adaptationio/multi-ai-verification

Multi-AI Verification

Overview

multi-ai-verification provides comprehensive quality assurance through a 5-layer verification pyramid, from automated rules to LLM-as-judge evaluation.

Purpose: Multi-layer independent verification ensuring production-ready quality

Pattern: Task-based (5 independent verification operations, one per layer)

Key Innovation: 5-layer pyramid (95% automated at base → 0% at apex) with independent verification preventing bias and test gaming

Core Principles (validated by tri-AI research):

1. Multi-Layer Defense - 5 layers catch different types of issues
2. Independent Verification - Separate agent from implementation/testing
3. Progressive Automation - Automate what can be automated (95% → 0%)
4. Quality Scoring - Objective 0-100 scoring with ≥90 threshold
5. Actionable Feedback - 100% feedback is specific and actionable (What/Where/Why/How/Priority)

Quality Gates: All 5 layers must pass for production approval

---

When to Use

Use multi-ai-verification when:

• Final quality check before commit/deployment
• Independent code review (preventing bias)
• Security verification (OWASP, vulnerabilities)
• Comprehensive QA (all layers)
• Test quality verification (prevent gaming)
• Production readiness validation

---

Prerequisites

Required

• Code to verify (implementation complete)
• Tests available (for functional verification)
• Quality standards defined

Recommended

• multi-ai-testing - For generating/running tests
• multi-ai-implementation - For implementing fixes

Tools Available

• Linters (ESLint, Pylint)
• Type checkers (TypeScript, mypy)
• Coverage tools (c8, pytest-cov)
• Security scanners (Semgrep, Bandit)
• Test frameworks (Jest, pytest)

---

The 5-Layer Verification Pyramid

         Layer 5: Quality Scoring
         (LLM-as-Judge, 0-20% automated)
              /\
             /  \
        Layer 4: Integration
        (E2E, System, 20-30% automated)
          /      \
         /        \
    Layer 3: Visual
    (UI, Screenshots, 30-50% automated)
      /          \
     /            \
Layer 2: Functional
(Tests, Coverage, 60-80% automated)
  /              \
 /                \
Layer 1: Rules-Based
(Linting, Types, Schema, 95% automated)

Principle: Fail fast at automated layers (cheap, fast) before expensive LLM-as-judge evaluation

---

Verification Operations

Operation 1: Rules-Based Verification (Layer 1)

Purpose: Automated validation of code structure, formatting, types

Automation: 95% automated Speed: Seconds (fast feedback) Confidence: High (deterministic)

Process:

1. Schema Validation (if applicable):

   # Validate JSON/YAML against schemas
   ajv validate -s plan.schema.json -d plan.json
   ajv validate -s task.schema.json -d tasks/*.json
   

2. Linting:

   # JavaScript/TypeScript
   npx eslint src/**/*.{ts,tsx,js,jsx}
   
   # Python
   pylint src/**/*.py
   
   # Expected: Zero linting errors
   

3. Type Checking:

   # TypeScript
   npx tsc --noEmit
   
   # Python
   mypy src/
   
   # Expected: Zero type errors
   

4. Format Validation:

   # Check formatting
   npx prettier --check src/**/*.{ts,tsx}
   
   # Or auto-fix
   npx prettier --write src/**/*.{ts,tsx}
   

5. Security Scanning (SAST):

   # Static security analysis
   npx semgrep --config=auto src/
   
   # Or for Python
   bandit -r src/
   
   # Check for:
   # - Hardcoded secrets
   # - SQL injection risks
   # - XSS vulnerabilities
   # - Insecure dependencies
   

6. Generate Layer 1 Report:

   # Layer 1: Rules-Based Verification
   
   ## Schema Validation
   ✅ plan.json validates
   ✅ All task files validate
   
   ## Linting
   ✅ 0 linting errors
   ⚠️ 3 warnings (non-blocking)
   
   ## Type Checking
   ✅ 0 type errors
   
   ## Formatting
   ✅ All files formatted correctly
   
   ## Security Scan (SAST)
   ✅ No critical vulnerabilities
   ⚠️ 1 medium: Weak password hashing rounds (bcrypt)
   
   **Layer 1 Status**: ✅ PASS (0 critical issues)
   **Issues to Address**: 1 medium security issue
   

Outputs:

• Lint report (errors/warnings)
• Type check results
• Schema validation results
• Security scan findings
• Layer 1 status (PASS/FAIL)

Validation:

• [ ] All automated checks run
• [ ] Results documented
• [ ] Critical issues = 0 for PASS
• [ ] Actionable feedback for warnings

Time Estimate: 15-30 minutes (mostly automated)

Gate 1: ✅ PASS if no critical issues (warnings acceptable)

---

Operation 2: Functional Verification (Layer 2)

Purpose: Validate functionality through test execution and coverage

Automation: 60-80% automated Speed: Minutes (medium feedback) Confidence: High (measurable outcomes)

Process:

1. Execute Complete Test Suite:

   # Run all tests with coverage
   npm test -- --coverage --verbose
   
   # Capture results
   # - Tests passed/failed
   # - Coverage metrics
   # - Execution time
   

2. Validate Example Code (from documentation):

   # Extract examples from SKILL.md
   # Execute each example automatically
   # Verify outputs match expected
   
   # Target: ≥90% examples work
   

3. Check Coverage:

   # Coverage Report
   
   **Line Coverage**: 87% ✅ (gate: ≥80%)
   **Branch Coverage**: 82% ✅
   **Function Coverage**: 92% ✅
   **Path Coverage**: 74% ✅
   
   **Gate Status**: PASS ✅ (all ≥80%)
   
   **Uncovered Code**:
   - src/admin/legacy.ts: 23% (low priority)
   - src/utils/deprecated.ts: 15% (deprecated, ok)
   

4. Regression Testing (for updates):

   # Compare before/after
   git diff main...feature --stat
   
   # Run all tests
   npm test
   
   # Verify: No new failures (regression prevention)
   

5. Performance Validation:

   # Run performance tests
   npm run test:performance
   
   # Check response times
   # Verify: Within acceptable ranges
   

6. Generate Layer 2 Report:

   # Layer 2: Functional Verification
   
   ## Test Execution
   ✅ 245/245 tests passing (100%)
   ⏱️ Execution time: 8.3 seconds
   
   ## Coverage
   ✅ Line: 87% (gate: ≥80%)
   ✅ Branch: 82%
   ✅ Function: 92%
   
   ## Example Validation
   ✅ 18/20 examples work (90%)
   ❌ 2 examples fail (outdated)
   
   ## Regression
   ✅ All existing tests still pass
   
   ## Performance
   ✅ All endpoints <200ms
   
   **Layer 2 Status**: ✅ PASS
   **Issues**: 2 outdated examples (update docs)
   

Outputs:

• Test execution results
• Coverage report
• Example validation results
• Regression check
• Performance metrics
• Layer 2 status

Validation:

• [ ] All tests executed
• [ ] Coverage meets gate (≥80%)
• [ ] Examples validated (≥90%)
• [ ] No regressions
• [ ] Performance acceptable

Time Estimate: 30-60 minutes

Gate 2: ✅ PASS if tests pass + coverage ≥80%

---

Operation 3: Visual Verification (Layer 3)

Purpose: Validate UI appearance, layout, accessibility (for UI features)

Automation: 30-50% automated Speed: Minutes-Hours Confidence: Medium (subjective elements)

Process:

1. Screenshot Generation:

   # Generate screenshots of UI
   npx playwright test --screenshot=on
   
   # Or manually:
   # Open application
   # Capture screenshots of key views
   

2. Visual Comparison (if previous version exists):

   # Compare against baseline
   npx playwright test --update-snapshots=missing
   
   # Or use Percy/Chromatic for visual regression
   npx percy snapshot screenshots/
   

3. Layout Validation:

   # Visual Checklist
   
   ## Layout
   - [ ] Components positioned correctly
   - [ ] Spacing/margins match mockup
   - [ ] Alignment proper
   - [ ] No overlapping elements
   
   ## Styling
   - [ ] Colors match design system
   - [ ] Typography correct (fonts, sizes)
   - [ ] Icons/images display properly
   
   ## Responsiveness
   - [ ] Mobile view (320px-480px): ✅
   - [ ] Tablet view (768px-1024px): ✅
   - [ ] Desktop view (>1024px): ✅
   

4. Accessibility Testing:

   # Automated accessibility scan
   npx axe-core src/
   
   # Check WCAG compliance
   npx pa11y http://localhost:3000
   
   # Manual checks:
   # - Keyboard navigation
   # - Screen reader compatibility
   # - Color contrast ratios
   

5. Generate Layer 3 Report:

   # Layer 3: Visual Verification
   
   ## Screenshot Comparison
   ✅ Login page matches mockup
   ✅ Dashboard layout correct
   ⚠️ Profile page: Avatar alignment off by 5px
   
   ## Responsiveness
   ✅ Mobile: All components visible
   ✅ Tablet: Layout adapts correctly
   ✅ Desktop: Full functionality
   
   ## Accessibility
   ✅ WCAG 2.1 AA compliance
   ✅ Keyboard navigation works
   ⚠️ 2 color contrast warnings (non-critical)
   
   **Layer 3 Status**: ✅ PASS (minor issues acceptable)
   **Issues**: Avatar alignment (cosmetic), contrast warnings
   

Outputs:

• Screenshots of UI
• Visual comparison results
• Responsiveness validation
• Accessibility report
• Layer 3 status

Validation:

• [ ] Screenshots captured
• [ ] Visual comparison done (if applicable)
• [ ] Layout validated
• [ ] Responsiveness tested
• [ ] Accessibility checked
• [ ] No critical visual issues

Time Estimate: 30-90 minutes (skip if no UI)

Gate 3: ✅ PASS if no critical visual/a11y issues

---

Operation 4: Integration Verification (Layer 4)

Purpose: Validate system-level integration, data flow, API compatibility

Automation: 20-30% automated Speed: Hours (complex) Confidence: Medium-High

Process:

1. Component Integration Tests:

   # Run integration test suite
   npm test -- tests/integration/
   
   # Verify components work together
   # - Database ← → API
   # - API ← → Frontend
   # - Frontend ← → User
   

2. Data Flow Validation:

   # Data Flow Verification
   
   **Flow 1: User Registration**
   Frontend form → API endpoint → Validation → Database → Email service
   ✅ Data flows correctly
   ✅ No data loss
   ✅ Transactions atomic
   
   **Flow 2: Authentication**
   Login request → API → Database lookup → Token generation → Response
   ✅ Token generated correctly
   ✅ Session stored
   ✅ Response includes token
   

3. API Integration Tests:

   # Test all API endpoints
   npm run test:api
   
   # Verify:
   # - All endpoints respond
   # - Status codes correct
   # - Response formats match spec
   # - Error handling works
   

4. End-to-End Workflow Tests:

   // Complete user journeys
   test('Complete registration and login flow', async () => {
     // 1. Register new user
     const registerResponse = await api.post('/register', userData);
     expect(registerResponse.status).toBe(201);
   
     // 2. Confirm email
     const confirmResponse = await api.get(confirmLink);
     expect(confirmResponse.status).toBe(200);
   
     // 3. Login
     const loginResponse = await api.post('/login', credentials);
     expect(loginResponse.status).toBe(200);
     expect(loginResponse.data.token).toBeDefined();
   
     // 4. Access protected resource
     const profileResponse = await api.get('/profile', {
       headers: { Authorization: `Bearer ${loginResponse.data.token}` }
     });
     expect(profileResponse.status).toBe(200);
   });
   

5. Dependency Compatibility:

   # Check external dependencies work
   npm audit
   
   # Check for breaking changes
   npm outdated
   
   # Verify integration with services
   # - Database connection
   # - Redis/cache
   # - External APIs
   

6. Generate Layer 4 Report:

   # Layer 4: Integration Verification
   
   ## Component Integration
   ✅ 12/12 integration tests passing
   ✅ All components integrate correctly
   
   ## Data Flow
   ✅ All 5 data flows validated
   ✅ No data loss or corruption
   
   ## API Integration
   ✅ All 15 endpoints functional
   ✅ Response formats correct
   ✅ Error handling works
   
   ## E2E Workflows
   ✅ 8/8 user journeys complete successfully
   ✅ No workflow breaks
   
   ## Dependencies
   ✅ 0 critical vulnerabilities
   ⚠️ 2 moderate (non-blocking)
   
   **Layer 4 Status**: ✅ PASS
   

Outputs:

• Integration test results
• Data flow validation
• API compatibility report
• E2E workflow results
• Dependency audit
• Layer 4 status

Validation:

• [ ] Integration tests pass
• [ ] Data flows validated
• [ ] APIs integrate correctly
• [ ] E2E workflows function
• [ ] Dependencies secure

Time Estimate: 45-90 minutes

Gate 4: ✅ PASS if all integration tests pass, no critical dependencies

---

Operation 5: Quality Scoring (Layer 5)

Purpose: Holistic quality assessment using LLM-as-judge and Agent-as-a-Judge patterns

Automation: 0-20% automated Speed: Hours (expensive) Confidence: Medium (requires judgment)

Process:

1. Spawn Independent Quality Assessor (Agent-as-a-Judge):

   Key: Use different model family if possible (prevent self-preference bias)

   const qualityAssessment = await task({
     description: "Assess code quality holistically",
     prompt: `Evaluate code quality in src/ and tests/.
   
     DO NOT read implementation conversation history.
   
     You have access to tools:
     - Read files
     - Execute tests
     - Run linters
     - Query database (if needed)
   
     Assess 5 dimensions (score each /20):
   
     1. CORRECTNESS (/20):
        - Logic correctness
        - Edge case handling
        - Error handling completeness
        - Security considerations
   
     2. FUNCTIONALITY (/20):
        - Meets all requirements
        - User workflows work
        - Performance acceptable
        - No regressions
   
     3. QUALITY (/20):
        - Code maintainability
        - Best practices followed
        - Anti-patterns avoided
        - Documentation complete
   
     4. INTEGRATION (/20):
        - Components integrate smoothly
        - API contracts correct
        - Data flow works
        - Backward compatible
   
     5. SECURITY (/20):
        - No vulnerabilities
        - Input validation
        - Authentication/authorization
        - Data protection
   
     TOTAL: /100 (sum of 5 dimensions)
   
     For each dimension, provide:
     - Score (/20)
     - Strengths (what's good)
     - Weaknesses (what needs improvement)
     - Evidence (file:line references)
     - Recommendations (specific, actionable)
   
     Write comprehensive report to: quality-assessment.md`
   });
   

2. Multi-Agent Ensemble (for critical features):

   3-5 Agent Voting Committee:

   // Spawn 3 independent quality assessors
   const [judge1, judge2, judge3] = await Promise.all([
     task({description: "Quality Judge 1", prompt: assessmentPrompt}),
     task({description: "Quality Judge 2", prompt: assessmentPrompt}),
     task({description: "Quality Judge 3", prompt: assessmentPrompt})
   ]);
   
   // Aggregate scores
   const scores = {
     correctness: median([judge1.correctness, judge2.correctness, judge3.correctness]),
     functionality: median([...]),
     quality: median([...]),
     integration: median([...]),
     security: median([...])
   };
   
   const totalScore = sum(Object.values(scores)); // Total /100
   
   // Check variance
   const totalScores = [judge1.total, judge2.total, judge3.total];
   const variance = max(totalScores) - min(totalScores);
   
   if (variance > 15) {
     // High disagreement → spawn 2 more judges (total 5)
     // Use 5-agent ensemble for final score
   }
   
   // Final score: median of 3 or 5
   

3. Calibration Against Rubric:

   # Scoring Calibration
   
   ## Correctness: 18/20 (Excellent)
   **20**: Zero errors, all edge cases handled perfectly
   **18**: Minor edge case missing, otherwise excellent ✅ (achieved)
   **15**: 1-2 significant edge cases missing
   **10**: Some logic errors present
   **0**: Major functionality broken
   
   **Evidence**: All tests pass, edge cases covered except timezone DST edge case (minor)
   
   ## Functionality: 19/20 (Excellent)
   [Similar rubric with evidence]
   
   ## Quality: 17/20 (Good)
   [Similar rubric with evidence]
   
   ## Integration: 18/20 (Excellent)
   [Similar rubric with evidence]
   
   ## Security: 16/20 (Good)
   [Similar rubric with evidence]
   
   **Total**: 88/100 ⚠️ (Below ≥90 gate)
   

4. Gap Analysis (if <90):

   # Quality Gap Analysis
   
   **Current Score**: 88/100
   **Target**: ≥90/100
   **Gap**: 2 points
   
   ## Critical Gaps (Blocking Approval)
   None
   
   ## High Priority (Should Fix for ≥90)
   1. **Security: Weak bcrypt rounds**
      - **What**: bcrypt using 10 rounds (outdated)
      - **Where**: src/auth/hash.ts:15
      - **Why**: Current standard is 12-14 rounds
      - **How**: Change `bcrypt.hash(password, 10)` to `bcrypt.hash(password, 12)`
      - **Priority**: High
      - **Impact**: +2 points → 90/100
   
   ## Medium Priority
   1. **Quality: Missing JSDoc for 3 functions**
      - Impact: +1 point → 91/100
   
   **Recommendation**: Fix high priority issue to reach ≥90 threshold
   **Estimated Effort**: 15 minutes
   

5. Generate Comprehensive Quality Report:

   # Layer 5: Quality Scoring Report
   
   ## Executive Summary
   **Total Score**: 88/100 ⚠️ (Below ≥90 gate)
   **Status**: NEEDS MINOR REVISION
   
   ## Dimension Scores
   - Correctness: 18/20 ⭐⭐⭐⭐⭐
   - Functionality: 19/20 ⭐⭐⭐⭐⭐
   - Quality: 17/20 ⭐⭐⭐⭐
   - Integration: 18/20 ⭐⭐⭐⭐⭐
   - Security: 16/20 ⭐⭐⭐⭐
   
   ## Strengths
   1. Comprehensive test coverage (87%)
   2. All functionality working correctly
   3. Clean integration with all components
   4. Good error handling
   
   ## Weaknesses
   1. Bcrypt rounds below current standard (security)
   2. Missing documentation for helper functions (quality)
   3. One timezone edge case not handled (correctness)
   
   ## Recommendations (Prioritized)
   
   ### Priority 1 (High - Needed for ≥90)
   1. Increase bcrypt rounds: 10 → 12
      - File: src/auth/hash.ts:15
      - Effort: 5 min
      - Impact: +2 points
   
   ### Priority 2 (Medium - Nice to Have)
   1. Add JSDoc to helper functions
      - Files: src/utils/validation.ts
      - Effort: 30 min
      - Impact: +1 point
   
   2. Handle timezone DST edge case
      - File: src/auth/tokens.ts:78
      - Effort: 20 min
      - Impact: +1 point
   
   **Next Steps**: Apply Priority 1 fix, re-verify to reach ≥90
   

Outputs:

• Quality score (0-100) with dimension breakdown
• Calibrated against rubric
• Gap analysis
• Prioritized recommendations (Critical/High/Medium/Low)
• Evidence-based feedback (file:line references)
• Action plan to reach ≥90

Validation:

• [ ] All 5 dimensions scored
• [ ] Scores calibrated against rubric
• [ ] Evidence provided for each score
• [ ] Gap analysis if <90
• [ ] Recommendations actionable
• [ ] Ensemble used for critical features (optional)

Time Estimate: 60-120 minutes (ensemble adds 30-60 min)

Gate 5: ✅ PASS if total score ≥90/100

---

Quality Gates Summary

All 5 Gates Must Pass for production approval:

Gate 1: Rules Pass ✅
   ↓ (Linting, types, schema, security)

Gate 2: Tests Pass ✅
   ↓ (All tests, coverage ≥80%)

Gate 3: Visual OK ✅
   ↓ (UI validated, a11y checked)

Gate 4: Integration OK ✅
   ↓ (E2E works, APIs integrate)

Gate 5: Quality ≥90 ✅
   ↓ (LLM-as-judge score ≥90/100)

✅ PRODUCTION APPROVED

If Any Gate Fails:

Failed Gate → Gap Analysis → Apply Fixes → Re-Verify → Repeat Until Pass

---

Appendix A: Independence Protocol

How Verification Independence is Maintained

Verification Agent Spawning:

// After implementation and testing complete
const verification = await task({
  description: "Independent quality verification",
  prompt: `Verify code quality independently.

  DO NOT read prior conversation history.

  Review:
  - Code: src/**/*.ts
  - Tests: tests/**/*.test.ts
  - Specs: specs/requirements.md

  Verify against specifications ONLY (not implementation decisions).

  Use tools:
  - Read files to inspect code
  - Run tests to verify functionality
  - Execute linters for quality checks

  Score quality (0-100) with evidence.
  Write report to: independent-verification.md`
});

Bias Prevention Checklist:

• [ ] Specifications written BEFORE implementation
• [ ] Verification agent prompt has no implementation context
• [ ] Agent evaluates against specs, not what code does
• [ ] Fresh context (via Task tool)
• [ ] Different model family used (if possible)

Validation of Independence:

## Independence Audit

**Expected Behavior**:
- ✅ Verifier finds 1-3 issues (healthy skepticism)
- ✅ Verifier references specifications
- ✅ Verifier uses tools to verify claims

**Warning Signs**:
- ⚠️ Verifier finds 0 issues (possible rubber stamp)
- ⚠️ Verifier doesn't use tools
- ⚠️ Verifier parrots implementation justifications

**If Warning**: Re-verify with stronger independence prompt

---

Appendix B: Operational Scoring Rubrics

Complete Rubrics for All 5 Dimensions

Correctness (/20)

20 (Perfect): Zero logic errors, all edge cases handled, security perfect 18 (Excellent): 1 minor edge case missing, otherwise flawless 15 (Good): 2-3 edge cases missing, no critical errors 12 (Acceptable): Some edge cases missing, 1 minor logic issue 10 (Needs Work): Multiple edge cases missing or 1 significant logic error 5 (Poor): Major logic errors present 0 (Broken): Critical functionality broken

Functionality (/20)

20: All requirements met, exceeds expectations 18: All requirements met, well implemented 15: All requirements met, basic implementation 12: 1 requirement partially missing 10: 2+ requirements partially missing 5: Several requirements not met 0: Core functionality missing

Quality (/20)

20: Exceptional code quality, best practices exemplified 18: High quality, follows best practices 15: Good quality, minor style issues 12: Acceptable quality, several style issues 10: Below standard, needs refactoring 5: Poor quality, significant issues 0: Unmaintainable code

Integration (/20)

20: Perfect integration, all touch points verified 18: Excellent integration, minor docs needed 15: Good integration, all major points work 12: Acceptable, 1-2 integration issues 10: Integration issues present 5: Multiple integration problems 0: Does not integrate

Security (/20)

20: Passes all security scans, OWASP compliant, hardened 18: Passes scans, 1 minor non-critical issue 15: Passes, 2-3 minor issues 12: 1 medium security issue 10: Multiple medium issues 5: 1 critical issue present 0: Multiple critical vulnerabilities

---

Appendix C: Technical Foundation

Verification Tools

Linting:

• ESLint (JavaScript/TypeScript)
• Pylint/Ruff (Python)

Type Checking:

• TypeScript compiler (tsc)
• mypy (Python)

Security (SAST):

• Semgrep (multi-language)
• Bandit (Python)
• npm audit (JavaScript)

Visual Testing:

• Playwright (screenshot, visual regression)
• Percy/Chromatic (visual diff)
• axe-core (accessibility)

Coverage:

• c8/nyc (JavaScript)
• pytest-cov (Python)

Cost Controls

Budget Caps:

• LLM-as-judge: $50/month
• Ensemble verification: $20/month
• Total verification: $70/month

Optimization:

• Cache quality scores for 24h (same code → same score)
• Skip Layer 5 for changes <50 lines
• Use ensemble (3-5 agents) only for critical features
• Use cheaper models for pre-filtering (Haiku for Layer 1-2)

---

Quick Reference

The 5 Layers

| Layer | Purpose | Automation | Time | Tools | |-------|---------|------------|------|-------| | 1 | Rules-based | 95% | 15-30m | Linters, types, SAST | | 2 | Functional | 60-80% | 30-60m | Test execution, coverage | | 3 | Visual | 30-50% | 30-90m | Screenshots, a11y | | 4 | Integration | 20-30% | 45-90m | E2E, API tests | | 5 | Quality Scoring | 0-20% | 60-120m | LLM-as-judge, ensemble |

Total: 3-6 hours for complete 5-layer verification

Quality Thresholds

• ≥90: ✅ Excellent (production-ready)
• 80-89: ⚠️ Good (needs minor improvements)
• 70-79: ❌ Acceptable (needs work before production)
• <70: ❌ Poor (significant rework required)

Gates

All 5 Must Pass:

1. Rules pass (no critical lint/type/security)
2. Tests pass + coverage ≥80%
3. Visual OK (no critical UI issues)
4. Integration OK (E2E works)
5. Quality ≥90/100

---

multi-ai-verification provides comprehensive, multi-layer quality assurance with independent LLM-as-judge evaluation, ensuring production-ready code through systematic verification from automated rules to holistic quality assessment.

For rubrics, see Appendix B. For independence protocol, see Appendix A.