personal-analytics – OpenClaw Skill

---

name: personal-analytics description: Analyze conversation patterns, track productivity, and surface self-knowledge insights. Use when user wants to understand their own patterns (when they chat, what topics they discuss, productivity trends, sentiment over time). Provides weekly/monthly reports, topic recommendations, and time-based insights. Privacy-first design with all analysis local.

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, ...
    }
  },
  "sentiment_stats": {
    "positive": 145,
    "neutral": 62,
    "negative": 18,
    "mixed": 5
  }
}

.topic_cache.json

Topic extraction cache (temporary):

{
  "hash_12345": ["Python", "FastAPI", "testing"],
  "hash_67890": ["FM26", "tactics"]
}

Auto-deleted after 7 days.

Insights & Patterns

Time-Based Insights

Productivity by Hour:

• Analyzes task completion rate by hour
• Identifies peak productivity windows
• Suggests optimal work scheduling

Day of Week Patterns:

• Activity levels per day
• Best days for deep work
• Meeting-heavy vs focus-heavy days

Topic Insights

Topic Clustering:

• Groups related topics
• Identifies emerging interests
• Detects topic trends (rising, falling, stable)

Depth Analysis:

• Surface-level mentions vs deep dives
• Problem-solving topics vs casual chat
• Technical vs non-technical ratio

Sentiment Insights

Mood Tracking:

• Overall sentiment trends
• Correlation with time of day
• Stress indicator detection

Well-being Metrics:

• Late-night work frequency
• Urgent/stress keywords
• Work-life balance indicators

Privacy Controls

Exclusion Patterns

Automatically exclude sensitive data:

{
  "privacy": {
    "exclude_patterns": [
      "password", "token", "key", "secret",
      "credit card", "ssn", "api key"
    ]
  }
}

Data Retention

Auto-delete old data:

{
  "privacy": {
    "auto_delete_after_days": 90,
    "keep_aggregated_stats": true
  }
}

Manual Deletion

# Delete all data
python3 scripts/delete_data.py --all

# Delete specific date range
python3 scripts/delete_data.py --since "2026-01-01" --until "2026-01-31"

# Delete specific topics
python3 scripts/delete_data.py --topics "topic1,topic2"

Advanced Features

Custom Productivity Markers

Define what "productivity" means for you:

{
  "insights": {
    "productivity_markers": [
      "completed", "shipped", "merged", "deployed",
      "fixed", "resolved", "closed", "published"
    ]
  }
}

Topic Suggestions for Proactive Research

Automatically suggest topics based on:

• Frequency threshold (mentioned N+ times)
• Trend detection (rising interest)
• Problem-solving patterns (technical depth)
• Temporal patterns (regular discussions)

Report Customization

{
  "reports": {
    "include_sections": [
      "time_patterns",
      "topic_insights",
      "productivity",
      "sentiment",
      "recommendations"
    ],
    "exclude_topics": ["personal", "family"],
    "min_session_count": 5
  }
}

Use Cases

🎯 Optimize Work Schedule

Discover your peak productivity hours and schedule deep work accordingly.

📚 Track Learning Journey

See which topics you're exploring, how deeply, and identify knowledge gaps.

🧘 Monitor Well-being

Track stress indicators, late-night work, and mood trends.

🔍 Discover Patterns

Surface interests you didn't realize were important.

🤝 Improve Collaboration

Understand when you're most responsive and schedule meetings accordingly.

💡 Generate Content Ideas

Your most-discussed topics are content goldmines.

Best Practices

1. Run weekly reports - Set up auto-generated reports every Sunday
2. Review recommendations - Check topic suggestions monthly
3. Adjust privacy settings - Start conservative, adjust as comfortable
4. Use with proactive-research - Turn insights into automated monitoring
5. Don't over-optimize - Insights are guides, not rules

Troubleshooting

No data collected:

• Verify tracking is enabled: python3 scripts/status.py
• Check Moltbot integration is active
• Run manual analysis: python3 scripts/analyze.py --verbose

Inaccurate sentiment:

• Sentiment detection is heuristic-based
• Adjust if needed in future versions

Missing topics:

• Topic extraction uses keyword matching
• Lower threshold in config if too restrictive

Privacy concerns:

• Review .analytics_data.json - only aggregated stats
• Delete data anytime: python3 scripts/delete_data.py --all
• Disable tracking: python3 scripts/disable.py

Credits

Built for ClawdHub. Privacy-first design inspired by Quantified Self movement.

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.

Features

• ⏰ Time Pattern Analysis - When you chat, for how long, productivity peaks
• 📚 Topic Tracking - What you discuss most, emerging interests
• 😊 Sentiment Monitoring - Mood trends, stress indicators
• 💡 Productivity Insights - Task completion, optimal work times
• 📊 Beautiful Reports - Weekly/monthly summaries
• 🔗 Proactive Research Integration - Auto-suggest monitoring topics
• 🔒 Privacy First - All local, no external data, opt-in design

Quick Start

# 1. Setup
cp config.example.json config.json

# 2. Enable tracking
python3 scripts/enable.py

# 3. Analyze (after some sessions)
python3 scripts/analyze.py --insights

# 4. Generate weekly report
python3 scripts/report.py weekly

# 5. Get topic recommendations
python3 scripts/recommend.py --explain

Privacy Guarantee

🔒 Your data never leaves your machine.

• Raw conversations NOT stored
• Only aggregated statistics saved
• All analysis happens locally
• No external APIs for analysis
• Data files are gitignored
• Delete anytime with one command

What Gets Tracked

✅ Tracked (Aggregated Only)

• Session timestamps and duration
• Topic frequencies
• Sentiment distribution
• Productivity markers
• Time-of-day patterns

❌ NOT Tracked

• Raw message content
• Personal information
• Sensitive data (passwords, keys)
• Specific conversation details

Use Cases

📈 Optimize Your Schedule

Discover when you're most productive and schedule deep work accordingly.

Example Insight:

"Your peak productivity is 09:00-11:00 on Wednesdays. Task completion is 68% faster during this window."

🎯 Focus on What Matters

Identify topics you're spending time on and decide if they align with your goals.

Example Insight:

"You've discussed 'Docker deployment' 23 times this month. Consider monitoring 'Docker security updates' proactively."

😊 Track Well-being

Monitor stress indicators, late-night work, and mood trends.

Example Insight:

"Stress keywords decreased 40% this week. Work-life balance improving! 🎉"

💡 Discover Hidden Interests

Surface emerging topics you didn't realize you cared about.

Example Insight:

"New interest detected: 'Rust' (12 mentions this week, 0 last week). Want to monitor Rust language updates?"

Commands

Enable/Disable

# Enable tracking
python3 scripts/enable.py

# Disable tracking
python3 scripts/disable.py

# Check status
python3 scripts/status.py

Analyze

# Analyze all data
python3 scripts/analyze.py

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

# With insights
python3 scripts/analyze.py --insights

# JSON output
python3 scripts/analyze.py --json

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"

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

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

Recommendations

# Get topic recommendations
python3 scripts/recommend.py

# With explanations
python3 scripts/recommend.py --explain

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

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

Sample Report

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

## 🎯 Highlights

- **Most productive day:** Wednesday (6 tasks completed)
- **Peak hours:** 09:00-11:00
- **Emerging topic:** Rust (+1200%)
- **Mood trend:** ↗️ 78% positive (up from 65%)

## ⏰ Time Patterns

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

## 📚 Top Topics

1. **Python** (32 sessions)
2. **FM26** (28 sessions)
3. **Audio Engineering** (15 sessions)

## 💡 Recommendations

- Schedule deep work 09:00-11:00 (your peak)
- Monitor "Rust updates" (new interest)
- Avoid late-night sessions (22% slower)

Integration

Proactive Research

Personal Analytics automatically suggests topics for monitoring:

python3 scripts/recommend.py --auto-add

Flow:

1. Analyze conversation patterns
2. Identify frequently discussed topics
3. Suggest adding to proactive-research
4. One-click setup

Moltbot Session Hooks

For automated tracking, integrate with Moltbot:

# On session end
python3 /path/to/personal-analytics/scripts/session_tracker.py \
  message \
  --session-id <id> \
  --topics "Python,Docker" \
  --sentiment positive

Configuration

See SKILL.md for complete configuration options.

Key Settings

{
  "enabled": true,
  "privacy": {
    "auto_delete_after_days": 90,
    "exclude_patterns": ["password", "secret"]
  },
  "insights": {
    "productivity_markers": ["completed", "shipped", "fixed"],
    "stress_indicators": ["urgent", "critical", "broken"]
  }
}

Data Storage

.analytics_data.json

Aggregated statistics only (privacy-safe):

{
  "sessions": [{
    "id": "uuid",
    "start": "2026-01-28T10:00:00Z",
    "duration_seconds": 900,
    "topics": ["Python", "Docker"],
    "sentiment": "positive",
    "productivity_score": 0.8
  }],
  "topic_stats": {
    "Python": {
      "total_mentions": 145,
      "trend": "stable"
    }
  }
}

Requirements

• Python 3.8+
• Optional: proactive-research skill (for recommendations)

FAQ

Q: Is my data shared?

No. Everything stays on your machine. No external APIs for analysis.

Q: Can I see the raw data?

Yes. Check .analytics_data.json - only aggregated stats, no message content.

Q: How do I delete everything?

rm .analytics_data.json .topic_cache.json

Q: Does this slow down my assistant?

No. Analysis runs separately, doesn't affect chat performance.

License

MIT

Credits

Built for ClawdHub by the Moltmates team. Privacy-first design inspired by Quantified Self movement.