feat(opt): three efficiency levers for NL→SQL workflow

Lever 1: Join-graph via FK logics in annotations + suggest-fks command
  - TableAnnotation.foreign_keys field stores curated logical FKs (DWH has no FK constraints)
  - tht schema suggest-fks: mine from approved SQL, heuristics (time_key → dim_time),
    same-name discovery + explicit --assume flag for multi-owner PKs
  - mschema renders 【Foreign keys】 section populated; validation in merge.py
  - SKILL.md F4 now reads FKs from mschema-text, no custom data_time_key logic

Lever 2: Context-pack consolidation at kickoff (tht search pack)
  - Single embedding of question, reused for schema + evidence + solved searches
  - One command: tht search pack <question> --session <id> → retrieval_pack.md
  - Graceful degradation when Ollama/vector store unreachable (exit 0, empty sections)
  - SKILL.md F1 prescribes as first call; reduces model thinking turns via pre-retrieval

Lever 3: Phase-summary recap v2 auto-construction from session ledger
  - tht session show --json includes full decisions ledger
  - tht phase meta --json exports 'emits' (substantive decision types per phase)
  - Gate appends deterministic 【Decisioni registrate in questa fase】 section (appendLedgerSection)
  - Model authors only summary + checks; recap table comes from persisted state (exact by construction)
  - SKILL.md Disciplina 6: brief model output, gate fills the rest

Tests: 358 Python (including 10 FK + 3 pack + 1 session-ledger tests) + 111 JS gate tests, all pass.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

harness/.pi/skills/tht-sessione/sql-generation.md

@@ -21,9 +21,9 @@ copy-pasteable: no rationale comments (that lives in the audit artifacts).
 ## Time dimension (analysis by year/month/quarter)
 
 Fact tables have `data_time_key` (`integer`, format `YYYYMMDD`): it is the FK to
-`dim_time.day_key`. **This FK is NOT declared** in the DWH (facts have
-`foreign_keys: []`), so it will NOT appear in `schema_linking.json`: you must add it
-by hand to the join.
+`dim_time.day_key`. The DWH does not declare it, but the workspace annotations do:
+it appears in the `【Foreign keys】` section of the mschema-text render (every
+`*_time_key` column maps to `dim_time.day_key`) — take it from there for the join.
 
 - To extract year, month, quarter, semester etc. do
   `JOIN dim_time dt ON dt.day_key = <fact>.data_time_key` and use the dimension's