personal-analytics

Personal Analytics

Know thyself. Work smarter. Discover patterns you didn't know existed.

Personal Analytics analyzes your conversation patterns to surface actionable insights about your work style, interests, and productivity—all while keeping your data completely private and local.

Core Capabilities

1. Session Analysis - When you chat, for how long, productivity patterns
2. Topic Tracking - What subjects come up repeatedly, trending interests
3. Sentiment Patterns - Mood tracking over time, stress indicators
4. Productivity Insights - When you're most effective, optimal work times
5. Weekly/Monthly Reports - Beautiful summaries of your patterns
6. Topic Recommendations - Auto-suggest topics for proactive-research monitoring

Privacy First

🔒 All analysis happens locally. Nothing leaves your machine.

• Raw conversations never stored
• Only aggregated statistics saved
• Opt-in design (must enable)
• Data deletion anytime
• No external APIs for analysis
• Gitignored data files

Quick Start

# Initialize
cp config.example.json config.json

# Enable tracking
python3 scripts/enable.py

# Analyze current sessions
python3 scripts/analyze.py

# Generate report
python3 scripts/report.py weekly

# Get topic recommendations
python3 scripts/recommend.py

What Gets Tracked

Session Metadata

• Timestamp (start/end)
• Duration
• Message count
• Primary topics discussed
• Sentiment (positive/neutral/negative/mixed)
• Productivity markers (tasks completed, decisions made)

Aggregated Stats

• Hourly activity heatmap
• Topic frequency over time
• Average session duration
• Productivity by time of day
• Sentiment trends

What's NOT Tracked

• ❌ Raw message content
• ❌ Personal information
• ❌ Sensitive data (passwords, keys, etc.)
• ❌ Specific conversations

Configuration

config.json

{
  "enabled": true,
  "tracking": {
    "sessions": true,
    "topics": true,
    "sentiment": true,
    "productivity": true
  },
  "privacy": {
    "min_aggregation_window_hours": 24,
    "auto_delete_after_days": 90,
    "exclude_patterns": ["password", "secret", "token", "key"]
  },
  "insights": {
    "productivity_markers": [
      "completed", "shipped", "fixed", "merged", "deployed"
    ],
    "stress_indicators": [
      "urgent", "asap", "critical", "broken", "emergency"
    ]
  },
  "reports": {
    "weekly_day": "sunday",
    "weekly_time": "20:00",
    "auto_send": false
  },
  "integrations": {
    "proactive_research": {
      "auto_suggest_topics": true,
      "suggestion_threshold": 3
    }
  }
}

Scripts

analyze.py

Analyze conversation patterns:

# Analyze all available data
python3 scripts/analyze.py

# Analyze specific time range
python3 scripts/analyze.py --since "2026-01-01" --until "2026-01-31"

# Analyze and show insights
python3 scripts/analyze.py --insights

# Verbose output
python3 scripts/analyze.py --verbose

Output:

📊 Personal Analytics Analysis

Period: Jan 1 - Jan 28, 2026 (28 days)

Session Summary:
  Total sessions: 145
  Total time: 18h 32m
  Avg session: 7m 40s
  Most active: Tuesday 10:00-11:00

Topics (Top 10):
  1. Python (32 sessions)
  2. FM26 (28 sessions)
  3. Dirac Live (15 sessions)
  4. ETH/crypto (12 sessions)
  5. Docker (11 sessions)
  ...

Productivity:
  High productivity: 09:00-12:00, 14:00-16:00
  Low productivity: Late night (after 22:00)
  Peak day: Wednesday
  
Sentiment:
  Positive: 62%
  Neutral: 28%
  Negative: 8%
  Mixed: 2%

report.py

Generate beautiful reports:

# Weekly report
python3 scripts/report.py weekly

# Monthly report
python3 scripts/report.py monthly

# Custom range
python3 scripts/report.py custom --since "2026-01-01" --until "2026-01-31"

# Export to file
python3 scripts/report.py weekly --output report.md

# Send via Telegram
python3 scripts/report.py weekly --send

Report Format:

# 📊 Weekly Analytics Report
**Jan 22 - Jan 28, 2026**

## 🎯 Highlights

- **Most productive day:** Wednesday (4 tasks completed)
- **Peak hours:** 09:00-11:00 (3h 45m focused work)
- **Emerging topic:** Rust (mentioned 12 times, +200% from last week)
- **Mood trend:** ↗️ Improving (78% positive, up from 65%)

## ⏰ Time Patterns

### Activity Heatmap

Mon ████░░░░░░░░░░░░░░░░░░░░ 4h Tue ██████████░░░░░░░░░░░░░░ 6h 30m Wed ████████████░░░░░░░░░░░░ 8h 15m ← Peak Thu ██████░░░░░░░░░░░░░░░░░░ 5h Fri ████░░░░░░░░░░░░░░░░░░░░ 3h 45m Sat ██░░░░░░░░░░░░░░░░░░░░░░ 1h 30m Sun ░░░░░░░░░░░░░░░░░░░░░░░░ 45m

### Hourly Distribution

06-09: ██░░░░░░░░ (12%) 09-12: ████████░░ (38%) ← Peak productivity 12-14: ███░░░░░░░ (15%) 14-17: █████░░░░░ (24%) 17-22: ██░░░░░░░░ (11%)

## 📚 Topic Insights

### Top Topics This Week
1. **Python Development** (32 sessions)
   - Focus: FastAPI, async, testing
   - Trend: Steady
   - Suggestion: Monitor "Python 3.13 features"

2. **FM26** (28 sessions)
   - Focus: Tactics, transfers, editor
   - Trend: ↗️ +15%
   - Suggestion: Already monitoring "FM26 patches" ✓

3. **Audio Engineering** (15 sessions)
   - Focus: Dirac Live, room correction, bass management
   - Trend: 🆕 New topic
   - Suggestion: Monitor "Dirac Live updates"

### Emerging Topics
- **Rust** (12 mentions, first appearance)
- **Kubernetes** (8 mentions, +300%)
- **Machine Learning** (6 mentions)

## 💡 Productivity Insights

### Task Completion
- Total tasks: 23 completed
- Success rate: 87%
- Best day: Wednesday (6 tasks)
- Best time: Morning (09:00-12:00)

### Focus Sessions
- Long sessions (>30m): 8
- Average focus time: 18m
- Longest session: 1h 42m (Wed 10:15)

### Problem-Solving Speed
- Quick wins (<15m): 14 problems
- Complex issues (>1h): 3 problems
- Average: 24m per problem

## 😊 Sentiment & Well-being

### Overall Mood

😊 Positive ████████████████░░ 78% (↗️ +13%) 😐 Neutral ████░░░░░░░░░░░░░░ 18% 😟 Negative ██░░░░░░░░░░░░░░░░ 4%

### Stress Indicators
- High stress: 3 sessions (down from 7)
- Urgent keywords: 5 (down from 12)
- Late-night work: 2 sessions (down from 8)

**Insight:** Stress levels decreasing. Good work-life balance this week! 🎉

## 🎯 Recommendations

### For Proactive Research
Based on your interests this week, consider monitoring:
1. **Rust language updates** (mentioned 12x, new interest)
2. **Dirac Live releases** (mentioned 15x, active problem-solving)
3. **Kubernetes security** (mentioned 8x, DevOps focus)

### Productivity Tips
- **Schedule deep work 09:00-11:00** (your peak productivity)
- **Batch meetings after lunch** (14:00-16:00 is secondary peak)
- **Avoid late-night sessions** (22% slower problem-solving)

### Topics to Explore
Based on your current interests, you might enjoy:
- Async Rust patterns (combines Rust + async focus)
- Kubernetes observability (combines K8s + monitoring)
- Audio DSP with Python (combines audio + Python)

---

_Generated by Personal Analytics • Privacy-first, locally processed_

recommend.py

Get topic recommendations for proactive-research:

# Get recommendations
python3 scripts/recommend.py

# Show reasoning
python3 scripts/recommend.py --explain

# Auto-add to proactive-research
python3 scripts/recommend.py --auto-add

# Set threshold (minimum mentions)
python3 scripts/recommend.py --threshold 5

Output:

💡 Topic Recommendations for Proactive Research

Based on your conversation patterns:

1. Rust Language Updates
   Mentioned: 12 times this week (new topic)
   Reason: Emerging interest, high engagement
   Suggested query: "Rust language updates releases"
   Suggested frequency: weekly
   
2. Dirac Live Updates
   Mentioned: 15 times this week
   Reason: Active problem-solving, technical depth
   Suggested query: "Dirac Live update release"
   Suggested frequency: daily
   
3. FM26 Patches
   Mentioned: 28 times this week
   Reason: Consistent interest over time
   NOTE: Already monitoring! ✓

Would you like to add these topics to proactive-research? [y/N]

session_tracker.py

Track individual sessions (called by Moltbot):

# Log session start
python3 scripts/session_tracker.py start --channel telegram

# Log session end
python3 scripts/session_tracker.py end --session-id <id>

# Log message (topics, sentiment)
python3 scripts/session_tracker.py message --session-id <id> \
  --topics "Python,Docker" \
  --sentiment positive

This script is designed to be called by Moltbot hooks, not manually.

enable.py / disable.py

Manage tracking:

# Enable tracking
python3 scripts/enable.py

# Disable tracking
python3 scripts/disable.py

# Show status
python3 scripts/status.py

Integration with Moltbot

Personal Analytics can integrate with Moltbot session lifecycle:

Hook Points

1. Session Start - Log timestamp, channel
2. Session End - Calculate duration, save stats
3. Message Received - Extract topics (lightweight), detect sentiment

Recommended Setup

Add to Moltbot SOUL.md:

## Personal Analytics Integration

After each session ends, if personal-analytics is enabled:
1. Extract primary topics discussed (max 5)
2. Determine overall sentiment
3. Detect productivity markers (tasks completed)
4. Log to personal-analytics via session_tracker.py

Data Storage

.analytics_data.json

Aggregated statistics only:

{
  "sessions": [
    {
      "id": "session_uuid",
      "start": "2026-01-28T10:00:00Z",
      "end": "2026-01-28T10:15:00Z",
      "duration_seconds": 900,
      "channel": "telegram",
      "topics": ["Python", "Docker"],
      "sentiment": "positive",
      "productivity_score": 0.8,
      "tasks_completed": 1
    }
  ],
  "topic_stats": {
    "Python": {
      "total_mentions": 145,
      "last_seen": "2026-01-28T10:15:00Z",
      "trend": "stable"
    }
  },
  "time_stats": {
    "hourly_distribution": {
      "09": 23, "10": 45, "11": 38, ...
    },
    "daily_distribution": {
      "monday": 120, "tuesday": 98

---

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