A diagnostic tool that tracks and visualizes the frequency and performance of installed AI agent skills through a terminal-based interface.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install skills-usage
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install skills-usage using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Skill Usage Statistics?
Skill Usage Statistics is a powerful analytics utility designed to give users deep insights into their local AI environment. By scanning interaction logs, it identifies which Openclaw Skills are being utilized most frequently, providing a clear ranking of tool adoption and usage trends over time.
This skill is essential for developers and power users who want to audit their automated workflows, identify underused tools, and optimize their project-specific AI agent interactions. It transforms raw log data into readable terminal reports featuring visual bar charts and structured tables.
Skill Usage Statistics Use Cases
- Identifying the most effective automation tools in your daily development cycle.
- Auditing AI usage patterns across the last 7, 30, or 90 days.
- Comparing skill adoption between global history and specific project sessions.
- Generating performance reports to benchmark AI agent efficiency.
How Skill Usage Statistics Works
- The user selects a desired timeframe for analysis, such as today, the past week, or the entire history.
- The system scans the local installation directory to identify all available Openclaw Skills.
- It parses global and project-specific history files (~/.claude/history.jsonl and session.jsonl) to locate skill invocation patterns.
- Call counts and frequency percentages are calculated based on millisecond timestamps and skill name matching.
- The final data is rendered as an optimized TUI report containing a ranked table and visual frequency bars.
Skill Usage Statistics Setup
To utilize this skill, ensure it is installed within your local agent directory. The system uses a Python-based execution logic to parse logs.
# Standard execution for the past 30 days
python3 ~/.claude/skills/skill-usage/stats.py past_30_days
Skill Usage Statistics Data Schema & Taxonomy
The skill organizes and filters data based on the following metadata structure:
| Data Point | Source | Format |
|---|---|---|
| Skill Identity | display field |
Matches /skill-name patterns |
| Event Timing | timestamp |
Millisecond Unix timestamps (converted to seconds) |
| Log Location | History Files | Analyzes both global and project-specific .jsonl files |
| Visual Output | TUI Report | Markdown table with ASCII bar charts |
Skill Usage Statistics Advanced Features
- Multi-Path Log Parsing: Automatically aggregates data from global history and individual project sessions.
- Dynamic Time Range Filtering: Supports granular filtering for today, 7 days, 30 days, 90 days, or all-time records.
- ASCII Visualization: Generates terminal-friendly bar charts to represent usage percentages without external dependencies.
- Automated Script Integration: Directly calls local Python logic for high-speed processing of large log files.
SKILL.md
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