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:

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: counterexamples and failure boundaries;
• evidence: only measured scores, verification status, and optional SHA-256 artifact hashes.

Do not include project names, URLs, paths, emails, source snippets, prompts, screenshots, colors, font names, or component names. Then capture it:

node <installed-ss-learn>/scripts/learning.mjs capture \
  --project-root . \
  --input /path/to/candidate.json

The CLI validates maintained context IDs, exact fields, privacy patterns, and evidence honesty. It writes an immutable draft ID under .styleseed/learning/candidates/.