Tessl Skills Clinic - Nnenna Ndukwe from Qodo
The session demonstrates how automated AI agent skills can be rigorously tested and improved using Tessl's review process against Anthropic's best practices. By applying human-guided refinements—particularly enhancing the skill description with natural trigger phrases and optimizing structure—the Qodo PR Resolver skill score jumped from 78% to 89%. This highlights that while AI generation is powerful, manual validation and refinement are crucial for achieving high-quality, reliable agent performance in production workflows.
Key takeaways
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Skill Description is Critical for Agent Triggering
The skill's description metadata is identified as the single biggest factor determining if an agent will use it. Improving this by adding natural trigger phrases (e.g., 'coding issues,' 'code reviews') significantly increases discoverability and usage likelihood.
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Human Refinement Boosts Skill Quality
Research suggests that while AI-generated skills are useful, validated and refined skills (human intervention) show a significant performance uplift. The average jump observed was approximately 14% for human-written skills.
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The Importance of Post-Fix Verification
It is a best practice to include a validation step (e.g., running linters and static analysis) after an agent has applied fixes, ensuring the code remains compliant even after automated changes.