ActivityWatch Analysis Skill

A Claude Code skill for weekly productivity analysis using ActivityWatch data. Calculates focus scores, detects "death loops" (repetitive app switching), and generates actionable insights.

Features

• Smart Auto-Categorization: Classifies activities into productive/neutral/distracting
• AI Agent Detection: Recognizes Claude Code, Codex, Aider, GitHub Copilot as productive workflows
• Dual Scoring: Productivity score (what you worked on) + Focus score (attention quality)
• Deep Browser Analysis: Site-level breakdown (Netflix, GitHub, ChatGPT) with productivity ratios
• Death Loop Detection: Identifies repetitive app switching patterns with fix suggestions
• Actionable Insights: Specific recommendations with blocking guides
• Customizable Categories: JSON config to tune for your workflow
• Timezone Support: Correctly handles ActivityWatch UTC timestamps

Requirements

• Python 3.8+
• ActivityWatch installed and running
• Optional: aw-client for direct API access (pip install aw-client)

Quick Start

Option 1: Direct API Fetch (Recommended)

If you have aw-client installed (pip install aw-client), you can fetch data directly from ActivityWatch:

# Analyze today's productivity
python scripts/analyze_aw.py --fetch --from today --report --timezone America/Los_Angeles

# Analyze yesterday
python scripts/analyze_aw.py --fetch --from yesterday --report

# Analyze the past week
python scripts/analyze_aw.py --fetch --from week --report

# Analyze specific date range
python scripts/analyze_aw.py --fetch --from 2025-12-20 --to 2025-12-26 --report

# Analyze last 7 days
python scripts/analyze_aw.py --fetch --from 7d --report

Supported date formats:

• Keywords: today, yesterday, week
• Relative: 7d (7 days ago), 2w (2 weeks ago)
• Absolute: YYYY-MM-DD (e.g., 2025-12-26)

Option 2: CSV Export (Fallback)

If you don't have aw-client or prefer manual export:

1. Open ActivityWatch (http://localhost:5600) → Raw Data → Export → CSV
2. Run analysis:

# Human-readable report (uses system timezone)
python scripts/analyze_aw.py export.csv --report

# With explicit timezone
python scripts/analyze_aw.py export.csv --timezone America/Los_Angeles --report

# With custom categories
python scripts/analyze_aw.py export.csv --config scripts/category_config.json --report

# JSON output for automation
python scripts/analyze_aw.py export.csv > summary.json

Recommended Workflow

Before trusting a weekly report, spot-check with a single day's data:

1. Spot-check first: Verify timezone, idle time, and categories match your experience
2. Fix issues: Adjust timezone or config if needed
3. Run full analysis: Once validated, trust the weekly report

Understanding Your Scores

Combined Score (0-100)

Range	Interpretation
80-100	Excellent - Deep work patterns, minimal distractions
60-79	Good - Solid productivity with room to improve
40-59	Moderate - Attention fragmented, review death loops
0-39	Needs work - High distraction, consider app blockers

Productivity vs Focus

• Productivity Score: Measures what you spent time on (deep work vs. entertainment)
• Focus Score: Measures how you worked (sustained attention vs. constant switching)

You can have high productivity but low focus (doing good work but constantly interrupted) or vice versa.

Death Loops

Death loops are repetitive A↔B app switches that fragment your attention.

Verdict	Meaning	Action
🤖 ai_assisted	AI coding agent active (Claude Code, Codex)	Productive workflow
🟢 productive	Normal workflow (IDE ↔ Terminal)	Consider split screen
🟡 mixed	Could go either way	Batch these activities
🔴 distracting	Attention leak	Block during focus hours

See references/blocking_guides.md for step-by-step setup of macOS Focus Mode, Cold Turkey, and other blocking tools.

Customizing Categories

Edit scripts/category_config.json to match your workflow:

{
  "my_product": {
    "weight": 0.8,
    "description": "My SaaS product work",
    "apps": [],
    "titles": ["MyApp", "myapp.com"]
  }
}

Weight scale:

• 1.0 = Deep work (Terminal, IDE)
• 0.7-0.9 = Productive (AI tools, writing)
• 0.5 = Mixed (meetings)
• 0.3 = Shallow (email, chat)
• 0.0 = Neutral (system utilities)
• -0.2 to -0.5 = Distracting (entertainment, social)

Weekly Review Ritual

Every Sunday (15 min):

1. Export week's data from ActivityWatch (CSV)
2. Run: python scripts/analyze_aw.py export.csv --report
3. Review the "One Change" recommendation
4. Implement one intervention
5. Track score improvement next week

Integration with Claude Code

As a Claude Code skill, you can ask:

"Analyze my ActivityWatch export from this week"
"Show me my death loops and how to fix them"
"What are my peak productive hours?"

Files

activitywatch-analysis-skill/
├── SKILL.md                      # Skill definition (AgentSkills.io format)
├── README.md                     # This file
├── scripts/
│   ├── analyze_aw.py             # Main analyzer (928 lines)
│   └── category_config.json      # Customizable categories
└── references/
    ├── analysis_prompts.md       # Prompts for deeper analysis with LLMs
    └── blocking_guides.md        # How to implement blocking recommendations

Privacy

All analysis runs locally. No data leaves your machine unless you choose to share it.

License

MIT

Author

Bayram Annakov (@BayramAnnakov)