# AI-Assisted Development Metrics & Issue Classification System

## Product Requirements Document (PRD)

**Version:** 1.0.0  
**Status:** Draft  
**Owner:** Product/Engineering  
**Target Release:** Q1 2025

---

## Executive Summary

This PRD defines a comprehensive AI-assisted development metrics and classification system for the Roadcrew platform. The system will objectively assess issue complexity, predict AI success probability, track AI development productivity, and provide data-driven insights for continuous improvement of human-AI collaboration workflows.

**Business Outcome:** Enable data-driven decisions about when AI can work autonomously vs. when human expertise is required, while tracking the efficiency and quality of AI-assisted development over time.

---

## Problem Statement

### Current State

Roadcrew currently uses rudimentary complexity assessment (low/medium/high) and manual assignment decisions. This creates several challenges:

1. **Subjective Assessment**: No objective criteria for determining issue complexity
2. **Assignment Inefficiency**: Humans manually decide AI vs. human assignment without data
3. **No Performance Tracking**: Cannot measure AI code generation efficiency or quality
4. **Missing Calibration**: No feedback loop to improve classification accuracy
5. **Incomplete Context**: Repo analysis lacks metrics needed for AI capability prediction

### Impact

- **Wasted AI Capacity**: High-skill humans work on tasks AI could handle
- **Wasted Human Time**: AI attempts tasks beyond its capability, requiring rework
- **No Learning Curve**: System doesn't improve from historical outcomes
- **Poor Planning**: Cannot accurately estimate AI vs. human effort for releases

---

## Goals & Success Metrics

### Business Goals

1. **Optimize Resource Allocation**: Route 70%+ of low-complexity issues to AI autonomously
2. **Reduce Rework**: Decrease AI implementation failures from unknown% to <15%
3. **Improve Estimation**: Achieve 85%+ accuracy in complexity scoring within 3 months
4. **Enable Scaling**: Support 3x more issues/release with same human team size

### Success Metrics

| Metric | Current | Target (3 months) | Target (6 months) |
|--------|---------|-------------------|-------------------|
| Classification Accuracy | N/A | 75% | 85% |
| AI Success Rate (Low-Risk) | Unknown | 70% | 85% |
| AI Success Rate (Medium-Risk) | Unknown | 50% | 65% |
| Human Rework Rate | Unknown | <20% | <15% |
| AI Code Contribution % | Unknown | 40% | 60% |
| Avg Implementation Time (AI) | Unknown | Baseline -30% | Baseline -50% |

### Key Performance Indicators (KPIs)

**AI Productivity Metrics:**
- **AI Code Contribution %**: Percentage of codebase written by AI vs. humans
- **AI Development Velocity**: Issues completed per week by AI vs. humans
- **AI Implementation Duration**: Average time for AI to complete issues by risk level
- **First-Time Success Rate**: % of AI implementations that pass review without rework

**Quality Metrics:**
- **Rework Rate**: % of AI-generated code requiring significant human revision
- **Scrapped Implementation Rate**: % of AI attempts completely abandoned
- **Test Coverage (AI vs. Human)**: Compare test quality between AI and human code
- **Bug Rate (AI vs. Human)**: Defects per 1000 lines of code by source

**Classification Accuracy:**
- **Score Prediction Accuracy**: % of issues where predicted complexity matches actual
- **Risk Assessment Accuracy**: % of risk levels correctly predicted
- **Effort Estimation Accuracy**: Predicted vs. actual hours variance

---

## User Personas & Use Cases

### Primary Personas

#### 1. Technical Product Manager
**Cares About:** Release planning, resource allocation, velocity
**Pain Points:** 
- Cannot accurately estimate AI vs. human capacity for release
- No visibility into which issues AI can handle autonomously
- Cannot track if AI is actually improving team velocity

**Use Cases:**
- **UC1: Release Planning** - View AI capability forecast for backlog
- **UC2: Sprint Planning** - Allocate issues between AI and human developers
- **UC3: Velocity Tracking** - Monitor AI contribution to team throughput

#### 2. Senior Software Engineer
**Cares About:** Code quality, team productivity, skill development
**Pain Points:**
- Spends time on simple tasks AI could handle
- Must review/fix AI code that exceeds its capability
- No data on when to trust AI vs. do it themselves

**Use Cases:**
- **UC4: Issue Triage** - Quickly determine if issue is AI-ready
- **UC5: Quality Assurance** - Review AI code with risk-aware scrutiny
- **UC6: Skill Optimization** - Focus on high-value work AI can't do

#### 3. Engineering Manager
**Cares About:** Team efficiency, continuous improvement, ROI on AI tools
**Pain Points:**
- Cannot measure ROI of AI-assisted development
- No insights into where AI succeeds vs. struggles
- Missing data for process improvements

**Use Cases:**
- **UC7: Performance Analytics** - Track AI vs. human productivity trends
- **UC8: Process Optimization** - Identify bottlenecks in AI workflow
- **UC9: Investment Justification** - Prove value of AI tools to leadership

---

## Functional Requirements

### FR1: 10-Point Issue Classification System

**Description:** Implement objective 10-point scoring system (1-10) that maps issue complexity to human involvement level.

**Scoring Criteria:**

**🟢 ai-solo (1-3): AI-autonomous, human reactive only**
- **Score 1**: Fully automated (linting, formatting, file operations)
- **Score 2**: Quick inspection (README updates, simple text changes)
- **Score 3**: Light review (logging additions, simple helpers)

**🟡 ai-led (4-6): AI-led, human validates**
- **Score 4**: Manual verification (simple bug fixes, isolated features)
- **Score 5**: Scenario testing (API endpoints, multi-path features)
- **Score 6**: Integration testing (database changes, multi-system impact)

**🟠 ai-assisted (7-8): Human-led, AI assists**
- **Score 7**: Detailed guidance needed (refactoring with dependencies)
- **Score 8**: Human does core work (novel features, performance optimization)

**🔴 ai-limited (9-10): Human-owned**
- **Score 9**: Human primary (security-critical, breaking changes)
- **Score 10**: Human-only (novel algorithms, architectural redesign)

**Acceptance Criteria:**
- [ ] System assigns scores 1-10 to all new issues
- [ ] Scores include confidence % (e.g., "Score 7, confidence 82%")
- [ ] Scoring considers: files modified, lines changed, test coverage, dependencies
- [ ] Scores displayed in issue templates and GitHub labels
- [ ] Historical scores tracked for calibration

---

### FR2: Enhanced Repository Analysis

**Description:** Extend `/analyze-repo` to collect complexity metrics needed for AI classification.

**New Metrics to Capture:**

**Codebase Complexity:**
- Core vs. peripheral models (risk classification)
- Change frequency per file/directory (last 6 months)
- Test coverage % per file/directory
- Dependency graph depth (shallow/medium/deep)
- Known complexity hotspots

**Historical Performance:**
- Past issue velocity by complexity score (1-10)
- Common underestimates (predicted vs. actual)
- High-risk patterns (areas with frequent overruns)
- Rework rate by file/area

**Static Code Metrics:**
- Lines of code per file
- Cyclomatic complexity
- Function/method count and size
- Import/dependency count

**Acceptance Criteria:**
- [ ] New section "Complexity Metrics" in repo-analysis output
- [ ] New section "Historical Performance" in repo-analysis output
- [ ] Metrics stored in structured format (JSON) for programmatic access
- [ ] `/analyze-repo` runs in <60 seconds for typical project
- [ ] Metrics update automatically after each release

---

### FR3: AI Self-Assessment with Contextual Scoring

**Description:** AI analyzes issue details + repo context to assign initial complexity score.

**Input Sources:**
1. **Issue Content:**
   - Title and description
   - Acceptance criteria
   - Technical approach (if provided)
   - Labels and epic context

2. **Repository Context:**
   - Affected files (from technical spec or keywords)
   - Test coverage of affected areas
   - Change frequency (volatile vs. stable)
   - Dependency complexity

3. **Historical Data:**
   - Similar past issues (title similarity, file overlap)
   - Actual vs. predicted scores for similar issues
   - Success/failure rate for issue type

**Scoring Logic:**
```typescript
interface IssueAssessment {
  score: number; // 1-10
  confidence: number; // 0-100%
  rationale: string[];
  adjustments: ScoreAdjustment[];
  riskFactors: RiskFactor[];
  estimatedHours: number;
  recommendedAssignee: 'ai' | 'junior' | 'mid' | 'senior' | 'staff';
}

interface ScoreAdjustment {
  factor: string; // "Core model change", "Low test coverage"
  delta: number; // +2, -1, etc.
  rationale: string;
}
```

**Acceptance Criteria:**
- [ ] AI generates assessment within 5 seconds
- [ ] Assessment includes score, confidence, and detailed rationale
- [ ] System explains each adjustment (+2 for core model, +1 for low coverage)
- [ ] Assessment stored with issue for later validation
- [ ] Confidence threshold (75%+) triggers automatic assignment

---

### FR4: AI Development Metrics Tracking

**Description:** Capture comprehensive metrics during AI-assisted implementation to measure productivity and quality.

**Metrics to Capture:**

**Implementation Metrics:**
```typescript
interface ImplementationMetrics {
  issueNumber: string;
  startTime: Date;
  endTime: Date;
  durationMinutes: number;
  tokensUsed: number;
  
  // Code Changes
  filesModified: string[];
  linesAdded: number;
  linesDeleted: number;
  linesChanged: number; // added + deleted
  
  // Implementation Approach
  implementedBy: 'ai' | 'human' | 'hybrid';
  aiAutonomyLevel: 1 | 2 | 3 | 4 | 5; // From autonomy framework
  humanInterventions: number; // Times human had to step in
  
  // Quality Indicators
  testsAdded: number;
  testsPassing: boolean;
  lintErrors: number;
  typeErrors: number;
  
  // Outcome
  firstAttemptSuccess: boolean; // Passed review on first try
  reworkRequired: boolean; // Needed significant changes
  scrapped: boolean; // Completely abandoned
  
  // Predicted vs. Actual
  predictedScore: number;
  predictedHours: number;
  actualScore: number; // Updated after completion
  actualHours: number;
}
```

**Acceptance Criteria:**
- [ ] Metrics captured automatically during `/implement-issue` and `/implement-epic`
- [ ] Metrics stored in `config/metrics/implementations/YYYY-MM-DD-issue-N.json`
- [ ] Start/end timestamps accurate to the second
- [ ] Token usage tracked per AI interaction
- [ ] Human intervention events logged with context
- [ ] Metrics include git diff statistics (lines added/deleted)

---

### FR5: Calibration & Learning Loop

**Description:** System learns from completed implementations to improve future predictions.

**Calibration Process:**

1. **Post-Implementation Review:**
   - Compare predicted score vs. actual difficulty
   - Identify factors that caused variance
   - Update scoring model weights

2. **Pattern Recognition:**
   - Cluster similar issues by outcome
   - Identify consistently underestimated patterns
   - Flag high-risk code areas

3. **Confidence Adjustment:**
   - Increase confidence for accurate predictions
   - Decrease confidence for persistent errors
   - Track confidence improvement over time

**Acceptance Criteria:**
- [ ] System recalibrates weekly using last 20 completed issues
- [ ] Calibration report shows prediction accuracy trend
- [ ] High-variance patterns flagged in repo analysis
- [ ] Confidence scores adjust based on historical accuracy
- [ ] System warns when making predictions in unfamiliar territory

---

### FR6: AI Productivity Dashboard

**Description:** Visual dashboard showing AI development metrics and trends.

**Dashboard Sections:**

**1. AI vs. Human Contribution**
- Pie chart: % of code written by AI vs. humans (this sprint/release)
- Trend line: AI contribution % over last 6 sprints
- Breakdown by risk level: Low/Medium/High issue completion rates

**2. Velocity & Throughput**
- Issues completed per week: AI vs. Human
- Average hours per issue: AI vs. Human (by risk level)
- Velocity trend: improving/declining

**3. Quality Metrics**
- First-time success rate: % of AI code passing review
- Rework rate: % requiring human intervention
- Scrapped rate: % completely abandoned
- Bug rate: Defects per 1000 lines (AI vs. Human)

**4. Classification Accuracy**
- Predicted vs. actual score variance (bar chart)
- Confidence calibration: predicted confidence vs. actual accuracy
- Top 5 underestimated patterns
- Top 5 overestimated patterns

**Acceptance Criteria:**
- [ ] Dashboard accessible via `/metrics-dashboard` command
- [ ] Generates static HTML report with charts
- [ ] Updates automatically after each implementation
- [ ] Supports time range filtering (sprint, release, all-time)
- [ ] Exportable as PDF for stakeholder reporting

---

### FR7: Integration with Existing Commands

**Description:** Seamlessly integrate classification and metrics into existing Roadcrew workflows.

**Integration Points:**

**`/scope-release`:**
- Display AI capability forecast for release
- Show recommended AI vs. human distribution
- Flag issues likely to need human expertise

**`/analyze-epic`:**
- Include complexity scores for all child issues
- Predict AI success probability for epic
- Recommend implementation approach (batch vs. parallel)

**`/implement-issue` and `/implement-epic`:**
- Record all implementation metrics
- Track human interventions during implementation
- Generate post-implementation summary with metrics

**Acceptance Criteria:**
- [ ] All existing commands enhanced with classification/metrics
- [ ] No breaking changes to existing workflows
- [ ] Metrics capture happens automatically (no user action required)
- [ ] Commands show AI capability assessment in output
- [ ] Help text updated to reference new features

---

### FR8: Automated Workflow Orchestration (`/implement-autopilot`)

**Description:** Intelligent batch implementation command that uses classification scores to determine automation level and orchestrates entire release workflows with minimal human touchpoints.

**Purpose:** Apply classification system in production to prove ROI and generate calibration data.

**Modes Based on Classification:**

**Low Risk Mode (`--mode low`):**
- Auto-implements issues scored 1-3 (🟢 ai-solo)
- Human approval required for scores 4+ (🟡🟠🔴 ai-led/ai-assisted/ai-limited)
- Batches approvals across multiple epics
- Expected: 4-6 human touchpoints per release
- **Best for:** First-time autopilot users, high-stakes releases

**Medium Risk Mode (`--mode medium`):**
- Auto-implements issues scored 1-6 (🟢🟡 ai-solo + ai-led)
- Auto-proceeds if all issues ≤6 AND no breaking changes
- Human approval only for scores 7+ (🟠🔴 ai-assisted/ai-limited)
- Expected: 1-3 human touchpoints per release
- **Best for:** Teams with 75%+ classification accuracy, routine releases

**Auto-Stop Conditions (Classification-Driven):**
- Any issue scored 9-10 → STOP, require human review
- Aggregate epic score >7.5 → STOP (weighted average too risky)
- Confidence <75% on any issue → STOP
- Breaking changes detected → STOP
- Test coverage <80% → STOP (medium mode only)
- Circular dependencies → STOP
- Schema migrations required → STOP (flag for review)

**Decision Matrix:**

| Issue Score | Classification | Low Mode | Medium Mode |
|-------------|----------------|----------|-------------|
| 1-3 (🟢) | ai-solo: AI-autonomous, human reactive only | Auto-implement | Auto-implement |
| 4-6 (🟡) | ai-led: AI-led, human validates | Human approval | Auto-implement |
| 7-8 (🟠) | ai-assisted: Human-led, AI assists | Human approval | Human approval |
| 9-10 (🔴) | ai-limited: Human-owned | STOP autopilot | STOP autopilot |

**Workflow Orchestration:**

1. **Detect & Validate Release File**
   - Auto-detect populated release (current vs. minor)
   - Validate no template variables remain
   - Run `/analyze-repo` for tech stack context

2. **Create GitHub Structure** (`/scope-release`)
   - Auto-fill from tech stack + smart defaults
   - Create all Epics + child issues (no batch prompts)
   - Apply classification scores to all issues

3. **Analyze All Epics** (parallel)
   - Run `/analyze-epic` for each epic simultaneously
   - Detect inter-epic dependencies
   - Calculate combined risk assessment
   - Show unified implementation plan
   - **Human checkpoint** (if --mode low or high-risk detected)

4. **Implement Epics** (sequential or parallel based on dependencies)
   - Respect inter-epic dependencies
   - Auto-approve sequences that meet criteria
   - Track metrics continuously
   - Stop if any auto-stop condition triggered

5. **Testing & PR Creation**
   - Consolidate test plans
   - Auto-skip manual tests if coverage >80% (medium mode)
   - Create PRs: epic-* → dev
   - Wait for Bugbot + CI checks
   - Auto-proceed if all checks pass (medium mode)

**Time Savings vs. Manual Workflow:**

| Workflow | Commands to Run | Total Touchpoints | Time Savings |
|----------|----------------|-------------------|--------------|
| Manual (per Epic) | 3 | 8-19 | Baseline |
| Autopilot (Low) | 1 total | 4-6 total | ~60% |
| Autopilot (Medium) | 1 total | 1-3 total | ~85% |

**Metrics Captured per Autopilot Run:**

```typescript
interface AutopilotRunMetrics {
  runId: string;
  startTime: Date;
  endTime: Date;
  mode: 'low' | 'medium';
  
  // Scope
  epicCount: number;
  totalIssues: number;
  
  // Classification Distribution
  issuesByScore: Record<1|2|3|4|5|6|7|8|9|10, number>;
  
  // Automation Decisions
  autoImplemented: number;
  humanApproved: number;
  stoppedIssues: number;
  stopReason?: string;
  
  // Time Savings
  estimatedManualMinutes: number;
  actualMinutes: number;
  timeSavedMinutes: number;
  timeSavedPercent: number;
  
  // Outcomes
  firstTimeSuccess: number;
  reworkRequired: number;
  scrapped: number;
}
```

**Acceptance Criteria:**
- [ ] Respects classification scores for all automation decisions
- [ ] Logs all auto-proceed decisions with score rationale
- [ ] Captures comprehensive metrics for every autopilot run
- [ ] Stops immediately if any score 9-10 or stop condition detected
- [ ] Configuration in `config/workflow-config.yml` with schema validation
- [ ] Dry-run mode (`--dry-run`) shows what would be automated without executing
- [ ] Post-run report includes: issues by score, auto vs. manual breakdown, time saved
- [ ] No breaking changes to existing command behavior
- [ ] Works with both current-release.md and minor-release.md
- [ ] Parallel epic analysis when no inter-epic dependencies
- [ ] Sequential epic implementation when dependencies exist

---

## Non-Functional Requirements

### Performance
- Classification scoring completes in <5 seconds
- Repository analysis with new metrics in <90 seconds
- Metrics dashboard generates in <10 seconds
- No impact on existing command latency

### Usability
- Classification rationale human-readable (no ML jargon)
- Dashboard charts intuitive for non-technical stakeholders
- Metrics automatically captured (zero user friction)
- Clear visual indicators (🟢🟡🟠🔴) for scores

### Reliability
- Scoring system has fallback if repo analysis unavailable
- Metrics capture resilient to command failures
- Historical data preserved across Roadcrew updates
- Graceful degradation if external APIs fail

### Maintainability
- Classification logic in dedicated TypeScript module
- Metrics schema versioned for evolution
- Dashboard templates customizable
- Clear separation of concerns (detection, scoring, tracking)

---

## Open Questions

1. **Metric Storage Format:**
   - JSON files per issue (current approach)?
   - SQLite database for complex queries?
   - Both (JSON for archives, DB for active queries)?

2. **Confidence Threshold:**
   - What confidence % triggers automatic AI assignment?
   - Should threshold be configurable per team?

3. **Historical Data Retention:**
   - How long to keep implementation metrics?
   - Archive strategy for large projects (1000+ issues)?

4. **Privacy Considerations:**
   - Should metrics include developer names?
   - Anonymize data for team-level reporting?

5. **Comparison Baseline:**
   - How to fairly compare AI vs. human velocity?
   - Should we account for issue complexity in comparisons?

---

## Future Enhancements (Out of Scope for v1)

### Phase 2: Machine Learning Classification
- Train ML model on historical data for better predictions
- Auto-detect new complexity patterns
- Adaptive scoring based on team-specific patterns

### Phase 3: Real-Time Recommendations
- Suggest mid-implementation course corrections
- Alert when AI implementation deviating from plan
- Recommend human escalation proactively

### Phase 4: High-Risk Automation (`/implement-yolo`)

**⚠️ EXPERIMENTAL - Requires Mature Classification System**

**Prerequisites:**
- 3+ months of `/implement-autopilot` usage
- 85%+ classification accuracy sustained
- Organizational opt-in and risk acceptance
- Legal/compliance approval for auto-merge
- Comprehensive rollback capabilities

**Description:** Advanced automation for organizations with proven classification systems and high AI trust.

**Additional Automation Beyond Medium Mode:**
- Auto-implements scores 1-8 (🟢🟡🟠 green + yellow + orange zones)
- AI infers missing technical details from historical context
- Auto-merges epic PRs to dev after CI passes
- Optional async code review (doesn't block merge)
- **EXTREME MODE**: Auto-merges release PRs to main (production)

**Decision Matrix:**

| Issue Score | Classification | Yolo Mode |
|-------------|----------------|-----------|
| 1-6 (🟢🟡) | ai-solo + ai-led | Auto-implement + auto-merge |
| 7-8 (🟠) | ai-assisted: Human-led, AI assists | Auto-implement + async review |
| 9-10 (🔴) | ai-limited: Human-owned | Human approval required |

**Safeguards:**
- Mandatory comprehensive test coverage (>90%)
- Async code review for scores 7-8 (review within 24h, doesn't block)
- Comprehensive audit trail with video recording of all automated merges
- One-click rollback capability within 24 hours
- Real-time monitoring dashboards
- Automatic rollback on production errors
- Team notification of all automated merges
- Weekly summary of automated decisions

**Insurance/Liability Considerations:**
- Organizations assume responsibility for auto-merged code
- Recommended: Cyber liability insurance with AI coverage
- Document automation policies for compliance audits
- Clear escalation path for production issues

**Target Users:**
- Organizations with 85%+ classification accuracy over 6 months
- High test coverage (>90%) across entire codebase
- Mature CI/CD pipelines with comprehensive checks
- Strong monitoring and rollback capabilities
- Cultural tolerance for occasional production issues
- Legal/compliance approval obtained

**Success Criteria to Enable:**
- [ ] 85%+ classification accuracy for 6 consecutive months
- [ ] <5% rollback rate for medium mode autopilot runs
- [ ] >90% test coverage sustained
- [ ] Zero security incidents from automated implementations
- [ ] Team consensus and explicit opt-in
- [ ] Legal/compliance sign-off
- [ ] Comprehensive monitoring and alerting in place

**Why This is Phase 4 (Not Now):**

You need baseline data first. Current unknowns:
- What is your actual classification accuracy? (Unknown)
- What % of auto-implementations require rework? (Unknown)
- How often do automated tests miss real issues? (Unknown)
- What's your team's risk tolerance? (Unknown)
- Can your infrastructure support auto-rollback? (Unknown)

**Decision Point:** After 6 months of classification system usage, evaluate:
1. Is accuracy consistently ≥85%?
2. Is medium mode autopilot providing sufficient automation (80%+ time savings)?
3. Do teams trust the system enough for production auto-merge?
4. What's the ROI vs. risk tradeoff?

Most organizations will find `--mode medium` sufficient and never need yolo mode.

### Phase 5: Team Benchmarking
- Compare AI efficiency across teams
- Industry benchmarks for AI-assisted development
- Best practices recommendations based on aggregate data

---

## Dependencies

- **Phase 2 (Hybrid Monorepo):** Not blocking, but repo structure affects file metrics
- **Issue Classification Utilities:** Extend existing `scripts/utils/issue-classification.ts`
- **GitHub CLI:** Required for issue/PR metadata retrieval
- **Git History:** Need git log access for change frequency metrics

---

## Success Criteria (Definition of Done)

### MVP (v1.0.0) Complete When:

- [ ] 10-point classification system implemented and documented
- [ ] `/analyze-repo` captures all new complexity metrics
- [ ] AI self-assessment working with 75%+ confidence
- [ ] Implementation metrics captured for all issues
- [ ] Calibration loop running weekly
- [ ] Dashboard shows all key metrics with charts
- [ ] Integration with existing commands complete
- [ ] Documentation updated (README, command help)
- [ ] Example metrics from 20+ completed issues
- [ ] Team trained on interpreting scores and metrics

### Success Validation:
- After 3 months: 75%+ classification accuracy
- After 3 months: AI completes 40%+ of low-risk issues successfully
- After 6 months: 85%+ classification accuracy
- After 6 months: Measurable velocity improvement (20%+ more issues/sprint)

---

## Appendix

### Related Documents
- Technical Specification: `ai-classification-metrics.md` (see companion artifact)
- Issue Classification Utilities: `scripts/utils/issue-classification.ts`
- Repo Analysis: `context/commands/analyze-repo.md`
- Implement Issue: `context/commands/implement-issue.md`

### Glossary
- **Complexity Score**: 1-10 rating indicating human involvement required
- **AI Code Contribution %**: Percentage of codebase lines written by AI
- **Rework Rate**: Percentage of AI implementations requiring significant human revision
- **Scrapped Rate**: Percentage of AI attempts completely abandoned
- **First-Time Success**: AI implementation passes review without changes

---

**Last Updated:** 2025-01-19  
**Version:** 1.0.0  
**Status:** Draft - Ready for Technical Specification