Phase 2 was straightforward. The design was already locked in entry 10. The only real decision was what to do with malformed optional fields.

The six extended patterns-garden fields — observed_in, suitability, variants, variant_frequency, authors, stability — are all optional. Someone submitting a pattern doesn’t have to fill in the provenance chain or the developer attribution. But if they do fill it in and get the structure wrong (observed_in: not-a-list, authors[0].role: inventor), what should the validator do?

I made it a WARNING, not a CRITICAL. The reasoning: these fields are enrichment — they improve retrieval quality and attribution, but the entry is valid without them. Blocking a contribution because the provenance list is malformed would be the wrong trade-off. The validator tells you it’s wrong; it doesn’t refuse to accept the entry.

The implementation split cleanly into three: validate_patterns_extended(fm) as a pure function returning warning strings, wiring it into validate() only when garden == 'patterns', and verifying that non-patterns gardens are completely unaffected. The isolation test — inject a malformed observed_in into a discovery entry, confirm no warning fires — is the one that would catch a future regression if someone forgets the guard.

We built it in seven tasks, subagent-driven, spec plus code quality review after each. Nothing notable went wrong. The code quality reviewer on Task 1 noted that VALID_STABILITY was defined but not yet used — correct, it gets used in Task 2. The reviewers confirmed each task matched spec before moving on.

747 tests on main. validate_pr.py now handles both the 6-garden taxonomy from Phase 1 and the patterns-garden extended schema from Phase 2.

Phase 3 is the project registry and ecosystem mining pipeline — the first part that requires new infrastructure rather than just extending the validator.