feat: always-on Insights — correlation analysis over viewing habits

Conservative, live insights (ADR-0010) derived from each user's diary:
rating lift by genre (first-time watches only), genre seasonality vs a
uniform 1/12 month baseline, medium weekend-lift, and watch-time rhythm.

- domain: WatchRow model, InsightsQuery port, pure insights_analyzer with
  all honesty thresholds (sample minimums, strength cutoffs, suppressions
  for import noise), unit-tested
- adapters: thin Sqlite/Postgres fetch twins, reusing WrapUp's genre fetch
- application: get_insights use case, InsightsGroup in Deps
- composition/server: wiring through Repositories/DatabaseOutput
- presentation: GET /api/v1/insights handler, typed serde-tagged DTOs,
  openapi doc
- spa: /insights route (shadcn cards), insights feature hook, profile
  entry link, en locale strings
- server test: end-to-end seed sqlite → HTTP → rating lift + rhythm

Also records ADR-0010, defines "Insight" in the glossary, and includes
pre-existing worktree comment-stripping cleanups.

crates/tui/src/tests/app.rs

@@ -501,9 +501,6 @@ fn auth_ok_uses_app_api_url_for_main_state() {
 
 // ── parse_csv ─────────────────────────────────────────────────────────────
 
-// CSV column order matches the export format:
-// title,year,director,rating,comment,watched_at,external_metadata_id
-
 #[test]
 fn parse_csv_valid_row_with_title() {
     let csv = "title,year,director,rating,comment,watched_at,external_metadata_id\nThe Matrix,1999,Wachowski,5,,1999-03-31T00:00:00,\n";