate

Comprehensive checks:
✅ All pre-implementation checks
✅ Test coverage (AC-ID coverage, gaps)
✅ E2E test coverage
✅ Documentation completeness

Final gate decision before closing increment

Enhanced Scoring Algorithm

Step 1: Dimension Evaluation (7 dimensions)

For each dimension (including NEW risk dimension), use Chain-of-Thought prompting:

<thinking>
1. Read spec.md thoroughly
2. For risk dimension specifically:
   - Identify all risks (security, technical, implementation, operational)
   - For each risk: calculate P, I, Score
   - Group by category
   - Calculate overall risk score
3. For other dimensions: evaluate criteria as before
4. Score 0.00-1.00
5. Identify issues
6. Provide suggestions
</thinking>

Score: 0.XX

Step 2: Weighted Overall Score (NEW weights)

overall_score =
  (clarity * 0.18) +
  (testability * 0.22) +
  (completeness * 0.18) +
  (feasibility * 0.13) +
  (maintainability * 0.09) +
  (edge_cases * 0.09) +
  (risk * 0.11)  // NEW!

Step 3: Quality Gate Decision

gate_decision = decide({
  spec_quality: overall_score,
  risk_score: risk_assessment.overall_risk_score,
  test_coverage: test_coverage.percentage, // if available
  security_audit: security_audit  // if available
})

Token Usage

Estimated per increment (Quick mode):

  • Small spec (<100 lines): 2,500 tokens ($0.025)
  • Medium spec (100-250 lines): 3,500 tokens ($0.035)
  • Large spec (>250 lines): 5,000 tokens ($0.050)

Cost increase from v1.0: +25% (added risk assessment dimension)

Optimization:

  • Only evaluate spec.md + plan.md for risks
  • Cache risk patterns for 5 min
  • Skip risk assessment if spec < 50 lines (too small to assess)

Configuration

{
  "qa": {
    "qualityGateThresholds": {
      "fail": {
        "riskScore": 9.0,
        "testCoverage": 60,
        "specQuality": 50,
        "criticalVulnerabilities": 1
      },
      "concerns": {
        "riskScore": 6.0,
        "testCoverage": 80,
        "specQuality": 70,
        "highVulnerabilities": 1
      }
    },
    "dimensions": {
      "risk": {
        "enabled": true,
        "weight": 0.11
      }
    }
  }
}

Migration from v1.0

v1.0 (6 dimensions):

  • Clarity, Testability, Completeness, Feasibility, Maintainability, Edge Cases

v2.0 (7 dimensions, NEW: Risk):

  • All v1.0 dimensions + Risk Assessment
  • Weights adjusted to accommodate new dimension
  • Quality gate decisions added
  • BMAD risk scoring added

Backward Compatibility:

  • v1.0 skills still work (auto-upgrade to v2.0 if risk assessment enabled)
  • Existing scores rescaled to new weights automatically
  • Can disable risk assessment in config to revert to v1.0 behavior

Best Practices

  1. Run early and often: Use --pre mode before implementation
  2. Fix blockers immediately: Don’t proceed if FAIL
  3. Address concerns before release: CONCERNS = should fix
  4. Use risk scores to prioritize: Fix CRITICAL risks first
  5. Export to tasks.md: Convert blockers/concerns to actionable tasks

Limitations

What quality-judge v2.0 CAN’T do:

  • ❌ Understand domain-specific compliance (HIPAA, PCI-DSS)
  • ❌ Verify technical feasibility with actual codebase
  • ❌ Replace human expertise and security audits
  • ❌ Predict actual probability without historical data

What quality-judge v2.0 CAN do:

  • ✅ Catch vague or ambiguous language
  • ✅ Identify missing security considerations (OWASP-based)
  • ✅ Spot untestable acceptance criteria
  • ✅ Suggest industry best practices
  • ✅ Flag missing edge cases
  • ✅ Assess risks systematically (BMAD pattern) – NEW!
  • ✅ Provide formal quality gate decisions – NEW!

Summary

increment-quality-judge v2.0 adds comprehensive risk assessment and quality gate decisions:

✅ Risk assessment (BMAD P×I scoring, 0-10 scale) ✅ Quality gate decisions (PASS/CONCERNS/FAIL with thresholds) ✅ 7 dimensions (added “Risk” to existing 6) ✅ NFR checking (performance, security, scalability) ✅ Enhanced output (blockers, concerns, recommendations) ✅ Chain-of-thought (LLM-as-Judge 2025 best practices) ✅ Backward compatible (can disable risk assessment)

Use it when: You want comprehensive quality assessment with risk scoring and formal gate decisions before implementation or release.

Skip it when: Quick iteration, tight token budget, or simple features where rule-based validation suffices.

Version: 2.0.0 Related: /sw:qa command, QAOrchestrator agent

Project-Specific Learnings

Before starting work, check for project-specific learnings:

# Check if skill memory exists for this skill
cat .specweave/skill-memories/increment-quality-judge-v2.md 2>/dev/null || echo "No project learnings yet"

Project learnings are automatically captured by the reflection system when corrections or patterns are identified during development. These learnings help you understand project-specific conventions and past decisions.

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