An automated evaluation engine that quantitatively assesses AI agent skills across six core dimensions to provide data-driven improvement insights.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install six-dim-evaluator
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 six-dim-evaluator 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 Six-Dim Evaluator?
Six-Dim Evaluator is a professional-grade L4 assessment engine designed to provide comprehensive, quantitative analysis for Openclaw Skills. By measuring six key dimensions—Technical Depth (T), Cognitive Enhancement (C), Orchestration (O), Evolution (E), Monetization (M), and User Experience (U)—it transforms qualitative performance into verifiable data.
This tool is essential for developers looking to standardize their evaluation workflow, offering a structured path from raw metrics to actionable AI-driven suggestions. Whether you are building complex agentic workflows or simple utilities, Six-Dim Evaluator ensures your skills meet high-performance standards through automated testing and historical trend analysis.
Six-Dim Evaluator Use Cases
- Quantitative evaluation of individual AI skills to identify performance bottlenecks.
- Side-by-side version comparison to track improvements or regressions between releases.
- Generating automated improvement roadmaps for specific weak dimensions like technical depth or monetization.
- Batch processing multiple skills within a repository for organizational oversight.
- Predictive trend analysis to forecast skill maturity based on historical scoring data.
How Six-Dim Evaluator Works
- Data Collection: The engine executes automated routines including Jest tests for technical metrics and source code analysis for documentation completeness.
- Metric Scoring: It analyzes 24 sub-dimensions to generate scores for the 6 primary T/C/O/E/M/U dimensions.
- Weighted Computation: A final aggregate score is calculated using weighted averages to assign a performance grade (e.g., Grade A).
- Report Generation: The system outputs detailed Markdown reports, JSON data for integration, and visual charts for easy interpretation.
- Insight Delivery: Based on the scores, the tool generates specific, executable suggestions to optimize low-performing areas.
Six-Dim Evaluator Setup
To integrate this evaluator into your workflow, ensure you have the necessary environment configured for Openclaw Skills development.
# Navigate to your project directory
cd six-dim-evaluator
# Install dependencies
npm install
# Run a single skill evaluation
node src/index.js evaluate --skill your-skill-name --version 1.0.0
# Run a comparison between two versions
node src/compare.js --v1 0.1.0 --v2 0.2.0
Six-Dim Evaluator Data Schema & Taxonomy
The skill utilizes a multi-layered taxonomy to organize evaluation metadata. Results are stored in an evaluation database to support long-term tracking.
| Level | Component | Description |
|---|---|---|
| Dimension | T/C/O/E/M/U | The 6 primary categories of assessment. |
| Metrics | 24 Sub-items | Granular data points like code coverage and documentation depth. |
| Outputs | Reports | Markdown, JSON, and Visual Chart exports. |
| Metadata | package.json | Version history and skill definitions for Openclaw Skills. |
Six-Dim Evaluator Advanced Features
- Automated CI/CD integration to evaluate every commit for quality regressions.
- AI-driven strategy generation that provides tailored code optimization snippets.
- Advanced trend forecasting using linear regression to predict future skill performance.
- Anomaly detection using Z-Score analysis to identify irregular scoring patterns in batch evals.
- Cross-skill synergy analysis to determine how well multiple Openclaw Skills work together in a chain.
SKILL.md
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