# Analytics Self-Critique & Improvement Plan

## Current State Analysis

### What We Have ✅
1. **AI Impact Analyzer**
   - Correlates Claude conversations with git commits
   - Measures velocity improvement (commits/hour)
   - Detects collaboration patterns
   - Tracks learning curve

2. **Data We Collect**
   - Claude conversations (timestamps, messages, tool uses)
   - Git commits (hash, author, timestamp, message, file stats, language)
   - Shell history (commands, sanitized)

### Critical Questions We DON'T Answer ❌

1. **Shell Command Analytics**
   - ❌ How much time is wasted on failed commands?
   - ❌ What's your command success rate?
   - ❌ Which commands do you retry most?
   - ❌ Do you Google things or ask Claude?
   - ❌ Terminal productivity patterns?

2. **Cross-Correlation Missing**
   - ❌ Shell commands → Claude questions → Git commits flow
   - ❌ "Struggled in terminal → asked Claude → made commit" detection
   - ❌ Which shell errors led to AI assistance?
   - ❌ Time between command failure and AI help request?

3. **File-Level Insights**
   - ❌ Which files get most AI help?
   - ❌ File complexity vs AI assistance rate
   - ❌ Which files have most bugs (fix commits)?
   - ❌ AI-written code survival rate (not deleted later)

4. **Code Quality Metrics**
   - ❌ Churn rate: how much code gets rewritten?
   - ❌ Lines that survived vs deleted within 7 days
   - ❌ Bug density: fixes per 100 lines
   - ❌ AI code quality vs solo code quality

5. **Workflow Efficiency**
   - ❌ Context switches: terminal → Claude → git → terminal
   - ❌ Multi-tasking detection (overlapping sessions)
   - ❌ Flow state detection (uninterrupted work)
   - ❌ Distraction patterns (random commands between work)

6. **Time Patterns**
   - ❌ Burnout detection: declining velocity over day/week
   - ❌ Flow state hours: when do you work uninterrupted?
   - ❌ Break patterns: gaps between sessions
   - ❌ Optimal work duration before quality drops

7. **Error Analysis**
   - ❌ Most common shell errors
   - ❌ Git commit patterns: reverts, emergency fixes
   - ❌ Debugging session detection (rapid file edits)
   - ❌ Error → Fix cycle time

8. **Project Health**
   - ❌ Project momentum: commits over time
   - ❌ Abandonment risk: declining activity
   - ❌ Code debt: files that never get cleaned up
   - ❌ Hotspot files: changed too often (smell)

## Proposed Improvements

### 1. Shell Command Analyzer (NEW)

**What it does:**
- Parses shell history for productivity patterns
- Detects failed commands (exit codes, error messages)
- Measures command success rate
- Identifies time wasters (repeated failures)
- Detects "struggle sessions" (many failed attempts)

**Insights:**
```
🐚 Shell Productivity Analysis
  • Commands executed: 125,430
  • Success rate: 73.2% (33,598 failures)
  • Most failed: npm install (2,345 failures)
  • Time wasted on failures: ~42 hours
  • Average retries before success: 2.4

  Struggle Patterns:
    • Git conflicts: 234 sessions (avg 8 retries)
    • Build failures: 189 sessions (avg 12 retries)
    • Permission errors: 156 sessions (avg 3 retries)
```

### 2. Cross-Tool Correlation Engine (NEW)

**What it does:**
- Tracks user journey: shell → Claude → commit
- Detects "stuck → helped → succeeded" patterns
- Measures AI helpfulness (did it actually solve the problem?)
- Finds what triggers AI help requests

**Insights:**
```
🔗 Workflow Correlation Analysis

  Common Patterns:
    1. Shell Error → Claude Help → Commit
       • 1,234 instances
       • Avg time to solution: 18 minutes
       • Success rate: 89%

    2. Multiple Failed Commands → Claude
       • Threshold: 3+ failures triggers AI help
       • Avg failures before asking: 4.2
       • AI solves 76% of these cases

    3. Git Conflict → Claude → Resolution
       • 89 instances
       • Avg resolution time: 12 minutes
       • Manual resolution: 45 minutes (3.75x slower)

  AI Request Triggers:
    • Build failures: 45%
    • Test failures: 23%
    • Deployment errors: 18%
    • Dependency issues: 14%
```

### 3. File-Level Intelligence (NEW)

**What it does:**
- Tracks which files get most AI assistance
- Correlates file complexity with AI dependency
- Detects problem files (high churn, many bugs)
- Measures code survival rate

**Insights:**
```
📁 File-Level Analysis

  Most AI-Assisted Files:
    1. src/api/routes.ts (89% AI-written, 234 edits)
    2. src/components/Auth.tsx (76% AI, 156 edits)
    3. tests/integration.spec.ts (92% AI, 89 edits)

  Problem Files (High Churn):
    • src/utils/parser.rs - Rewritten 8 times
    • config/webpack.js - Changed 45 times in 30 days
    • Churn rate: 234% (more deletions than additions)

  Code Survival Rate:
    • AI-written code surviving >7 days: 68%
    • Solo code surviving >7 days: 82%
    • Difference: AI code 17% more likely to be rewritten
```

### 4. Code Quality Analyzer (NEW)

**What it does:**
- Calculates churn rate (code rewritten vs surviving)
- Detects bug patterns (commits with "fix" in message)
- Measures code lifetime
- Compares AI vs solo code quality

**Insights:**
```
💎 Code Quality Metrics

  Churn Analysis:
    • Total lines added: 125,430
    • Lines deleted within 7 days: 31,358 (25%)
    • Lines surviving >30 days: 78,945 (63%)
    • Churn rate: 25% (industry avg: 15-20%)

  Bug Density:
    • Total bug fix commits: 456
    • Lines per bug fix: 187
    • AI code bug rate: 1 bug per 245 lines
    • Solo code bug rate: 1 bug per 312 lines
    • AI code 27% more bugs (but 42% faster)

  Code Lifetime:
    • AI code median lifetime: 18 days
    • Solo code median lifetime: 34 days
    • AI code gets rewritten 47% faster
```

### 5. Workflow Efficiency Tracker (NEW)

**What it does:**
- Detects context switches between tools
- Identifies flow states (uninterrupted work)
- Measures distraction impact
- Finds optimal work patterns

**Insights:**
```
⚡ Workflow Efficiency

  Context Switches:
    • Per day: 87 switches
    • Cost per switch: ~3 minutes (refocus time)
    • Total time lost: 261 min/day (4.3 hours)
    • Most productive: <20 switches/day

  Flow States Detected:
    • Sessions >2 hours uninterrupted: 34
    • Productivity in flow: 3.2x normal
    • Best flow hours: 9-11 AM, 2-4 PM
    • Flow state triggers: complex tasks, deadlines

  Distraction Patterns:
    • Social media checks: 23/day
    • Email checks: 45/day
    • Slack messages: 67/day
    • Peak distraction: 3-5 PM
```

### 6. Temporal Pattern Detector (NEW)

**What it does:**
- Detects burnout patterns (declining velocity)
- Identifies optimal work durations
- Finds break patterns that maximize productivity
- Detects energy cycles

**Insights:**
```
⏰ Temporal Patterns

  Burnout Indicators:
    • Velocity declining by week:
      Week 1: 3.4 commits/hour
      Week 2: 2.9 commits/hour
      Week 3: 2.1 commits/hour
      Week 4: 1.6 commits/hour (53% drop - BURNOUT!)

    • Quality declining: bug rate up 2.3x
    • Signs: longer sessions, fewer commits, more reverts

  Optimal Work Duration:
    • Peak productivity: 90-120 minute sessions
    • After 2 hours: 34% velocity drop
    • After 4 hours: 67% velocity drop
    • Recommended: 90 min work + 15 min break

  Energy Cycles:
    • Peak energy: 9-11 AM (3.8 commits/hour)
    • Post-lunch dip: 1-2 PM (1.9 commits/hour)
    • Second wind: 3-5 PM (3.1 commits/hour)
    • Evening crash: >8 PM (1.2 commits/hour)
```

### 7. Error Pattern Intelligence (NEW)

**What it does:**
- Categorizes common errors
- Tracks error → fix cycle time
- Identifies recurring problems
- Predicts error-prone areas

**Insights:**
```
🐛 Error Pattern Analysis

  Top Error Categories:
    1. Type errors (34% of bugs)
    2. Null pointer exceptions (23%)
    3. API failures (18%)
    4. Build errors (15%)
    5. Test failures (10%)

  Fastest Fixes:
    • Syntax errors: 4 minutes avg
    • Import errors: 6 minutes avg
    • Type errors: 12 minutes avg

  Slowest Fixes:
    • Race conditions: 4.3 hours avg
    • Memory leaks: 3.8 hours avg
    • Integration issues: 2.9 hours avg

  Recurring Problems:
    • API timeout in prod: 12 fixes (not solved)
    • TypeScript any abuse: 89 fixes (tech debt)
    • Test flakiness: 45 fixes (unstable tests)
```

### 8. Project Health Monitor (NEW)

**What it does:**
- Tracks project momentum over time
- Detects abandonment risk
- Identifies code debt hotspots
- Measures project sustainability

**Insights:**
```
📊 Project Health Dashboard

  Momentum Score: 67/100 (Declining)
    • Commit frequency: -23% vs last month
    • Code additions: -34% vs last month
    • Active contributors: 1 (risky)
    • Trend: Slowing down

  Abandonment Risk: MEDIUM
    • Days since last commit: 5
    • Incomplete features: 8
    • Open TODOs: 234
    • Test coverage: 34% (declining)

  Code Debt Hotspots:
    • src/legacy/ - 12 files, 0 commits in 90 days
    • config/ - 234 TODOs, complex configs
    • tests/ - 67% flaky tests
    • docs/ - Last updated 120 days ago

  Sustainability Index: 42/100 (Poor)
    • Single point of failure: You
    • No documentation: 78% of code
    • No tests: 45% of files
    • Risk: Project dies if you stop
```

## Implementation Priority

### Phase 1: High-Value Quick Wins
1. ✅ Shell Command Analyzer
2. ✅ Cross-Tool Correlation
3. ✅ File-Level Intelligence

### Phase 2: Quality & Efficiency
4. ✅ Code Quality Analyzer
5. ✅ Workflow Efficiency Tracker
6. ✅ Temporal Pattern Detector

### Phase 3: Advanced Intelligence
7. ✅ Error Pattern Intelligence
8. ✅ Project Health Monitor

## Expected Impact

**Before:**
"I have backup archives with logs"

**After Phase 1:**
"I waste 42 hours/month on failed commands"
"I ask Claude after 4 failed attempts on average"
"My AI code gets rewritten 47% faster than solo code"

**After Phase 2:**
"I lose 4.3 hours/day to context switching"
"My productivity peaks at 9-11 AM"
"After 2 hours of work, my velocity drops 34%"

**After Phase 3:**
"I have 12 recurring bugs that keep coming back"
"My project has 67% abandonment risk"
"src/legacy/ is code debt - 0 commits in 90 days"

## Actionable Recommendations

Instead of just metrics, provide:
1. "You should take breaks every 90 minutes"
2. "Stop working after 8 PM - your quality drops 67%"
3. "File X needs refactoring - rewritten 8 times"
4. "Your project is slowing down - schedule time to finish incomplete features"
5. "You're burning out - Week 4 velocity down 53%"

## Data Sources Needed

✅ Already have:
- Claude conversations
- Git commits with stats
- Shell history

❌ Would be even better with:
- IDE activity logs
- Browser history (research patterns)
- Error logs (application crashes)
- Test results (pass/fail history)
- CI/CD logs (build times, failures)
