OpenRouter rescue ladder + tree-split reduce fix + observer→LLM Team + scrum_applier + first auto-applied patch

## Infrastructure (scrum loop hardening)

crates/gateway/src/v1/openrouter.rs — new OpenRouter provider
  Direct HTTPS to openrouter.ai/api/v1/chat/completions with OpenAI-compatible shape.
  Key resolution: OPENROUTER_API_KEY env → /home/profit/.env → /root/llm_team_config.json
  (shares LLM Team UI's quota). Added after iter 5 hit repeated Ollama Cloud 502s on
  kimi-k2:1t — different provider backbone as rescue rung. Unit tests pin the URL
  stripping and OpenAI wire shape.

crates/gateway/src/v1/mod.rs + main.rs
  Added `"openrouter" | "openrouter_free"` arm to /v1/chat dispatch.
  V1State.openrouter_key loaded at startup via openrouter::resolve_openrouter_key()
  mirroring the Ollama Cloud pattern. Startup log:
    "v1: OpenRouter key loaded — /v1/chat provider=openrouter enabled"

tests/real-world/scrum_master_pipeline.ts
  * 9-rung ladder — kimi-k2:1t → qwen3-coder:480b → deepseek-v3.1:671b →
    mistral-large-3:675b → gpt-oss:120b → qwen3.5:397b → openrouter/gpt-oss-120b:free
    → openrouter/gemma-3-27b-it:free → local qwen3.5:latest.
    Added qwen3-coder:480b as rung 2 after live probes confirmed it rescues
    kimi-k2:1t 502s cleanly (0.9s latency, substantive reviews).
    Dropped devstral-2 (displaced by qwen3-coder); dropped kimi-k2.6 (not available);
    dropped minimax-m2.7 (returned 0 chars / 400 thinking tokens).
    Local fallback promoted qwen3.5:latest per J's direction 2026-04-24.
  * MAX_ATTEMPTS bumped 6 → 9 to accommodate the rescue tier.
  * Tree-split scratchpad fixed — was concatenating shard markers directly
    into the reviewer input, causing kimi-k2:1t to write titles like
    "Forensic Audit Report – file.rs (shard 3)". Now uses internal §N§
    markers during accumulation and runs a proper reduce step that
    collapses per-shard digests into ONE coherent file-level synthesis
    with markers stripped. Matches the Phase 21 aibridge::tree_split
    map→reduce design. Fallback to stripped scratchpad if reducer returns thin.

tests/real-world/scrum_applier.ts — NEW (737 lines)
  The auto-apply pipeline. Reads scrum_reviews.jsonl, filters rows where
  gradient_tier ∈ {auto, dry_run} AND confidence_avg ≥ MIN_CONF (default 90),
  asks the reviewer model for concrete old_string/new_string patch JSON,
  applies via text replacement, runs cargo check after each file, commits
  if green and reverts if red. Deny-list: /etc/, config/, ops/, auditor/,
  docs/, data/, mcp-server/, ui/, sidecar/, scripts/. Hard caps: per-patch
  confidence ≥ MIN_CONF, old_string must be exactly unique, max 20 lines per
  patch. Never runs on main without explicit LH_APPLIER_BRANCH override.
  Audit trail in data/_kb/auto_apply.jsonl.

  Empirical behavior (dry-run over iter 4 reviews):
    5 eligible files → 1 green commit-ready, 2 build-red reverts, 2 all-rejected
  The build-green gate caught 2 bad patches before they'd have merged.

mcp-server/observer.ts — LLM Team code_review escalation
  When a sig_hash accumulates ≥3 failures (ESCALATION_THRESHOLD), fire-and-forget
  POST /api/run?mode=code_review at localhost:5000 with the failure cluster context.
  Parses facts/entities/relationships/file_hints from the response. Writes to a
  new data/_kb/observer_escalations.jsonl surface. Answers J's vision of the
  observer triggering richer LLM Team calls when failures pile up.
  Non-blocking: runs parallel to existing qwen2.5 analyzer, never replaces it.
  Tracks escalated sig_hashes in a session-local Set to avoid re-hammering
  LLM Team when a cluster persists across observer cycles.

crates/aibridge/src/context.rs
  First auto-applied patch produced by scrum_applier.ts (dry-run path —
  applier writes files in dry-run mode but doesn't commit; bug noted for
  iter 6 fix). Adds #[deprecated] annotation to the inline estimate_tokens
  helper pointing callers to the centralized shared::model_matrix::ModelMatrix
  entry point (P21-002 — duplicate token-estimator surfaces). Cargo check
  passes with the annotation (verified by applier's own build gate).

## Visual Control Plane (UI)

ui/server.ts — Bun.serve on :3950 with /data/* fan-out:
  /data/services, /data/reviews, /data/metrics, /data/trust, /data/overrides,
  /data/findings, /data/outcomes, /data/audit_facts, /data/file/:path,
  /data/refactor_signals, /data/search?q=, /data/signal_classes,
  /data/logs/:svc (journalctl tail per systemd unit), /data/scrum_log.
  Bug fix: tryFetch always attempts JSON.parse before falling back to text
  — observer's Bun.serve returns JSON without application/json content-type,
  which was displaying stats as a raw string ("0 ops" on map) before.

ui/index.html + ui.css — dark neo-brutalist shell. 6 views:
  MAP (D3 force-graph + overlays) / TRACE (per-file iter history) /
  TRAJECTORY (signal-class cards + refactor-signals table + reverse-index
  search box) / METRICS (every card has SOURCE + GOOD lines explaining
  where the number comes from and what target trajectory means) /
  KB (card grid with tooltips on every field) / CONSOLE (per-service
  journalctl tabs).

ui/ui.js — polling client, D3 wiring, signal-class panel, refactor-signals
  table, reverse-index search, per-service console tabs. Bug fix:
  renderNodeContext had Object.entries() iterating string characters when
  /health returned a plain string — now guards with typeof check so
  "lakehouse ok" renders as one row instead of "0 l / 1 a / 2 k / ...".

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

mcp-server/observer.ts

@@ -141,6 +141,93 @@ async function persistOp(op: ObservedOp) {
 }
 
 
+// ─── LLM Team escalation (code_review mode) ───
+//
+// When recent failures on a single sig_hash cross a threshold the
+// local qwen2.5 analysis is probably insufficient. J's 2026-04-24
+// direction: "the observer would trigger to give more context" —
+// route failure clusters to LLM Team's specialized code_review mode
+// (via /api/run) so richer structured signal lands in the KB for
+// scrum + auditor + playbook memory to consume next pass.
+//
+// Non-destructive: runs in parallel to the existing qwen2.5 analysis,
+// never replaces it. Writes to data/_kb/observer_escalations.jsonl
+// as a dedicated audit surface.
+
+const LLM_TEAM = process.env.LH_LLM_TEAM_URL ?? "http://localhost:5000";
+const LLM_TEAM_ESCALATIONS = "/home/profit/lakehouse/data/_kb/observer_escalations.jsonl";
+const ESCALATION_THRESHOLD = 3; // N+ failures on same sig_hash triggers
+
+async function escalateFailureClusterToLLMTeam(sigHash: string, cluster: ObservedOp[]) {
+  // Package the failure cluster as a single context blob for code_review mode.
+  const context = cluster.slice(-8).map((o, i) =>
+    `[${i + 1}] endpoint=${o.endpoint} input=${o.input_summary} error=${o.error ?? "?"}`
+  ).join("\n");
+
+  try {
+    const resp = await fetch(`${LLM_TEAM}/api/run?mode=code_review`, {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({
+        input: `sig_hash=${sigHash} · ${cluster.length} failures on same signature:\n\n${context}\n\nReview this failure pattern. What is the root cause? What code change would prevent it? Respond with structured facts + specific file hints.`,
+      }),
+      signal: AbortSignal.timeout(60000),
+    });
+    if (!resp.ok) {
+      console.error(`[observer] LLM Team code_review ${resp.status}: ${(await resp.text()).slice(0, 200)}`);
+      return;
+    }
+    const j: any = await resp.json();
+
+    // Write an audit row. Fields are deliberately permissive — LLM
+    // Team's response shape can evolve without breaking this write.
+    const row = {
+      ts: new Date().toISOString(),
+      source: "observer_escalation",
+      mode: "code_review",
+      sig_hash: sigHash,
+      cluster_size: cluster.length,
+      cluster_staffer: cluster[0]?.staffer_id,
+      cluster_endpoint: cluster[0]?.endpoint,
+      llm_team_run_id: j.run_id ?? j.llm_team_run_id ?? null,
+      facts: j.facts ?? [],
+      entities: j.entities ?? [],
+      relationships: j.relationships ?? [],
+      raw_response: typeof j.response === "string" ? j.response.slice(0, 2000) : null,
+      recommended_files: j.file_hints ?? j.files ?? [],
+    };
+    const { appendFile } = await import("node:fs/promises");
+    await appendFile(LLM_TEAM_ESCALATIONS, JSON.stringify(row) + "\n");
+    console.error(
+      `[observer] escalated sig_hash=${sigHash.slice(0, 8)} · cluster=${cluster.length} · facts=${row.facts.length} entities=${row.entities.length}`
+    );
+  } catch (e) {
+    console.error(`[observer] LLM Team escalation failed: ${(e as Error).message}`);
+  }
+}
+
+// Track which sig_hashes we've already escalated this session so we
+// don't hammer LLM Team on every analyzeErrors tick when a cluster
+// persists across cycles.
+const escalatedSigHashes = new Set<string>();
+
+async function maybeEscalate(failures: ObservedOp[]) {
+  // Group failures by sig_hash
+  const bySig = new Map<string, ObservedOp[]>();
+  for (const f of failures) {
+    const k = f.sig_hash ?? "__no_sig__";
+    (bySig.get(k) ?? bySig.set(k, []).get(k)!).push(f);
+  }
+  for (const [sigHash, cluster] of bySig) {
+    if (sigHash === "__no_sig__") continue;
+    if (cluster.length < ESCALATION_THRESHOLD) continue;
+    if (escalatedSigHashes.has(sigHash)) continue;
+    escalatedSigHashes.add(sigHash);
+    // Fire-and-forget — don't block the existing analyzer loop.
+    escalateFailureClusterToLLMTeam(sigHash, cluster).catch(() => {});
+  }
+}
+
 // ─── Error analyzer loop ───
 
 async function analyzeErrors() {
@@ -148,6 +235,11 @@ async function analyzeErrors() {
   const failures = recentOps.filter(op => !op.success);
   if (failures.length === 0) return;
 
+  // NEW 2026-04-24: escalate recurring sig_hash clusters to LLM Team
+  // code_review mode. Runs in parallel to the local qwen2.5 analysis
+  // below — non-blocking, richer downstream signal for scrum/auditor.
+  maybeEscalate(failures).catch(() => {});
+
   const errorSummary = failures.slice(-10).map(f =>
     `[${f.endpoint}] ${f.input_summary}: ${f.error}`
   ).join("\n");