skill-domain-discovery

To map library documentation to actionable AI coding skills, you can analyze docs and source code to extract capability domains. This process clusters developer tasks into a structured skill taxonomy, outputting a domain map and skill specification for agent-facing knowledge.

Organizing existing documentation into skill categories requires extracting implicit capability areas from your library files. By clustering these tasks into domain-aligned skills, you generate structured outputs like domain_map.yaml and skill_spec.md for downstream tooling.

Maintainer interviews help discover developer tasks by guiding conversations that elicit developer journeys and capability domains. The interview insights are then synthesized into structured skill specifications and domain maps for AI coding agents.

Yes, you can run the domain discovery workflow without external dependencies. It operates independently by analyzing your library documentation and source code to produce domain_map.yaml and skill_spec.md outputs for downstream tooling.

You should not use automated skill taxonomy generation when your library lacks sufficient documentation or source code for analysis. The workflow relies on extracting implicit capability areas from these inputs to successfully structure agent-facing knowledge.