Audit pipeline PR #9: determinism + fact extraction + verifier gate + KB stats + context injection (PR #9)

Bundles PR #9's work for the audit pipeline:

- N=3 consensus on cloud inference (gpt-oss:120b parallel) with qwen3-coder:480b tie-breaker
- audit_discrepancies.jsonl logs N-run disagreements
- scrum_master reviews route through llm_team fact extraction; source="scrum_review"
- Verifier-gated persistence: drops INCORRECT, keeps UNVERIFIABLE/UNCHECKED; schema_version:2
- scrum_master_reviewed flag on accepted reviews
- auditor/kb_stats.ts: on-demand observability script
- claim_parser history/proof pattern class (verified-on-PR, was-flipping, the-proven-X)
- claim_parser quoted-string guard (mirrors static.ts fix)
- fact_extractor project context injection via docs/AUDITOR_CONTEXT.md
- Fixed verifier-verdict parser to handle multiple gemma2 output formats

Empirical: 3-run determinism test on unchanged PR #9 SHA showed 7/7 warn findings stable; block count oscillation eliminated; llm_team quality scores 8-9 on context-injected extract runs.

See PR #9 for full run-by-run commit history.

tests/real-world/scrum_master_pipeline.ts

@@ -343,12 +343,50 @@ Respond with markdown. Be specific, not generic. Cite file-region + PRD-chunk-of
       attempts_made: history.length,
       tree_split_fired: treeSplitFired,
       suggestions_preview: accepted.slice(0, 2000),
+      schema_version: 2,
+      scrum_master_reviewed: true,
     };
     try {
       await appendFile(SCRUM_REVIEWS_JSONL, JSON.stringify(row) + "\n");
     } catch (e) {
       console.error(`[scrum] failed to append scrum_reviews.jsonl: ${(e as Error).message}`);
     }
+
+    // Route the accepted review through llm_team's fact extractor so
+    // its entities + relationships land in audit_facts.jsonl alongside
+    // inference-side extractions. Same index, two sources. Tagged
+    // source:"scrum_review" + scrum_master_reviewed:true so downstream
+    // queries can filter by provenance. Reviews shorter than 120
+    // chars are skipped — they're usually one-liners ("LGTM") with
+    // no extractable knowledge.
+    if (accepted.length >= 120 && process.env.LH_SCRUM_SKIP_EXTRACT !== "1") {
+      try {
+        const { extractFacts } = await import("../../auditor/fact_extractor.ts");
+        const ex = await extractFacts(accepted);
+        if (!ex.error || ex.entities.length + ex.facts.length > 0) {
+          const factRow = {
+            pr_number: 0,                   // scrum runs outside a PR scope
+            file: rel,
+            head_sha: "",                    // no SHA scope; scope is the file+timestamp
+            extracted_at: ex.extracted_at,
+            extractor: ex.extractor_model,
+            verifier: ex.verifier_model,
+            llm_team_run_id: ex.llm_team_run_id ?? null,
+            facts: ex.facts,
+            entities: ex.entities,
+            relationships: ex.relationships,
+            verification_preview: ex.verification.slice(0, 400),
+            schema_version: 2,
+            source: "scrum_review",
+            scrum_master_reviewed: true,
+          };
+          const AUDIT_FACTS_JSONL = "/home/profit/lakehouse/data/_kb/audit_facts.jsonl";
+          await appendFile(AUDIT_FACTS_JSONL, JSON.stringify(factRow) + "\n");
+        }
+      } catch (e) {
+        console.error(`[scrum] fact extraction failed for ${rel}: ${(e as Error).message}`);
+      }
+    }
   }
 
   return review;