An automated diagnostic tool designed to evaluate the quality and performance of installed skills based on five key technical dimensions.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install skill-quality-checker
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install skill-quality-checker using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
Skill Quality Checker is a specialized auditing utility for the Openclaw ecosystem. It provides developers with an automated way to assess the reliability and efficiency of their custom skills. By performing heuristic static analysis, the tool ensures that every skill meets a high standard of professional quality before deployment or sharing.
This tool is essential for maintaining a high-performance library of Openclaw Skills, helping users identify technical debt, missing documentation, or inefficient token usage that could hinder the AI agent's performance. It serves as a bridge between development and production-ready quality assurance.
The Skill Quality Checker relies exclusively on the Python standard library, ensuring zero-dependency installation within your environment.
# Navigate to your skill directory and run the scanner
python3 {SKILL_DIR}/scripts/check_quality.py --scan-dir /root/.openclaw/skills/
# To scan a specific skill instead of the entire directory
python3 {SKILL_DIR}/scripts/check_quality.py --skill /root/.openclaw/skills/target-skill/
The tool evaluates skills across five specific dimensions, each contributing 20 points toward a total score of 100:
| Dimension | Weight | Key Metrics |
|---|---|---|
| Problem-Solution Fit | 20 | Task alignment and implementation strategy accuracy |
| Completeness | 20 | File integrity, formatting standards, and absence of TODOs |
| Fault Tolerance | 20 | Error handling and fallback mechanism robustness |
| Description Precision | 20 | Trigger word coverage, length optimization, and specificity |
| Token Efficiency | 20 | File size management and progressive disclosure logic |
Rating Tiers:
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