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/adapters/sqlite/src/models.rs

@@ -99,7 +99,6 @@ impl ReviewRow {
     }
 }
 
-// Used by query_diary JOIN — r.id aliased to review_id to avoid ambiguity with m.id
 #[derive(sqlx::FromRow)]
 pub(crate) struct DiaryRow {
     pub id: String,
@@ -173,7 +172,6 @@ impl MovieStatsRow {
     }
 }
 
-// Like DiaryRow but includes user_email from JOIN with users table
 #[derive(sqlx::FromRow)]
 pub(crate) struct FeedRow {
     pub id: String,