Skill Insight 🦞

By Hal Labs — Part of the Hal Stack

What it does: Analyzes skill usage data you already have. Generates reports, tracks success/failure, identifies dead weight. It does not automatically collect data for you.

The Honest Scope

This skill is a data analysis and reporting tool. It assumes usage data already exists. If you don't collect any, the reports will be empty.

What it CAN do well:

• Generate readable usage reports (daily/weekly/monthly)
• Track success vs failure rates per skill
• Recommend which zero-use skills to uninstall
• Work fully automatically for script-type skills (cron scan)

What it CANNOT do automatically:

• Detect route-type skill calls — these happen inside the AI's reasoning, never appearing in session text
• Know which skills you actually use without you telling it

Setup is required. See the section below.

Quick Setup (3 steps)

Step 1 — Add skills to your registry

# Start with your installed skills:
bash scripts/add_skill.sh <skill-name> <category> <description>
# Example: bash scripts/add_skill.sh fullstack-dev development "Backend architecture"

Step 2 — Choose a data collection method

This is the critical part. Pick one that matches your workflow:

Option A: For script-type skills only (easiest, automatic)

# Add to crontab -e:
0 9 * * * cd ~/.openclaw/workspace/skills/skill-insight && bash scripts/cron_wrapper.sh >> ~/.local/log/skill-insight.log 2>&1

This runs daily and scans your session history for skill scripts that were executed via exec commands. Works for script access_type skills. Will NOT detect route-type skills.

Option B: For route-type skills (requires agent cooperation)

Add to your agent's HEARTBEAT.md or equivalent:

# After any skill executes successfully:
bash scripts/record.sh <skill-name> "<what you used it for>" --lang en

# If it failed:
bash scripts/record_outcome.sh --skill <skill-name> --scene "<what>" failed "<reason>" --lang en

Option C: Manual (lowest effort, most incomplete)

# After any skill call, run:
bash scripts/record.sh <skill-name> "<scene>"

Step 3 — Generate reports

bash scripts/report.sh --period week --lang zh   # Chinese
bash scripts/report.sh --period week --lang en   # English
bash scripts/analyze.sh --period 7               # Unused skill analysis

How Data Collection Works

Understanding access_type helps you know what's covered:

If most of your skills are route-type (most OpenClaw skills), Option B is the only way to get meaningful data.

Setting Up Auto-Record for Route-Type Skills

If your agent supports HEARTBEAT.md or similar periodic scripts, add this:

### Skill Usage Tracking
- After any skill is used: `bash scripts/record.sh <skill-name> "<scene>" --lang en`
- If skill failed: `bash scripts/record_outcome.sh --skill <skill-name> --scene "<scene>" failed --lang en`
- Daily: `bash scripts/report.sh --period today --lang en`

For OpenClaw agents with proactive agent protocols (WAL/Working Buffer), the record call can be inserted after the WAL write step.

Commands Reference

# Record a skill invocation
bash scripts/record.sh <skill> <scene>              # success by default
bash scripts/record.sh <skill> <scene> skipped     # explicitly skipped

# Update outcome
bash scripts/record_outcome.sh <id> failed <reason>
bash scripts/record_outcome.sh --skill <name> --scene <pattern> failed <reason>

# Reports
bash scripts/report.sh --period today|week|month|all [--lang en|zh]
bash scripts/analyze.sh --period 7|30 [--lang en|zh]

# Registry
bash scripts/add_skill.sh <name> <category> <description> [installed]

# Session scan (script-type only)
bash scripts/scan_sessions.py [--lang en|zh]

Output Language

Use --lang en for English, --lang zh for Chinese. Falls back to English if unset.

Architecture

skill-insight/
├── scripts/
│   ├── record.sh / .py        # Record an invocation
│   ├── record_outcome.sh / .py   # Update outcome
│   ├── report.sh / .py        # Usage report
│   ├── analyze.sh / .py       # Unused skill analysis
│   ├── scan_sessions.sh / .py # Session scanner (script-type only)
│   ├── add_skill.sh / .py    # Add to registry
│   ├── cron_wrapper.sh        # Daily cron wrapper
│   ├── i18n.py               # Translations
│   └── path_utils.py          # Path resolution
├── data/                      # ← Your data (NOT published)
│   ├── skill_registry.json
│   └── usage.json
├── sample/                    # Example data
└── SKILL.md

Philosophy

You can't optimize what you don't measure.

This skill answers: which skills am I actually using? Which ones are dead weight? Is this skill reliable?

The goal isn't to use every skill — it's to know which ones earn their place.