Activitywatch Analysis Skill

ActivityWatch Analysis Skill

Analyze ActivityWatch data to identify focus problems, track productivity, and generate actionable insights.

Detect Mode

Check what the user typed:

• /activitywatch calibrate or first run → Calibrate Mode
• /activitywatch or /activitywatch today or /activitywatch week → Analyze Mode
• /activitywatch review → Review Mode (coaching perspective)

MODE 1: Calibrate (first run)

Run this once to personalize the analyzer for the user's workflow.

Step 1: Check ActivityWatch is running + watchers

curl -s http://localhost:5600/api/0/info 2>/dev/null

If not running, tell user: "Start ActivityWatch first — it needs to be collecting data."

Then check which watchers are active:

curl -s http://localhost:5600/api/0/buckets 2>/dev/null | python3 -c "import json,sys; buckets=json.load(sys.stdin); [print(k) for k in sorted(buckets.keys())]"

You should see at minimum:

• aw-watcher-window_* — tracks active app + window title (essential)
• aw-watcher-afk_* — tracks idle/active state (essential)

Recommend if missing:

• aw-watcher-web-* — browser tab tracking (critical for site-level analysis). Install: Chrome extension from Chrome Web Store "ActivityWatch Web Watcher", or for Safari build from https://github.com/ActivityWatch/aw-watcher-web
• aw-watcher-vscode — VS Code extension for code-level tracking
• aw-watcher-input — keyboard/mouse activity (optional, privacy-sensitive)

Tell user: "Without aw-watcher-web, all browser time shows as one block under 'Chrome' or 'Safari' — no site-level breakdown. Install the Chrome extension for much better analysis."

Step 2: Run calibration on last 7 days

python scripts/analyze_aw.py --fetch --from week --calibrate --config scripts/category_config.json

This shows:

• Uncategorized apps with >5 min usage
• Default-categorized items that may need reclassification
• Telegram usage breakdown (if significant)

Step 3: Interview the user about uncategorized items

For each uncategorized app/site shown:

1. Ask: "What is [AppName]? Is it productive work, neutral, or distracting?"
2. Based on answer, assign to the right category and weight
3. Update scripts/category_config.json with the new entries

Step 4: Personalize product_work and telegram_chats

Ask:

• "What are your main product/business apps or sites? (e.g., your SaaS dashboard, CRM)"
• "Do you have work-related Telegram chats? List the ones that count as productive."

Add answers to product_work.titles and telegram_chats.product_work.patterns in the config.

Step 5: Verify

python scripts/analyze_aw.py --fetch --from today --report --config scripts/category_config.json

Ask user to spot-check: "Does this report match your experience of today? Any app miscategorized?"

MODE 2: Analyze (daily/weekly)

Parse the request

• "today" / "yesterday" / no argument → --from today or --from yesterday
• "this week" / "week" → --from week
• Specific dates → --from YYYY-MM-DD --to YYYY-MM-DD

Run analysis

python scripts/analyze_aw.py --fetch --from [date] --report --config scripts/category_config.json --timezone [user's timezone]

Present the report. Highlight:

• Productivity score and focus score
• Death loops (repetitive app switching)
• Top distraction
• AI-assisted development time
• The "One Change" recommendation

If user asks for deeper analysis

Read references/analysis_prompts.md for coaching-style follow-up questions.

MODE 3: Review (coaching perspective)

Compare this week to previous weeks. Look for:

• Score trends (improving, declining, flat)
• Recurring death loops that haven't been addressed
• Deep work hour trends
• Whether the "One Change" from last week was implemented

If the user has a coaching system (e.g., ~/GH/bayram-os/coaching/), cross-reference with process goals.

Features

• Smart Auto-Categorization: Classifies activities into productive/neutral/distracting
• AI Agent Detection: Recognizes Claude Code, Codex, Aider, and other AI coding agents
• Dual Scoring: Productivity score (what you worked on) + Focus score (attention quality)
• Deep Browser Analysis: Site-level breakdown with productivity ratios (Netflix, GitHub, ChatGPT, etc.)
• Death Loop Detection: Identifies repetitive app switching patterns with fix suggestions
• Actionable Insights: Specific recommendations based on your data
• Customizable Categories: JSON config to tune for your workflow

Quick Start

Option 1: Direct API Fetch (Recommended)

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

# 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

Date formats supported:

• today, yesterday, week (last 7 days)
• 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 prefer manual export or don't have aw-client:

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

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

# Specify timezone explicitly
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 (5 min): Analyze yesterday's data

   • Verify timezone is correct (timestamps match your memory)
   • Verify idle time makes sense (screen lock duration)
   • Check if top apps/categories match your experience

2. Fix issues if found:

   • Wrong timezone? Use --timezone America/Los_Angeles (or your zone)
   • Missing apps? Add them to scripts/category_config.json
   • Wrong categories? Adjust weights in config

3. Run full analysis: Once spot-check passes, trust the weekly report

Timezone Handling

ActivityWatch stores timestamps in UTC. The analyzer converts them to your local timezone for accurate hourly analysis.

# Common timezone examples:
--timezone America/Los_Angeles  # Pacific Time
--timezone America/New_York     # Eastern Time
--timezone Europe/London        # UK
--timezone Asia/Tokyo           # Japan

If not specified, the system's local timezone is used.

Time Breakdown

The report shows three time metrics:

Understanding Your Scores

Combined Score (0-100)

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.

Category Weights

Death Loops

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

Common patterns:

• Slack ↔ IDE: Waiting for responses → Batch check times
• Browser ↔ IDE: Testing/debugging → Use split screen
• Email ↔ Work: Anxiety/FOMO → Close email, check 2x/day
• Social ↔ Anything: Procrastination → Block during focus hours

Browser Analysis

Browser time is often 30-50% of screen time. The analyzer extracts sites from window titles and categorizes them:

Site Categories

Report Output

## 🌐 Browser Activity

**Total browser time:** 66.1h

| Type | Hours | % |
|------|-------|---|
| 🟢 Productive | 11.9h | 18% |
| 🟡 Neutral | 25.9h | 41% |
| 🔴 Distracting | 27.0h | 41% |

### Top Sites
| Site | Hours | Category | Type |
|------|-------|----------|------|
| Netflix | 11.9h | entertainment | 🔴 |
| YouTube | 7.8h | video | 🟡 |
| ChatGPT | 1.9h | ai_tools | 🟢 |

Customizing Site Categories

Add sites to KNOWN_SITES in analyze_aw.py:

'mysite.com': ('MySite', 'development', 0.8),

AI Agent Detection

The analyzer recognizes when you're using AI coding agents and adjusts scoring accordingly.

Supported Agents

How It Works

When you use AI coding agents, frequent Browser ↔ Terminal switching is expected and productive (reviewing docs, checking dashboards, supervising AI output). The analyzer:

1. Detects AI agent running in Terminal by window title
2. Marks Browser ↔ Terminal switches as "ai_assisted" instead of "distracting"
3. Excludes productive AI switches from Focus Score penalty
4. Still flags distracting switches (Telegram ↔ Terminal) even during AI sessions

Report Section

The report includes an "AI-Assisted Development" section showing:

| Agent | Hours | Switches |
|-------|-------|----------|
| claude_code | 25.6h | ~6700 |
| codex | 24.2h | ~6700 |

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", "MyApp Dashboard"]
  }
}

Fields:

• weight: Productivity impact (-0.5 to 1.0)
• apps: Match by application name (exact)
• titles: Match by window title (case-insensitive, partial match)
• description: Human-readable explanation

Telegram Chat Categorization

Telegram chats can be categorized by chat name (extracted from window title). Add to category_config.json:

{
  "telegram_chats": {
    "content_creation": {
      "weight": 0.7,
      "patterns": ["My Channel Name", "Zettelkasten"]
    },
    "product_work": {
      "weight": 0.8,
      "patterns": ["Course Chat", "EDU", "admin-monitoring", "bot-alerts"]
    },
    "communication_work": {
      "weight": 0.3,
      "patterns": ["Team Chat", "Project Group", "work"]
    }
  }
}

• Matches are case-insensitive and partial (pattern "EDU" matches "EDU Chat")
• Unmatched Telegram chats fall back to communication_personal (weight 0.1)
• This allows tracking productive Telegram use (content creation, course chats) vs personal messaging

Browser Title Categorization

Browser apps (Chrome, Safari, Firefox, ChatGPT Atlas, etc.) are automatically categorized by window title using the KNOWN_SITES dictionary. You can override the list of browser apps in config:

{
  "browser_apps": ["Google Chrome", "Safari", "Firefox", "Arc", "ChatGPT Atlas", "Edge"]
}

This is useful when using alternative browsers like ChatGPT Atlas that should get title-based categorization instead of being treated as a single "ai_tools" category

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 Ideas

n8n Automation

Weekly trigger → Export AW data → Run analyzer → Send to Telegram/Slack

Claude Memory

Ask Claude to remember your patterns:

• "My peak productive hours are 11am-1pm"
• "My main death loop is Telegram ↔ Terminal"

Focus Apps

Use insights to configure blocking tools. See references/blocking_guides.md for step-by-step setup:

• macOS Focus Mode
• Cold Turkey (cross-platform)
• iOS Screen Time / Android Digital Wellbeing
• Browser extensions (LeechBlock, StayFocusd)

Focus Guard - App Blocker

Focus Guard is an open-source app blocker for macOS that prevents distracting apps from running during focus hours.

Quick Start

# Start blocking (runs until you stop it)
python scripts/focus_guard.py --start

# Quick 2-hour focus session
python scripts/focus_guard.py --start --duration 2

# Block specific apps
python scripts/focus_guard.py --start --block Telegram Slack Discord

# Check status
python scripts/focus_guard.py --status

# Stop blocking
python scripts/focus_guard.py --stop

How It Works

1. Monitors running apps every 2 seconds
2. When a blocked app is detected:
   • Shows macOS notification with warning
   • Gives 5-second grace period to save work
   • Quits the app automatically
3. Logs all violations for weekly review

Configuration

Edit scripts/focus_config.json:

{
  "blocked_apps": ["Telegram", "Slack", "Discord"],
  "schedule": {
    "enabled": true,
    "start_hour": 9,
    "end_hour": 17,
    "days": ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"]
  },
  "settings": {
    "grace_period_seconds": 5,
    "show_notifications": true
  }
}

Integration with Analyzer

After a focus session, run the analyzer to see your productivity:

python scripts/focus_guard.py --stop
python scripts/analyze_aw.py --fetch --from today --report

Files

activitywatch-analysis/
├── SKILL.md                      # This file
├── scripts/
│   ├── analyze_aw.py             # Main analyzer
│   ├── focus_guard.py            # App blocker for focus sessions
│   ├── category_config.json      # Customizable categories
│   └── focus_config.json         # Focus Guard configuration
└── references/
    ├── analysis_prompts.md       # Prompts for deeper analysis
    └── blocking_guides.md        # How to implement blocking recommendations

Privacy

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

Requirements

• Python 3.8+
• ActivityWatch installed and running
• No external dependencies (uses only stdlib)