infra: replace gpt-oss with Ollama Pro + OpenCode Zen across hot paths

Ollama Pro plan went live today (39-model fleet on the same
OLLAMA_CLOUD_KEY) and OpenCode Zen was already wired in the gateway
but not consumed. Routing every gpt-oss call site to faster /
stronger replacements:

| Site | gpt-oss → replacement | Why |
|---|---|---|
| ollama_cloud default | gpt-oss:120b → deepseek-v3.2 | newest DeepSeek revision; live-probed `pong` |
| openrouter default | openai/gpt-oss-120b:free → x-ai/grok-4.1-fast | already the scrum LADDER's PRIMARY |
| modes.toml staffing_inference | openai/gpt-oss-120b:free → kimi-k2.6 | coding-specialized, on Ollama Pro |
| modes.toml doc_drift_check | gpt-oss:120b → gemini-3-flash-preview | speed leader for factual checks |
| scrum_master_pipeline tree-split MAP+REDUCE | gpt-oss:120b → gemini-3-flash-preview | latency-dominated path (5-20× per file) |
| bot/propose.ts CLOUD_MODEL | gpt-oss:120b → deepseek-v3.2 | same Ollama key, faster |
| mcp-server/observer.ts overseer label fallback | gpt-oss:120b → claude-opus-4-7 | matches new overseer model |
| crates/gateway/src/execution_loop overseer escalation | ollama_cloud/gpt-oss:120b → opencode/claude-opus-4-7 | frontier reasoning matters here — fires only after local self-correct fails twice; Zen pay-per-token cost is bounded |

Verification:
- `cargo check -p gateway --tests` — clean
- Live probes through localhost:3100/v1/chat:
  - `opencode/claude-opus-4-7` → "pong"
  - `gemini-3-flash-preview` (ollama_cloud) → "pong"
  - `kimi-k2.6` (ollama_cloud) → "pong"
  - `deepseek-v3.2` (ollama_cloud) → "Pong! 🏓"

Notes:
- kimi-k2:1t still upstream-broken (HTTP 500 on Ollama Pro probe today,
  matches yesterday's memory). Replacement table never picks it.
- The Rust changes need a `systemctl restart lakehouse.service` to
  take effect on the running gateway. TS callers reload on next run.
- aibridge/src/context.rs still has gpt-oss:{20b,120b} in its window-
  size lookup table; harmless and kept for callers that pass it
  explicitly as an override.

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

crates/gateway/src/execution_loop/mod.rs

@@ -582,10 +582,10 @@ impl ExecutionLoop {
     /// Phase 20 step (8) — T3 overseer escalation.
     ///
     /// When the local executor/reviewer loop can't self-correct, call
-    /// the cloud overseer (`gpt-oss:120b` via Ollama Cloud) with (a)
-    /// the KB context — recent outcomes + prior corrections for this
-    /// sig_hash + task_class, across every profile that has run it —
-    /// and (b) the recent log tail. Its output is appended as a
+    /// the cloud overseer (`claude-opus-4-7` via OpenCode Zen) with
+    /// (a) the KB context — recent outcomes + prior corrections for
+    /// this sig_hash + task_class, across every profile that has run
+    /// it — and (b) the recent log tail. Its output is appended as a
     /// `system` role turn so the next executor generation sees it,
     /// AND written to `data/_kb/overseer_corrections.jsonl` so every
     /// future profile activation reads from the same learning pool.
@@ -593,9 +593,16 @@ impl ExecutionLoop {
     /// This is the "pipe to the overviewer" piece from 2026-04-23 —
     /// the overseer is now a first-class KB consumer AND producer, not
     /// a one-shot correction oracle.
+    ///
+    /// 2026-04-28: routed through OpenCode (Zen tier) for Claude Opus
+    /// 4.7. Frontier reasoning matters here because the overseer fires
+    /// only after local self-correction has failed twice — by that
+    /// point we need the strongest reasoning available, not the
+    /// cheapest token. Frequency is low so the Zen pay-per-token cost
+    /// stays bounded.
     async fn escalate_to_overseer(&mut self, turn: u32, reason: &str) -> Result<(), String> {
-        let Some(cloud_key) = self.state.ollama_cloud_key.clone() else {
-            return Err("OLLAMA_CLOUD_KEY not configured — skipping escalation".into());
+        let Some(opencode_key) = self.state.opencode_key.clone() else {
+            return Err("OPENCODE_API_KEY not configured — skipping escalation".into());
         };
 
         let kb = KbContext::load_for(&sig_hash(&self.req), &self.req.task_class).await;
@@ -604,16 +611,18 @@ impl ExecutionLoop {
         let started = std::time::Instant::now();
         let start_time = chrono::Utc::now();
         let chat_req = crate::v1::ChatRequest {
-            model: "gpt-oss:120b".to_string(),
+            model: "claude-opus-4-7".to_string(),
             messages: vec![crate::v1::Message::new_text("user", prompt.clone())],
             temperature: Some(0.1),
             max_tokens: None,
             stream: Some(false),
-            think: Some(true),    // overseer KEEPS thinking (Phase 20 rule)
-            provider: Some("ollama_cloud".into()),
+            // Anthropic models on opencode reject `think` (handled in
+            // the adapter), but we keep the intent flag for parity.
+            think: Some(true),
+            provider: Some("opencode".into()),
         };
-        let resp = crate::v1::ollama_cloud::chat(&cloud_key, &chat_req).await
-            .map_err(|e| format!("ollama_cloud: {e}"))?;
+        let resp = crate::v1::opencode::chat(&opencode_key, &chat_req).await
+            .map_err(|e| format!("opencode: {e}"))?;
         let latency_ms = started.elapsed().as_millis() as u64;
         let end_time = chrono::Utc::now();
         let correction_text: String = resp.choices.into_iter().next()
@@ -633,8 +642,8 @@ impl ExecutionLoop {
         if let Some(lf) = &self.state.langfuse {
             use crate::v1::langfuse_trace::ChatTrace;
             lf.emit_chat(ChatTrace {
-                provider: "ollama_cloud".into(),
-                model: "gpt-oss:120b".into(),
+                provider: "opencode".into(),
+                model: "claude-opus-4-7".into(),
                 input: vec![crate::v1::Message::new_text("user", prompt.clone())],
                 output: correction_text.clone(),
                 prompt_tokens: resp.usage.prompt_tokens,
@@ -650,7 +659,7 @@ impl ExecutionLoop {
 
         // Append to the transcript so the next executor turn sees it.
         self.append(LogEntry::new(
-            turn, "system", "gpt-oss:120b", "overseer_correction",
+            turn, "system", "claude-opus-4-7", "overseer_correction",
             serde_json::json!({
                 "reason": reason,
                 "correction": correction_text,
@@ -672,7 +681,7 @@ impl ExecutionLoop {
             "task_class": self.req.task_class,
             "operation": self.req.operation,
             "reason": reason,
-            "model": "gpt-oss:120b",
+            "model": "claude-opus-4-7",
             "correction": correction_text,
             "applied_at_turn": turn,
             "kb_context_used": kb,