This one watches your screenpipe history, clusters the workflows you actually repeat, and drafts new Claude skills for the patterns that aren't already covered by the 135 skills in the repo. You run it on demand with a time window, it analyzes locally captured screen data, compares clusters against existing skills using local embeddings, then hands an LLM (defaults to LM Studio with Gemma) the redacted summaries to decide whether to compose existing skills or write new ones. Proposals land in a staging folder you review before promoting. The privacy model is solid: raw OCR never leaves your machine, screenpipe filters sensitive apps at capture time, and the detection pipeline is fully local. Useful if you want your skill library to grow from actual usage rather than guessing what you might need.
npx skills add https://github.com/k-dense-ai/scientific-agent-skills --skill autoskill