ss-learn

# Learn from project design decisions `ss-learn` turns a **specific human-approved correction** into a generalized candidate rule. It does not train a model, scrape a repository, or upload telemetry. The CLI is local-only. An optional plugin MCP bridge can expose one exact package to its connected client/model only after a separate one-time human grant. Read `references/privacy-contract.md` before using this skill. ## When not to use - The user did not explicitly ask to capture or share a lesson. - The change was accepted only by the agent, not a person. - The lesson cannot be expressed without client/product identity, source code, a screenshot, proprietary tokens, or user content. - A score or visual pass was not actually measured. Record it as `null` or `not-run`; never infer. - The observation belongs only to one project's taste. Keep it in `STYLESEED.md` instead. ## 1. Initialize local learning After explicit user approval: ```bash node <installed-ss-learn>/scripts/learning.mjs init --project-root . ``` This creates `.styleseed/learning/config.json` with sharing disabled and all raw-material collection disabled. It performs no network request. ## 2. Draft a candidate Use `references/candidate.schema.json`. Generalize the lesson: - problem: what design failure was observed; - intervention: what bounded change the person accepted; - rationale: why it improved the product job; - appliesWhen: conditions where the judgment should transfer; - avoidWhen: counterexam...