Test and evaluate Agent Skill performance with benchmarks
Scanned 9/12/2026
Install to Claude Code
npx -y skills add thedixitjain/the-mega-skill-library --skill testing-and-qa --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: test-skill
description: "Test and evaluate Agent Skill performance with benchmarks"
category: testing-and-qa
source_repo: libukai/awesome-agent-skills
source_path: "plugins/agent-skills-toolkit/1.0.0/commands/test-skill.md"
source_url: https://github.com/libukai/awesome-agent-skills/blob/HEAD/plugins/agent-skills-toolkit/1.0.0/commands/test-skill.md
---
# Test and Evaluate Skill
You are helping the user test and evaluate an Agent Skill's performance.
**IMPORTANT**: First invoke `/agent-skills-toolkit:skill-creator-pro` to load the complete testing and evaluation framework, including scripts and evaluation tools.
Once skill-creator-pro is loaded, use the evaluation workflow and tools:
## Quick Testing Process
1. **Prepare Test Cases**
- Review existing test prompts
- Add new test cases if needed
- Cover various scenarios
2. **Run Tests** (use skill-creator-pro's scripts)
- Execute test prompts with the skill
- Use `scripts/run_eval.py` for automated testing
- Use `scripts/run_loop.py` for batch testing
- Collect results and outputs
3. **Qualitative Evaluation**
- Review outputs with the user
- Use `eval-viewer/generate_review.py` to visualize results
- Assess quality and accuracy
- Identify improvement areas
4. **Quantitative Metrics** (use skill-creator-pro's tools)
- Run `scripts/aggregate_benchmark.py` for metrics
- Measure success rates
- Calculate variance analysis
- Compare with baseline
5. **Generate Report**
- Use `scripts/generate_report.py` for comprehensive reports
- Summarize test results
- Highlight strengths and weaknesses
- Provide actionable recommendations
## Available Tools from skill-creator-pro
- `scripts/run_eval.py` - Run evaluations
- `scripts/run_loop.py` - Batch testing
- `scripts/aggregate_benchmark.py` - Aggregate metrics
- `scripts/generate_report.py` - Generate reports
- `eval-viewer/generate_review.py` - Visualize results
- `agents/grader.md` - Grading subagent
- `agents/analyzer.md` - Analysis subagent
- `agents/comparator.md` - Comparison subagent
## Evaluation Criteria
- **Accuracy**: Does it produce correct results?
- **Consistency**: Are results reliable across runs?
- **Completeness**: Does it handle all use cases?
- **Efficiency**: Is the workflow optimal?
- **Usability**: Is it easy to trigger and use?
## Next Steps
Based on test results:
- Run `/agent-skills-toolkit:improve-skill` to address issues
- Expand test coverage for edge cases
- Document findings for future reference
---
**Source:** [`libukai/awesome-agent-skills`](https://github.com/libukai/awesome-agent-skills) → `plugins/agent-skills-toolkit/1.0.0/commands/test-skill.md`
**Also appears in:** `libukai/awesome-agent-skills/plugins/agent-skills-toolkit/1.1.0/commands/test-skill.md`, `libukai/awesome-agent-skills/plugins/agent-skills-toolkit/1.2.0/commands/test-skill.md`
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