xiaobai-skill-quality-eval（小白技能素质评测）

Skill Quality Evaluator 📊

Score any skill on 6 dimensions. Catch the 30% of skills that look good but fail silently.

Why This Matters

Tessl Research (April 2026) found:

• 20% accuracy gain when using a good skill vs no skill
• 3X cost savings when small model + right skill matches large model
• 40% activation rate — agents often fail to use available skills
• 30% of evaluation tasks have leakage — skills that seem great but aren't

This skill helps you evaluate and improve your skills systematically.

6-Dimension Evaluation

1. Activation Reliability (0-100)

Can the agent find and activate this skill when needed?

Checklist:

• [ ] Trigger words are specific and unambiguous
• [ ] Description matches actual functionality
• [ ] No conflicting skills with similar triggers
• [ ] Skill is discovered when user asks relevant questions

Common Issues:

• Vague description → agent doesn't know when to use it
• Missing trigger words → skill never activates
• Too broad → activates when it shouldn't

Score Guide:

• 90+: Agent activates correctly 95%+ of the time
• 70-89: Activates in most relevant contexts
• 50-69: Sometimes activates, sometimes misses
• <50: Agent rarely finds/uses this skill

2. Task Coverage (0-100)

Does the skill handle the tasks it claims to cover?

Checklist:

• [ ] Each claimed capability has a usage example
• [ ] Edge cases are documented
• [ ] Known limitations are stated
• [ ] Failure modes are explained

Common Issues:

• Claims broad coverage but only handles happy path
• No examples for secondary features
• Undocumented prerequisites

Score Guide:

• 90+: All claimed tasks have working examples
• 70-89: Main tasks covered, some gaps in secondary features
• 50-69: Core functionality works but incomplete
• <50: Major claims unsupported

3. Instruction Clarity (0-100)

Can the agent follow the instructions without confusion?

Checklist:

• [ ] Instructions are step-by-step, not vague guidelines
• [ ] Decision points have clear criteria
• [ ] Output format is specified
• [ ] Anti-patterns are listed

Common Issues:

• "Do X when appropriate" → when is appropriate?
• Missing priority/precedence rules
• Contradictory instructions

Score Guide:

• 90+: Agent follows instructions correctly 95%+ of the time
• 70-89: Mostly clear, occasional confusion
• 50-69: Agent frequently asks for clarification
• <50: Instructions are ambiguous or contradictory

4. Leakage Resistance (0-100)

Does the evaluation actually test the skill, or does it leak answers?

Checklist:

• [ ] Examples don't contain verbatim solutions
• [ ] Test tasks require genuine skill application
• [ ] No shortcut paths that bypass skill content
• [ ] Evaluation criteria measure real capability

Common Issues (from Tessl Research):

• Example tasks are too similar to skill content
• Skill contains answers verbatim
• Test can be solved by pattern matching without understanding

Score Guide:

• 90+: No leakage, genuine skill testing
• 70-89: Minor leakage that doesn't significantly inflate scores
• 50-69: Moderate leakage, scores may be 10-20% inflated
• <50: Major leakage, evaluation results unreliable

5. Model Compatibility (0-100)

Does the skill work across different model sizes?

Checklist:

• [ ] Tested with at least 2 model sizes
• [ ] Works with smaller/cheaper models
• [ ] Performance difference between models documented
• [ ] Minimum model requirements stated

Tessl Finding: Small model + right skill ≈ Large model at 3X lower cost.

Score Guide:

• 90+: Works well with small models (haiku-level)
• 70-89: Works with medium models (sonnet-level)
• 50-69: Requires large models (opus-level)
• <50: Even large models struggle

6. Real-World Value (0-100)

Does using this skill actually improve outcomes vs no skill?

Checklist:

• [ ] Measurable improvement over baseline
• [ ] Users would notice the difference
• [ ] Saves time or reduces errors
• [ ] No negative side effects

Score Guide:

• 90+: Clear, significant improvement (20%+ accuracy gain)
• 70-89: Noticeable improvement
• 50-69: Marginal improvement
• <50: No improvement or negative impact

Evaluation Report Template

# Skill Evaluation Report

**Skill**: [name]
**Version**: [version]
**Date**: YYYY-MM-DD
**Evaluator**: [agent/session]

## Overall Score: XX/100

| Dimension | Score | Status |
|-----------|-------|--------|
| Activation Reliability | XX | 🟢/🟡/🔴 |
| Task Coverage | XX | 🟢/🟡/🔴 |
| Instruction Clarity | XX | 🟢/🟡/🔴 |
| Leakage Resistance | XX | 🟢/🟡/🔴 |
| Model Compatibility | XX | 🟢/🟡/🔴 |
| Real-World Value | XX | 🟢/🟡/🔴 |

🟢 80+ | 🟡 50-79 | 🔴 <50

## Critical Issues
1. [Issue] → [Fix]

## Improvement Recommendations
1. [Recommendation] → [Expected impact]

## Quick Wins (easy fixes, big impact)
1. [Fix] → +X points on [dimension]

Usage

Evaluate a skill

Read the skill's SKILL.md and evaluate on all 6 dimensions.
Generate the evaluation report.
Save to memory/evaluations/<skill-name>-eval.md

Improve a skill based on evaluation

1. Read evaluation report
2. Focus on lowest-scoring dimension
3. Apply quick wins first
4. Re-evaluate
5. Repeat until all dimensions ≥ 70

Batch evaluate all skills

For each skill in skills/ directory:
  1. Read SKILL.md
  2. Evaluate on 6 dimensions
  3. Generate report
  4. Identify top 3 improvements
Save summary to memory/evaluations/batch-report.md

Anti-Patterns to Detect

License

MIT