skills-ingest

You can normalize raw skill drafts for CI safety by running the /skills-ingest command, which converts drafts into a standardized format, checks for overlaps, and resolves trigger conflicts to ensure router and CI compatibility.

The best way to prevent skill duplication during knowledge corpus ingestion is to use automated overlap prevention logic. The ingestion process checks existing skills, disambiguates potential trigger conflicts, and determines the correct directory placement before finalizing the integration.

Yes, automating skill classification requires a defined knowledge corpus structure and a standard skill format. The ingestion process relies on this established directory structure and standard formats to accurately classify raw drafts and determine their correct placement.

Skill disambiguation works by resolving potential conflicts between triggers of newly proposed skills and existing ones. During ingestion, the system validates triggers, normalizes formats, and ensures the externally-proposed skill is clean and ready for the next integration phase without manual intervention.

Yes, Python and YAML are listed as dependencies for preparing skill drafts. You can use these formats alongside placement hints when running the ingestion command to automate the classification, normalization, and directory placement of the skill within the knowledge corpus.