feat(WP-0013): IHF Phase 12 — Platform Memory and Continuous Learning

Closes the long-range feedback loop: outcome signals now enrich the full
traceability chain and feed back into routing, triage, and AI proposals.

Schema (T01):
- outcome_correlations (CHECK correlation_type)
- pattern_performance_records
- adaptive_threshold_configs
- institutional_knowledge_entries (GIN tsvector FTS)
- learning_insights (CHECK insight_type)
- ALTER TABLE decision_records + requirement_candidates: outcome_summary JSONB
- AFTER INSERT trigger trg_enrich_lineage on outcome_signals
- contracts/core/ updated (outcome-summary-columns-v1, append-only addendum)

Correlation engine (T02):
- Application/Helper/CorrelationEngine.hs: pure annotation→outcome SQL
- Web/Controller/OutcomeCorrelations.hs: ComputeCorrelationsAction + index

Pattern performance (T03):
- Web/Controller/PatternPerformance.hs: ComputePatternPerformanceAction

Adaptive thresholds (T04):
- Web/Controller/AdaptiveThresholds.hs: CalibrateThresholdsAction
- Application/Helper/FrictionScore.hs: applyAdaptiveWeights

Institutional knowledge (T05):
- DistilDecisionAction in DecisionRecords controller
- Web/Controller/InstitutionalKnowledge.hs: QueryKnowledgeBaseAction

Lineage enrichment (T06):
- Web/Controller/LineageEnrichment.hs: EnrichLineageAction (batch backfill)
- enrich_lineage_on_outcome_batch() PL/pgSQL helper in migration

Learning dashboard (T07):
- Web/Controller/LearningDashboard.hs: 5-panel autoRefresh view
- "Learning" nav link in FrontController

API v2 learning endpoints (T08):
- GET /api/v2/outcome-correlations, /pattern-performance, /knowledge-base/{id}
- OpenAPI schemas: OutcomeCorrelation, PatternPerformanceRecord, InstitutionalKnowledgeEntry

GAAF scorecard + docs (T09):
- Core 3.8→3.9, Functional 3.6→3.8, overall 3.61→3.68
- CLAUDE.md: IHF v0.2 complete, no active workplan

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Application/Helper/FrictionScore.hs

@@ -4,6 +4,8 @@ import IHP.Prelude
 import IHP.ModelSupport
 import Generated.Types
 import Data.Time.Clock (addUTCTime, getCurrentTime)
+import qualified Data.Aeson as A
+import qualified Data.HashMap.Strict as H
 
 -- | Friction score formula (documented):
 --
@@ -62,3 +64,35 @@ scoreBand s
     | s < 40    = "bg-yellow-100 text-yellow-800"
     | s < 60    = "bg-orange-100 text-orange-800"
     | otherwise = "bg-red-100 text-red-800"
+
+-- | Read per-hub AdaptiveThresholdConfig and apply weight_overrides
+-- to friction component scores before summing. Falls back to global
+-- defaults when no config exists for the hub.
+-- weight_overrides keys: "annotation", "error", "regression", "stale"
+applyAdaptiveWeights ::
+    (?modelContext :: ModelContext) =>
+    Id Hub ->
+    Int ->  -- annotationCount
+    Int ->  -- errorEventCount
+    Bool -> -- regressionFlag
+    Int ->  -- staleCandidateCount
+    IO Int
+applyAdaptiveWeights hubId annCount errCount isRegressed staleCount = do
+    mConfig <- query @AdaptiveThresholdConfig
+        |> filterWhere (#hubId, hubId)
+        |> fetchOneOrNothing
+    let overrides = maybe mempty (.weightOverrides) mConfig
+        w k def   = case overrides of
+            A.Object o -> case H.lookup k o of
+                Just (A.Number n) -> round (n * fromIntegral def) :: Int
+                _                 -> def
+            _ -> def
+        annW      = w "annotation" 5
+        errW      = w "error"      10
+        regW      = w "regression" 20
+        staleW    = w "stale"      8
+        raw       = annCount * annW
+                  + errCount * errW
+                  + (if isRegressed then regW else 0)
+                  + staleCount * staleW
+    pure (min 100 raw)