skills-optimize-skill-discoverability

AI agents fail to activate skills when metadata lacks clear naming, descriptions, or tags. This skill audits and refines existing metadata using "Use when..." clauses and naming conventions to ensure proper agent matching and discoverability.

Optimize skill metadata by enforcing the `<context>-<workflow>` naming pattern, rewriting descriptions with "Use when..." clauses, and enhancing tag relevance. This broadens search coverage and ensures AI agents can find and recommend the skill.

The best way to name AI agent skills is using the `<context>-<workflow>` naming convention. This pattern provides clear context and action signals, allowing AI agents to accurately identify, match, and activate the skill during relevant tasks.

Yes, you can fix a skill never suggested by AI by auditing its metadata. Refine the skill name, clarify the description with "Use when..." clauses, and improve tags to align with agent activation triggers and broader search queries.

Optimizing AI skill discoverability is limited to metadata refinement, including names, descriptions, and tags. It does not alter the internal processing logic or execution depth of the skill itself, focusing solely on agent activation and search matching.