feat(graph): dynamic force-directed Team-memory graph + honest metrics (on main's pipeline)

Lands the dynamic Team-memory redesign on top of main's continual-learning
pipeline. main already computes loop/activity in the API but its GraphView never
rendered them and kept a static-column graph; this swaps in the dynamic graph and
surfaces the rails, reusing main's richer PodLearningLoop / PodGraphActivity types.

Frontend (new frontend/src/components/graph/*, composed into GraphView.tsx):
- forceSim.ts: dependency-free force layout (charge, link springs, centroid
  recenter, 2-pass collision, bounds clamp, alpha anneal) — no new deps/lockfile churn.
- GraphCanvas.tsx: SVG render from the sim — draggable + pinnable nodes, curved
  edges, weight-sized shapes, fade-in, animated learned_from dash, risk-path
  lighting / rest dimmed, label collision-avoidance.
- MetricsRail / LearningLoop / ActivityStream / SelectedNodePanel / encoding.ts:
  the mock's rails + stream + detail panel in light shadcn. LearningLoop consumes
  main's PodLearningLoop (steps + activeStep); ActivityStream consumes
  PodGraphActivity (title + detail). SelectedNodePanel adds a Flow section that
  narrates the path through a clicked node (flowNarrative); default copy is
  mode-aware. Edge legend rounded out (editing/touches).
- GraphView polls every 5s and diffs (positions preserved), + ws /api/events nudge.
  Stale selection (node gone across a poll) dropped so the canvas can't dim entirely.

Backend (surgical — main's materializer + buildLoop kept):
- live.ts: headline metric cards derived from the FINAL de-noised graph (Open risk
  paths = distinct collision files; Learned owners = distinct owner engineers)
  instead of raw collision-signature / accepted-outcome counts that inflate with
  test churn (50 -> 4 risk files, 16 -> 1 owner on live). buildLoop untouched.
- demo.ts: metrics realigned to the demo graph (3 owners / 1 risk path / 100%).

Verified: lint + -r typecheck + -r build pass; Playwright confirmed the dynamic
graph (ticks on load, draggable), the loop rail (5 steps, ADAPT active) and
activity stream rendering main's shapes, honest metrics (3/1/100%), and the flow
narrative per node kind — zero page errors.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

backend/src/graph/demo.ts

@@ -11,21 +11,23 @@ export function createDemoPodGraph(podId: string): PodGraph {
   return {
     podId,
     generatedAt: new Date().toISOString(),
+    // Kept consistent with the graph below (3 owner engineers, 1 collision file,
+    // 1 of 1 interventions accepted) so the numbers never contradict the picture.
     metrics: [
       {
         label: 'Learned owners',
-        value: '5',
-        detail: 'Ownership edges retained from accepted interventions.',
+        value: '3',
+        detail: 'Distinct owners retained from accepted interventions.',
       },
       {
         label: 'Open risk paths',
-        value: '2',
-        detail: 'auth.ts and the memory API have converging editors.',
+        value: '1',
+        detail: 'File with two or more converging editors.',
       },
       {
         label: 'Accept rate',
-        value: '86%',
-        detail: 'Interventions accepted this session (+14%).',
+        value: '100%',
+        detail: 'Interventions accepted vs total this session.',
       },
     ],
     loop: {

backend/src/graph/live.ts

@@ -502,6 +502,7 @@ export async function materializePodGraph(podId: string): Promise<PodGraph | nul
 
   const acceptedReal = outcomeDocs.filter((o) => o.accepted && o.wasRealCollision).length;
   const totalOutcomes = outcomeDocs.length;
+  // Raw distinct collision signatures — kept for the learning-loop throughput view.
   const riskPaths = new Set(
     collisionDocs.map(
       (col) =>
@@ -509,21 +510,38 @@ export async function materializePodGraph(podId: string): Promise<PodGraph | nul
         `${normalizeFile(col.file)}#${col.symbol ?? ''}`,
     ),
   ).size;
+
+  // Headline metric cards are derived from the FINAL de-noised graph so they match
+  // what's drawn. Counting raw collision signatures / accepted-outcome rows inflates
+  // them with test churn (e.g. 50 "risk paths" for 4 files), which reads as fake.
+  const finalEdges = [...b.edges.values()];
+  const riskFiles = new Set<string>();
+  for (const e of finalEdges) {
+    if (e.kind === 'touches' && b.nodes.get(e.source)?.kind === 'file') riskFiles.add(e.source);
+  }
+  const openRiskPaths = riskFiles.size || nodes.filter((n) => n.kind === 'collision').length;
+  const ownerSet = new Set<string>();
+  for (const e of finalEdges) {
+    if (e.kind === 'learned_from') ownerSet.add(e.target);
+    if (e.kind === 'owns') ownerSet.add(e.source);
+  }
+  const learnedOwners = [...ownerSet].filter((id) => b.nodes.get(id)?.kind === 'engineer').length;
+
   const metrics: PodGraphMetric[] = [
     {
       label: 'Learned owners',
-      value: String(acceptedReal),
-      detail: 'Ownership retained from accepted interventions.',
+      value: String(learnedOwners),
+      detail: 'Distinct owners retained from accepted interventions.',
     },
     {
       label: 'Open risk paths',
-      value: String(riskPaths),
-      detail: 'Files with two or more converging editors.',
+      value: String(openRiskPaths),
+      detail: `${openRiskPaths === 1 ? 'File' : 'Files'} with two or more converging editors.`,
     },
     {
       label: 'Accept rate',
       value: totalOutcomes ? `${Math.round((acceptedReal / totalOutcomes) * 100)}%` : '—',
-      detail: 'Interventions accepted this session.',
+      detail: 'Interventions accepted vs total this session.',
     },
   ];
   const learnedEdges = [...b.edges.values()].filter((e) => e.kind === 'learned_from').length;