skill-creator

To build an AI agent skill, define the intent, write a SKILL.md with YAML frontmatter, bundle scripts and references, then run iterative test cases with baseline comparisons to evaluate performance and refine instructions.

Optimizing prompt engineering for triggering accuracy involves refining the skill description, running targeted evaluations against baseline performance metrics, and analyzing benchmark results to iteratively adjust the instructions and metadata.

Performance evaluation for generated AI skills works by running iterative test cases, comparing outputs against established baselines, and analyzing benchmark results to identify areas for improvement in instructions and resource bundling.

Yes, you can bundle external scripts, references, and assets into your AI skill. This allows the skill to access necessary resources and execute complex logic beyond the core markdown instructions during runtime.

To start developing AI skills for code generation, you need a defined intent for the capability and a structured SKILL.md file with YAML frontmatter and markdown instructions to guide the agent's behavior and resource usage.

Custom AI skills may not trigger correctly if the description lacks specificity. You can resolve this by applying targeted prompt engineering, running description optimization evaluations, and refining the metadata for better discoverability.