skillify

To convert raw features into structured agent skills, you audit the feature for completeness, generate a SKILL.md definition, add deterministic implementations, and create supporting test artifacts for reliable agent activation.

Auditing AI capability development involves evaluating whether a feature meets the requirements to become a complete Skill unit by checking for contracts, phases, operational guidance, and validation checks.

Setting up quality workflows for agent skills requires a repeatable process to add implementation code, evaluations, tests, and resolver entries, followed by verification checks to establish quality gates.

Yes, generating a SKILL.md definition is required to prepare an AI capability for agent activation, as it provides the discovery metadata and operational workflows needed for resolver integration.

The best way to structure agent workflows and resolver integration is to transform raw features into structured skills by applying quality gates, generating discovery metadata, and adding validation checks.

You should not use automation to turn a feature into an agent skill if the raw feature lacks clear operational guidance, making it unable to pass the audit for contracts, phases, and deterministic implementations.