Why Evaluate an AI Skill Before Adopting It
Not all AI skills are created equal. Some are brilliantly designed, tested, and maintained. Others are basic prompts wrapped in a Markdown file. Before integrating a skill into your professional workflow, you must rigorously evaluate it on three axes: quality, security, and performance.
This guide provides a complete checklist so you never make a bad choice.
Axis 1: Skill Quality
Structure and Instruction Clarity
A quality skill is immediately readable. Check for:
- Clearly defined role: does the skill precisely explain what it does?
- Structured instructions: are steps numbered and logical?
- Specified output format: do you know exactly what you will get?
- Rules and constraints: are limitations documented?
- Examples provided: are there concrete input/output examples?
Content Depth
A superficial skill produces superficial results. Evaluate:
- Domain expertise: does the skill demonstrate deep subject knowledge?
- Edge case handling: what happens with unusual inputs?
- Customization: can the skill be adapted to different contexts?
- Versioning: has the skill been recently updated?
Real-World Results
The real test is actual usage:
- Test with 5 different inputs: does the skill produce consistent results?
- Compare with manual work: is the result at least as good?
- Check reliability: run the same input 3 times — are results similar?
- Evaluate adaptability: does the skill handle ambiguous requests well?
Axis 2: Skill Security
Content Analysis
Security starts with carefully reading the SKILL.md file:
- No hidden instructions: read the entire file, including comments
- No external URLs: the skill should not call external services without your consent
- No data collection: verify no instruction requests sending your data to third parties
- No privilege escalation: the skill should not request system permissions
Sensitive Data Protection
If you use skills with professional data:
- Local processing: does the skill run entirely locally or does it send data to a server?
- Data in prompts: is your sensitive data included in prompts sent to the AI?
- Logs and history: where are conversations containing your data stored?
- GDPR compliance: does the processing comply with data protection regulations?
Provenance and Trust
The skill's origin is a reliability indicator:
- Identified author: who created the skill? Do they have a reputation in the domain?
- Verifiable source: does the skill come from a public GitHub repository or trusted platform?
- Active community: have other users tested and validated the skill?
- Clear license: are the terms of use explicit?
Axis 3: Skill Performance
Prompt Efficiency
A performant skill optimizes token usage:
- Conciseness: is the skill as short as possible without sacrificing quality?
- Context size: does the skill fit within your model's context window?
- Tokens consumed: how many tokens does the skill use per execution?
- Quality/cost ratio: does the result justify the token consumption?
Compatibility
A good skill works everywhere:
- Multi-model: does the skill work with Claude, GPT-4, Gemini?
- Multi-editor: is it compatible with Cursor, Windsurf, VS Code?
- Multi-language: does it correctly handle English and French?
- Dependencies: does it require specific tools or configurations?
Maintainability
A skill must evolve with your needs:
- Modularity: can you modify one part without breaking the rest?
- Documentation: can a new team member understand and use the skill?
- Extensibility: can features be added easily?
The Complete Evaluation Checklist
Quality (score out of 10)
- [ ] Clear and structured instructions
- [ ] Input/output examples provided
- [ ] Edge case handling
- [ ] Consistent results across 5 tests
- [ ] Customization possible
Security (score out of 10)
- [ ] No hidden or suspicious instructions
- [ ] No calls to external services
- [ ] Sensitive data protection
- [ ] Verifiable author and source
- [ ] Clear license
Performance (score out of 10)
- [ ] Reasonable size (under 2000 tokens)
- [ ] Compatible with your stack
- [ ] Results in acceptable time
- [ ] Satisfactory quality/cost ratio
- [ ] Complete documentation
Total score out of 30: a skill should score at minimum 20/30 to be adopted in production.
Red Flags: When to Reject a Skill
Immediately reject a skill if:
- It contains URLs to unknown services
- It requests sending data to third parties
- It is excessively long without justification
- Its instructions are obscure or contradictory
- The author is anonymous with no community feedback
- It claims to bypass AI security limits
Evaluate Before You Adopt
Taking 10 minutes to evaluate a skill will save you hours of problems. Use this checklist systematically and share your evaluations with the community on Skills Guides.