AI & Machine Learning
Library Reference Distillation
Methodology for starting a new library-reference distillation skill — one that turns an external library (nuqs, zod, framer-motion, msw, react-hook-form, emilkowal-animations) into an idiomatic-usage rule pack — or evolving one against a new upstream release. Distills the conventions empirically shared across shipped library-ref skills in this repo — the source-priority ladder (docs → blog/changelog → issues → types → examples), version pinning that inverts with API velocity, the universal 4-tier category ladder (CRITICAL setup → HIGH isolation → MEDIUM composition → LOW edge cases), the 4-slot When-to-Apply template, the failure-gap exemplar heuristic (privilege production lessons over API restatement), and metadata.references[] as cite-set checksum. Triggers on "I want to write a skill for library X", "refresh against new upstream", "where should I source rules from", "what categories should this skill have", and on /dev-skill:new for a library-reference distillation.
- Publisher / source
- pproenca/dot-skills (found on skills.sh)
- Upstream record
- pproenca/dot-skills
- Version
- Version 2
- Published
- Publication review
- Approved for publication
- Content fingerprint
b008b50bd59f659f4d737120e07888293d15a94117f3f7e364bbfba1fd4821b5