fix(diagnostician): 修復 'AI 深度診斷' 垃圾根因顯示

根因三層鏈：
1. openclaw.call(prompt) 不傳 context
2. OPENCLAW_NEMO fallback 把 prompt[:500]（系統說明文字）當 signal description
3. Nemo LLM 回傳 action_title="調查 AWOOOI SRE 系統的偵探 Agent"（任務描述）
4. _extract_hypotheses() 用 action_title 作為根因假設描述 → Telegram 顯示垃圾

修復：
- openclaw.call() 新增 alert_context 可選參數，透傳給 _call_with_fallback
- diagnostician._analyze() 建立 alert_context（incident_id + evidence_summary as signal）
  → nemo 使用結構化 API 收到真實感應器資料而非系統說明文字
- _extract_hypotheses() nemo 格式轉換：優先用 reasoning（為什麼）作為假設描述
  而非 action_title（做什麼）— reasoning 更接近根因分析

2026-04-16 ogt + Claude Sonnet 4.6 (台北時區)

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

apps/api/src/agents/diagnostician_agent.py

@@ -102,9 +102,21 @@ class DiagnosticianAgent(BaseAgent):
             "anomaly_context": snapshot.anomaly_context,
         })
 
+        # 2026-04-16 ogt + Claude Sonnet 4.6: 傳遞 snapshot 結構化資料給 OPENCLAW_NEMO
+        # 根因：原本 call(prompt) 不傳 context → nemo fallback 把 prompt[:500]（系統說明）
+        #       當 signal description → LLM 回傳 "調查 AWOOOI SRE 系統的偵探 Agent" 垃圾
+        # 修復：把 snapshot.evidence_summary 放進 alert_context.signals 讓 nemo 看到真實資料
+        _evidence = (snapshot.evidence_summary or "（待感應器資料）")[:800]
+        alert_context = {
+            "incident_id": snapshot.snapshot_id or "UNKNOWN",
+            "severity": "P3",
+            "signals": [{"alert_name": "evidence_snapshot", "description": _evidence}],
+            "affected_services": [],
+        }
+
         from src.services.openclaw import get_openclaw
         openclaw = get_openclaw()
-        response_text, _provider, success = await openclaw.call(prompt)
+        response_text, _provider, success = await openclaw.call(prompt, alert_context=alert_context)
 
         if not success or not response_text:
             return self._degraded_report(snapshot, 0, reason="llm_failed")
@@ -223,8 +235,13 @@ def _extract_hypotheses(parsed: dict[str, Any]) -> list[Hypothesis]:
                        "medium": "ModerateIssue", "low": "LowRisk"}
         category = risk_to_cat.get(risk_level.lower(), "Unknown")
         if action_title and confidence > 0:
+            # 2026-04-16 ogt + Claude Sonnet 4.6: 優先用 reasoning 作為假設描述
+            # reasoning（解釋「為什麼」採取行動）比 action_title（「做什麼」）更接近根因
+            # 例: reasoning="CPU 95%, 系統過載" vs action_title="重啟 Pod"
+            nemo_reasoning = str(parsed.get("reasoning", "")).strip()
+            description = nemo_reasoning[:500] if len(nemo_reasoning) > 20 else action_title[:500]
             return [Hypothesis(
-                description=action_title[:500],
+                description=description,
                 confidence=confidence,
                 evidence_chain=[],
                 category=category,