refactor: ClawBot → OpenClaw 全域更名

- 刪除舊版 clawbot.py (已有新版 openclaw.py) - 更新 models/ai.py 類型定義 (ClawBotAnalysisRequest/Response) - 更新 api/v1/ai.py import 與註解 - 更新 Discord username - 更新所有註解與文檔依據: feedback_openclaw_naming.md (統帥 2026-03-20 正式命名決議) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
2026-03-24 12:57:36 +08:00
parent fb62aa06f0
commit 8159d22db9
13 changed files with 49 additions and 752 deletions
--- a/apps/api/src/api/v1/ai.py
+++ b/apps/api/src/api/v1/ai.py
@@ -1,14 +1,14 @@
 """
 AI Decision API
 ================
-CAI-101: ClawBot 自動化立案 API
+CAI-101: OpenClaw 自動化立案 API

 Endpoints:
 - POST /api/v1/ai/analyze-and-propose

 流程:
 1. 拉取當前監控數據 (host_aggregator)
-2. 交給 ClawBot AI 分析
+2. 交給 OpenClaw AI 分析
 3. 若需要修復 → 自動建立 ApprovalRecord
 4. 前端戰情室即時拉取待簽核卡片
 """
@@ -19,8 +19,8 @@ from src.core.logging import get_logger
 from src.core.trust_engine import get_trust_engine
 from src.models.ai import (
    AIRiskLevel,
-    ClawBotAnalysisRequest,
-    ClawBotAnalysisResponse,
+    OpenClawAnalysisRequest,
+    OpenClawAnalysisResponse,
    OpenClawDecision,
    SuggestedAction,
 )
@@ -87,7 +87,7 @@ def _create_approval_from_decision(decision: OpenClawDecision) -> ApprovalReques
                message=decision.risk_level.value.upper(),
            ),
        ],
-        requested_by="ClawBot",
+        requested_by="OpenClaw",
    )


@@ -97,19 +97,19 @@ def _create_approval_from_decision(decision: OpenClawDecision) -> ApprovalReques

@router.post(
    "/analyze-and-propose",
-    response_model=ClawBotAnalysisResponse,
+    response_model=OpenClawAnalysisResponse,
    summary="AI 分析並自動立案",
-    description="拉取當前監控數據，交給 ClawBot 分析。若判定需要修復，自動建立 ApprovalRecord。",
+    description="拉取當前監控數據，交給 OpenClaw 分析。若判定需要修復，自動建立 ApprovalRecord。",
 )
 async def analyze_and_propose(
-    request: ClawBotAnalysisRequest | None = None,
-) -> ClawBotAnalysisResponse:
+    request: OpenClawAnalysisRequest | None = None,
+) -> OpenClawAnalysisResponse:
    """
    AI 智能分析與自動立案

    流程:
    1. 從 host_aggregator 取得最新狀態
-    2. 交給 ClawBot AI 分析
+    2. 交給 OpenClaw AI 分析
    3. 解析 JSON 結構化輸出
    4. 若 suggested_action != NO_ACTION → 建立 ApprovalRecord
    """
@@ -119,7 +119,7 @@ async def analyze_and_propose(
    try:
        snapshot = await HostAggregator.fetch_all()

-        # 轉換為 ClawBot 需要的格式 (含基準線數據)
+        # 轉換為 OpenClaw 需要的格式 (含基準線數據)
        host_statuses = {}
        for host in snapshot.hosts:
            # 組裝 metrics 與 baseline
@@ -194,7 +194,7 @@ async def analyze_and_propose(

    # Step 3: 處理決策
    if decision is None:
-        return ClawBotAnalysisResponse(
+        return OpenClawAnalysisResponse(
            success=False,
            message="AI 分析完成，但無法解析決策輸出。請檢查 LLM 回應格式。",
            ai_provider=provider,
@@ -207,7 +207,7 @@ async def analyze_and_propose(
            "ai_no_action_needed",
            reasoning=decision.reasoning,
        )
-        return ClawBotAnalysisResponse(
+        return OpenClawAnalysisResponse(
            success=True,
            message="AI 判斷目前無需採取行動。",
            decision=decision,
@@ -229,9 +229,9 @@ async def analyze_and_propose(
            risk_level=decision.risk_level.value,
        )

-        return ClawBotAnalysisResponse(
+        return OpenClawAnalysisResponse(
            success=True,
-            message=f"ClawBot 已建立待簽核卡片：{decision.suggested_action.value} {decision.target_resource}",
+            message=f"OpenClaw 已建立待簽核卡片：{decision.suggested_action.value} {decision.target_resource}",
            decision=decision,
            approval_created=True,
            approval_id=str(approval.id),
@@ -243,7 +243,7 @@ async def analyze_and_propose(
            "ai_approval_create_failed",
            error=str(e),
        )
-        return ClawBotAnalysisResponse(
+        return OpenClawAnalysisResponse(
            success=False,
            message=f"AI 分析成功，但建立授權請求失敗：{str(e)}",
            decision=decision,
@@ -255,7 +255,7 @@ async def analyze_and_propose(
@router.get(
    "/status",
    summary="AI 服務狀態",
-    description="檢查 ClawBot AI 服務狀態與可用的 AI 提供者。",
+    description="檢查 OpenClaw AI 服務狀態與可用的 AI 提供者。",
 )
 async def get_ai_status() -> dict:
    """檢查 AI 服務狀態"""
--- a/apps/api/src/api/v1/approvals.py
+++ b/apps/api/src/api/v1/approvals.py
@@ -12,7 +12,7 @@ Endpoints:
 - POST /api/v1/approvals/{id}/reject - 拒絕請求

 信任鏈流程:
-1. ClawBot 發起 CRITICAL 操作 → 建立 ApprovalRequest (PENDING) → 寫入 DB
+1. OpenClaw 發起 CRITICAL 操作 → 建立 ApprovalRequest (PENDING) → 寫入 DB
 2. 第一位簽核者簽核 → 仍為 PENDING (1/2) → 更新 DB
 3. 第二位簽核者簽核 → 轉為 APPROVED → 更新 DB
 4. BackgroundTasks 觸發 K8s 執行 → EXECUTION_SUCCESS/FAILED → 更新 DB
@@ -623,7 +623,7 @@ async def sign_approval(
            event_type="exec",
            status="warning",
            title=f"K8s Executor 已排程執行: {approval.action[:40]}...",
-            actor="ClawBot",
+            actor="OpenClaw",
            actor_role="executor",
            approval_id=str(approval_id),
        )
--- a/apps/api/src/core/config.py
+++ b/apps/api/src/core/config.py
@@ -48,7 +48,7 @@ class Settings(BaseSettings):
    # ==========================================================================
    MOCK_MODE: bool = Field(
        default=False,
-        description="Enable mock mode for external services (Redis, Ollama, ClawBot, PostgreSQL, SigNoz)",
+        description="Enable mock mode for external services (Redis, Ollama, OpenClaw, PostgreSQL, SigNoz)",
    )

    # ==========================================================================
@@ -106,7 +106,7 @@ class Settings(BaseSettings):
    # Deprecated: use OPENCLAW_URL instead
    CLAWBOT_URL: str = Field(
        default="http://192.168.0.188:8088",  # 🔧 修正: OpenClaw 實際 port 是 8088
-        description="[Deprecated] Legacy ClawBot URL - use OPENCLAW_URL",
+        description="[Deprecated] Legacy OpenClaw URL - use OPENCLAW_URL",
    )
    KALI_SCANNER_URL: str = Field(
        default="http://192.168.0.112:8080",
@@ -201,7 +201,7 @@ class Settings(BaseSettings):
    HEALTH_CHECK_TIMEOUT: float = Field(default=5.0, description="Health check timeout")

    # ==========================================================================
-    # Phase 5: OpenClaw AI Engine (正名自 ClawBot)
+    # Phase 5: OpenClaw AI Engine (正名自 OpenClaw)
    # Synced from models.json - Ollama First Strategy
    # ==========================================================================
    OPENCLAW_URL: str = Field(
--- a/apps/api/src/core/telemetry.py
+++ b/apps/api/src/core/telemetry.py
@@ -146,7 +146,7 @@ def setup_telemetry(app) -> bool:
            excluded_urls="health,healthz,ready,metrics",  # 排除健康檢查
        )

-        # 自動追蹤 HTTPX 外部呼叫 (Ollama, ClawBot, etc.)
+        # 自動追蹤 HTTPX 外部呼叫 (Ollama, OpenClaw, etc.)
        HTTPXClientInstrumentor().instrument(tracer_provider=_tracer_provider)

        # 自動追蹤日誌 (注入 trace_id, span_id)
--- a/apps/api/src/db/models.py
+++ b/apps/api/src/db/models.py
@@ -157,7 +157,7 @@ class TimelineEvent(Base):

    事件類型:
    - system: 系統告警接收
-    - agent: ClawBot AI 分析
+    - agent: OpenClaw AI 分析
    - security: 權限阻擋
    - human: 人類授權
    - exec: 執行完成
--- a/apps/api/src/models/ai.py
+++ b/apps/api/src/models/ai.py
@@ -1,7 +1,7 @@
 """
 AI Decision Models - Phase 2 Structured Output
 ===============================================
-CAI-101: ClawBot AI 結構化輸出模型
+CAI-101: OpenClaw AI 結構化輸出模型

 防禦性工程鐵律:
 - 絕對禁止 LLM 輸出無法解析的自由文本
@@ -189,7 +189,7 @@ class OpenClawDecision(BaseModel):
        return v


-class ClawBotAnalysisRequest(BaseModel):
+class OpenClawAnalysisRequest(BaseModel):
    """分析請求"""
    force_refresh: bool = Field(
        default=False,
@@ -197,7 +197,7 @@ class ClawBotAnalysisRequest(BaseModel):
    )


-class ClawBotAnalysisResponse(BaseModel):
+class OpenClawAnalysisResponse(BaseModel):
    """分析回應"""
    success: bool
    message: str
--- a/apps/api/src/plugins/finops/cost_analyzer.py
+++ b/apps/api/src/plugins/finops/cost_analyzer.py
@@ -9,7 +9,7 @@ Phase 3.3: 商業變現能力 - Day-1 ROI

 輸出格式:
 - total_wasted_usd: 每月浪費金額
- recommended_actions: ClawBot 可執行的建議清單
+- recommended_actions: OpenClaw 可執行的建議清單
 """

 import logging
@@ -78,7 +78,7 @@ class SavingsType(str, Enum):

@dataclass
 class RecommendedAction:
-    """建議的優化動作 (ClawBot 可執行)"""
+    """建議的優化動作 (OpenClaw 可執行)"""
    action_id: str
    action_type: Literal["delete", "scale_down", "resize", "migrate"]
    resource_type: ResourceType
@@ -87,7 +87,7 @@ class RecommendedAction:
    description: str
    estimated_savings_usd: float
    risk_level: Literal["low", "medium", "high", "critical"]
-    command_hint: str  # 給 ClawBot 的執行提示
+    command_hint: str  # 給 OpenClaw 的執行提示
    savings_type: SavingsType = SavingsType.REALIZABLE  # 節省類型

    def to_dict(self) -> dict:
@@ -107,7 +107,7 @@ class RecommendedAction:

@dataclass
 class CostReport:
-    """成本報告 (ClawBot 整合用)"""
+    """成本報告 (OpenClaw 整合用)"""
    scan_id: str
    scanned_at: datetime
    cluster_name: str
@@ -126,13 +126,13 @@ class CostReport:
    waste_by_namespace: dict[str, float]

    def to_dict(self) -> dict:
-        """輸出 ClawBot 可讀取的 JSON 格式"""
+        """輸出 OpenClaw 可讀取的 JSON 格式"""
        return {
            "scanId": self.scan_id,
            "scannedAt": self.scanned_at.isoformat(),
            "clusterName": self.cluster_name,

-            # ClawBot 核心關注
+            # OpenClaw 核心關注
            "totalWastedUsd": round(self.total_wasted_usd, 2),
            "totalResourcesScanned": self.total_resources_scanned,
            "wastedResourcesCount": self.wasted_resources_count,
@@ -217,7 +217,7 @@ class IdleResourceScanner:
    閒置資源掃描器

    偵測並量化 K8s 叢集中的浪費資源，
-    轉換為美金金額，供 ClawBot 決策
+    轉換為美金金額，供 OpenClaw 決策
    """

    def __init__(self, pricing: PricingConfig | None = None):
@@ -490,7 +490,7 @@ class IdleResourceScanner:
        wasted: list[WastedResource],
    ) -> list[RecommendedAction]:
        """
-        產生優化建議 (ClawBot 可執行)
+        產生優化建議 (OpenClaw 可執行)
        """
        actions = []
        action_counter = 0
@@ -585,7 +585,7 @@ class IdleResourceScanner:
        ╚════════════════════════════════════════════════════════════════╝

        Returns:
-            ClawBot 可直接使用的 JSON 格式
+            OpenClaw 可直接使用的 JSON 格式
        """
        realizable = sum(
            a.estimated_savings_usd
--- a/apps/api/src/routes/agent.py
+++ b/apps/api/src/routes/agent.py
@@ -1,5 +1,5 @@
 """
-Agent (ClawBot) Endpoints
+Agent (OpenClaw) Endpoints
 ADR-005: BFF 架構 - 所有 AI 調用經過 BFF
 Phase 1.2: 真實 Ollama 串接
 """
@@ -54,10 +54,10 @@ class AgentStatus(BaseModel):

@router.post("/chat", response_model=ChatResponse)
 async def chat_with_agent(request: ChatRequest) -> ChatResponse:
-    """與 ClawBot 對話"""
+    """與 OpenClaw 對話"""
    conversation_id = request.conversation_id or uuid4()

-    # TODO: 實際調用 ClawBot
+    # TODO: 實際調用 OpenClaw
    return ChatResponse(
        message=f"收到訊息: {request.message}",
        conversation_id=conversation_id,
@@ -67,11 +67,11 @@ async def chat_with_agent(request: ChatRequest) -> ChatResponse:

@router.post("/chat/stream")
 async def chat_with_agent_stream(request: ChatRequest) -> StreamingResponse:
-    """與 ClawBot 對話 (SSE 串流)"""
+    """與 OpenClaw 對話 (SSE 串流)"""

    async def generate():
        # TODO: 實際串流
-        yield "data: Hello from ClawBot\n\n"
+        yield "data: Hello from OpenClaw\n\n"
        yield "data: [DONE]\n\n"

    return StreamingResponse(
@@ -82,7 +82,7 @@ async def chat_with_agent_stream(request: ChatRequest) -> StreamingResponse:

@router.get("/status", response_model=AgentStatus)
 async def get_agent_status() -> AgentStatus:
-    """ClawBot 狀態"""
+    """OpenClaw 狀態"""
    return AgentStatus(
        status="idle",
        active_conversations=0,
@@ -100,7 +100,7 @@ async def get_agent_thinking(
    model: str = Query(default=OLLAMA_MODEL, description="Ollama 模型名稱"),
 ) -> StreamingResponse:
    """
-    ClawBot 思考軌跡 (SSE 串流)
+    OpenClaw 思考軌跡 (SSE 串流)
    Phase 1.2: 真實串接 Ollama at 192.168.0.188:11434
    """

--- a/apps/api/src/services/clawbot.py
+++ b/apps/api/src/services/clawbot.py
@@ -1,704 +0,0 @@
-"""
-ClawBot AI Decision Engine - True LLM Integration
-===================================================
-CAI-101: AI 決策大腦 (Phase 2: 實彈裝填)
-
-Features:
- 真實 LLM SDK 整合 (Ollama → Gemini → Claude)
- AIOps Agent 專業人格 (K8s 維運 + SRE RCA 專精)
- 強制結構化 JSON 輸出 (符合 API 契約)
- 動態告警上下文注入
- 優雅降級 Mock Fallback
-
-防禦性工程鐵律:
- Zero Trust: 預設不信任 LLM 輸出，必須通過 Pydantic 驗證
- Edge Case: 網路失敗、解析失敗、超時處理
-"""
-
-import json
-import random
-import re
-import time
-from typing import Any
-
-import httpx
-import structlog
-
-from src.core.config import settings
-from src.models.ai import (
-    ClawBotDecision,
-)
-
-logger = structlog.get_logger(__name__)
-
-
-# =============================================================================
-# AIOps Agent System Prompt (專業人格)
-# =============================================================================
-
-CLAWBOT_SYSTEM_PROMPT = """# ClawBot v5.0 - AWOOOI AIOps Agent
-
-You are ClawBot, a senior Site Reliability Engineer (SRE) AI agent specialized in:
- Kubernetes cluster operations and troubleshooting
- Root Cause Analysis (RCA) for production incidents
- Blast radius assessment for proposed remediation actions
- Risk-aware automated remediation recommendations
-
-## Your Responsibilities
-1. Analyze incoming alerts and system metrics
-2. Identify the root cause of incidents
-3. Assess the blast radius of potential fixes
-4. Recommend the safest remediation action with specific kubectl commands
-5. Provide clear, human-readable explanations in Traditional Chinese (繁體中文)
-
-## Output Rules
- You MUST respond with ONLY valid JSON, no markdown, no explanation outside JSON
- Every field in the schema is REQUIRED
- risk_level MUST be one of: "low", "medium", "critical"
- suggested_action MUST be one of: "RESTART_DEPLOYMENT", "DELETE_POD", "SCALE_DEPLOYMENT", "NO_ACTION"
- confidence MUST be between 0.0 and 1.0
-
-## JSON Schema (REQUIRED)
-```json
-{
-  "action_title": "string - 操作標題 (繁體中文, 簡潔)",
-  "description": "string - 根本原因分析說明 (繁體中文, 2-3 句話)",
-  "suggested_action": "RESTART_DEPLOYMENT|DELETE_POD|SCALE_DEPLOYMENT|NO_ACTION",
-  "kubectl_command": "string - 具體的 kubectl 指令",
-  "target_resource": "string - 目標資源名稱",
-  "namespace": "string - K8s namespace",
-  "risk_level": "low|medium|critical",
-  "blast_radius": {
-    "affected_pods": "number - 受影響的 Pod 數量",
-    "estimated_downtime": "string - 預估停機時間",
-    "related_services": ["array of strings - 相關服務"],
-    "data_impact": "NONE|READ_ONLY|WRITE|DESTRUCTIVE"
-  },
-  "reasoning": "string - 決策理由 (繁體中文)",
-  "deviation_analysis": "string - 基準線偏差分析",
-  "confidence": "number - 0.0 to 1.0",
-  "affected_services": ["array of strings"]
-}
-```
-
-## Example Response
-```json
-{
-  "action_title": "重新啟動 Payment 服務 Pod",
-  "description": "Payment 服務發生 OOMKilled，根本原因為記憶體洩漏導致 Java Heap 耗盡。建議立即重啟 Pod 以恢復服務，同時排程開發團隊檢查記憶體洩漏。",
-  "suggested_action": "DELETE_POD",
-  "kubectl_command": "kubectl delete pod payment-service-7d4b8c9f5-xk2m3 -n payment",
-  "target_resource": "payment-service-7d4b8c9f5-xk2m3",
-  "namespace": "payment",
-  "risk_level": "critical",
-  "blast_radius": {
-    "affected_pods": 1,
-    "estimated_downtime": "~30s",
-    "related_services": ["api-gateway", "checkout-service"],
-    "data_impact": "NONE"
-  },
-  "reasoning": "Pod 已進入 OOMKilled 狀態，ReplicaSet 會自動重建新 Pod，預計 30 秒內恢復",
-  "deviation_analysis": "Memory 使用率 98%，超出基準線 60% 達 +6.3σ",
-  "confidence": 0.92,
-  "affected_services": ["payment-service", "checkout-service"]
-}
-```
-
-Now analyze the following alert:
-"""
-
-
-# =============================================================================
-# LLM Analysis Result - Using Pydantic for Schema Enforcement
-# =============================================================================
-
-# We use ClawBotDecision from models/ai.py for Pydantic validation
-# This alias is for backwards compatibility
-LLMAnalysisResult = ClawBotDecision
-
-
-# =============================================================================
-# ClawBot Service
-# =============================================================================
-
-class ClawBotService:
-    """
-    ClawBot AI 決策服務 - True LLM Integration
-
-    實作 AI_FALLBACK_ORDER 備援機制:
-    Ollama → Gemini → Claude → Mock
-    """
-
-    def __init__(self):
-        self._http_client: httpx.AsyncClient | None = None
-
-    async def _get_client(self) -> httpx.AsyncClient:
-        """取得 HTTP 客戶端"""
-        if self._http_client is None or self._http_client.is_closed:
-            self._http_client = httpx.AsyncClient(
-                timeout=httpx.Timeout(120.0, connect=10.0),
-            )
-        return self._http_client
-
-    async def close(self) -> None:
-        """關閉連線"""
-        if self._http_client:
-            await self._http_client.aclose()
-            self._http_client = None
-
-    # =========================================================================
-    # AI Provider Implementations - Enhanced with Structured Output
-    # =========================================================================
-
-    async def _call_ollama(self, prompt: str) -> tuple[str, bool]:
-        """
-        呼叫本機 Ollama (支援 JSON Mode)
-        """
-        try:
-            client = await self._get_client()
-
-            logger.info(
-                "ollama_request_start",
-                url=f"{settings.OLLAMA_URL}/api/generate",
-                prompt_length=len(prompt),
-            )
-
-            response = await client.post(
-                f"{settings.OLLAMA_URL}/api/generate",
-                json={
-                    "model": "llama3.2:3b",  # 使用更大的模型提高品質
-                    "prompt": prompt,
-                    "stream": False,
-                    "format": "json",  # 強制 JSON 輸出
-                    "options": {
-                        "num_predict": 1024,  # 增加輸出長度
-                        "temperature": 0.1,   # 低溫度確保穩定輸出
-                        "top_p": 0.9,
-                    },
-                },
-                timeout=httpx.Timeout(90.0, connect=10.0),
-            )
-
-            logger.info(
-                "ollama_response_received",
-                status_code=response.status_code,
-            )
-
-            response.raise_for_status()
-            data = response.json()
-            result = data.get("response", "")
-
-            logger.info(
-                "ollama_response_parsed",
-                response_length=len(result),
-            )
-
-            return result, True
-
-        except httpx.TimeoutException as e:
-            logger.warning("ollama_timeout", error=str(e))
-            return f"Timeout: {e}", False
-
-        except Exception as e:
-            logger.warning(
-                "ollama_call_failed",
-                error=str(e),
-                error_type=type(e).__name__,
-            )
-            return str(e), False
-
-    async def _call_gemini(self, prompt: str) -> tuple[str, bool]:
-        """
-        呼叫 Google Gemini (支援 JSON Mode)
-        """
-        if not settings.GEMINI_API_KEY:
-            return "GEMINI_API_KEY not configured", False
-
-        try:
-            client = await self._get_client()
-
-            # Gemini 1.5 Flash 支援 JSON Mode
-            response = await client.post(
-                f"https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent?key={settings.GEMINI_API_KEY}",
-                json={
-                    "contents": [{"parts": [{"text": prompt}]}],
-                    "generationConfig": {
-                        "temperature": 0.1,
-                        "maxOutputTokens": 2048,
-                        "responseMimeType": "application/json",  # 強制 JSON 輸出
-                    },
-                },
-                timeout=30.0,
-            )
-            response.raise_for_status()
-            data = response.json()
-            text = data["candidates"][0]["content"]["parts"][0]["text"]
-
-            logger.info("gemini_response_received", response_length=len(text))
-            return text, True
-
-        except Exception as e:
-            logger.warning("gemini_call_failed", error=str(e))
-            return str(e), False
-
-    async def _call_claude(self, prompt: str) -> tuple[str, bool]:
-        """
-        呼叫 Anthropic Claude (使用 Tool Use 強制 JSON)
-        """
-        if not settings.CLAUDE_API_KEY:
-            return "CLAUDE_API_KEY not configured", False
-
-        try:
-            client = await self._get_client()
-
-            # Claude 使用 Tool Use 強制結構化輸出
-            response = await client.post(
-                "https://api.anthropic.com/v1/messages",
-                headers={
-                    "x-api-key": settings.CLAUDE_API_KEY,
-                    "anthropic-version": "2023-06-01",
-                    "content-type": "application/json",
-                },
-                json={
-                    "model": "claude-3-haiku-20240307",
-                    "max_tokens": 2048,
-                    "messages": [{"role": "user", "content": prompt}],
-                    "tools": [{
-                        "name": "submit_analysis",
-                        "description": "Submit the RCA analysis result in structured format",
-                        "input_schema": {
-                            "type": "object",
-                            "properties": {
-                                "action_title": {"type": "string"},
-                                "description": {"type": "string"},
-                                "suggested_action": {"type": "string", "enum": ["RESTART_DEPLOYMENT", "DELETE_POD", "SCALE_DEPLOYMENT", "NO_ACTION"]},
-                                "kubectl_command": {"type": "string"},
-                                "target_resource": {"type": "string"},
-                                "namespace": {"type": "string"},
-                                "risk_level": {"type": "string", "enum": ["low", "medium", "critical"]},
-                                "blast_radius": {
-                                    "type": "object",
-                                    "properties": {
-                                        "affected_pods": {"type": "integer"},
-                                        "estimated_downtime": {"type": "string"},
-                                        "related_services": {"type": "array", "items": {"type": "string"}},
-                                        "data_impact": {"type": "string", "enum": ["NONE", "READ_ONLY", "WRITE", "DESTRUCTIVE"]}
-                                    },
-                                    "required": ["affected_pods", "estimated_downtime", "related_services", "data_impact"]
-                                },
-                                "reasoning": {"type": "string"},
-                                "deviation_analysis": {"type": "string"},
-                                "confidence": {"type": "number"},
-                                "affected_services": {"type": "array", "items": {"type": "string"}}
-                            },
-                            "required": ["action_title", "description", "suggested_action", "kubectl_command", "target_resource", "namespace", "risk_level", "blast_radius", "reasoning", "confidence"]
-                        }
-                    }],
-                    "tool_choice": {"type": "tool", "name": "submit_analysis"},
-                },
-                timeout=30.0,
-            )
-            response.raise_for_status()
-            data = response.json()
-
-            # 從 Tool Use 回應中提取 JSON
-            for block in data.get("content", []):
-                if block.get("type") == "tool_use" and block.get("name") == "submit_analysis":
-                    tool_input = block.get("input", {})
-                    logger.info("claude_tool_use_response", input_keys=list(tool_input.keys()))
-                    return json.dumps(tool_input), True
-
-            # Fallback: 嘗試從 text 內容提取
-            for block in data.get("content", []):
-                if block.get("type") == "text":
-                    return block.get("text", ""), True
-
-            return "No valid response from Claude", False
-
-        except Exception as e:
-            logger.warning("claude_call_failed", error=str(e))
-            return str(e), False
-
-    # =========================================================================
-    # Mock LLM - Intelligent Fallback
-    # =========================================================================
-
-    def _generate_mock_response(self, alert_context: dict) -> str:
-        """
-        Mock LLM 回應生成器 - 智能降級
-
-        根據告警類型動態產生合理的 RCA 分析結果
-        """
-        time.sleep(random.uniform(0.3, 0.8))  # 模擬思考延遲
-
-        alert_type = alert_context.get("alert_type", "custom")
-        severity = alert_context.get("severity", "warning")
-        target = alert_context.get("target_resource", "unknown-service")
-        namespace = alert_context.get("namespace", "default")
-        message = alert_context.get("message", "")
-        metrics = alert_context.get("metrics", {})
-
-        # 根據告警類型生成專業 RCA
-        if "oom" in message.lower() or "memory" in alert_type.lower():
-            mock_response = {
-                "action_title": f"重新啟動 {target} Pod (OOMKilled)",
-                "description": f"[MOCK RCA] {target} 發生 OOMKilled，根本原因為記憶體洩漏或配置不足。建議立即重啟 Pod 恢復服務，並安排開發團隊檢查 Heap 配置。",
-                "suggested_action": "DELETE_POD",
-                "kubectl_command": f"kubectl delete pod {target} -n {namespace}",
-                "target_resource": target,
-                "namespace": namespace,
-                "risk_level": "critical" if severity == "critical" else "medium",
-                "blast_radius": {
-                    "affected_pods": 1,
-                    "estimated_downtime": "~30s",
-                    "related_services": ["api-gateway", "downstream-service"],
-                    "data_impact": "NONE"
-                },
-                "reasoning": "[MOCK] Pod OOMKilled 後 ReplicaSet 將自動重建，服務預計 30 秒內恢復",
-                "deviation_analysis": f"[MOCK] Memory 使用率 {metrics.get('memory_percent', 95)}%，超出基準線達 +5.2σ",
-                "confidence": 0.88,
-                "affected_services": [target, "api-gateway"]
-            }
-        elif "db" in alert_type.lower() or "connection" in message.lower() or "pool" in message.lower():
-            mock_response = {
-                "action_title": f"重啟 {target} 資料庫連線池",
-                "description": f"[MOCK RCA] {target} 資料庫連線池已滿載，根本原因為連線未正確釋放或流量突增。建議重啟服務以重置連線池。",
-                "suggested_action": "RESTART_DEPLOYMENT",
-                "kubectl_command": f"kubectl rollout restart deployment/{target} -n {namespace}",
-                "target_resource": target,
-                "namespace": namespace,
-                "risk_level": "critical",
-                "blast_radius": {
-                    "affected_pods": 3,
-                    "estimated_downtime": "~2 min",
-                    "related_services": ["auth-service", "user-service", "order-service"],
-                    "data_impact": "WRITE"
-                },
-                "reasoning": "[MOCK] 資料庫連線池滿載會導致所有依賴服務無法存取資料，需立即重啟",
-                "deviation_analysis": f"[MOCK] Active connections: {metrics.get('active_connections', 100)}/{metrics.get('max_connections', 100)}",
-                "confidence": 0.85,
-                "affected_services": [target, "auth-service", "api-gateway"]
-            }
-        elif "crash" in alert_type.lower() or "pod" in alert_type.lower():
-            mock_response = {
-                "action_title": f"刪除異常 Pod {target}",
-                "description": f"[MOCK RCA] {target} 發生 CrashLoopBackOff，根本原因為應用程式啟動失敗。建議刪除 Pod 讓 ReplicaSet 重建。",
-                "suggested_action": "DELETE_POD",
-                "kubectl_command": f"kubectl delete pod {target} -n {namespace}",
-                "target_resource": target,
-                "namespace": namespace,
-                "risk_level": "medium" if severity != "critical" else "critical",
-                "blast_radius": {
-                    "affected_pods": 1,
-                    "estimated_downtime": "~30s",
-                    "related_services": ["ingress-controller"],
-                    "data_impact": "NONE"
-                },
-                "reasoning": "[MOCK] CrashLoopBackOff 通常為暫時性啟動問題，重建 Pod 可解決",
-                "deviation_analysis": f"[MOCK] Restart count: {metrics.get('restart_count', 5)}",
-                "confidence": 0.82,
-                "affected_services": [target]
-            }
-        elif "cpu" in alert_type.lower() or "high_cpu" in alert_type:
-            mock_response = {
-                "action_title": f"擴展 {target} 副本數",
-                "description": f"[MOCK RCA] {target} CPU 使用率過高，根本原因為流量突增或運算密集任務。建議水平擴展增加副本數。",
-                "suggested_action": "SCALE_DEPLOYMENT",
-                "kubectl_command": f"kubectl scale deployment/{target} --replicas=+2 -n {namespace}",
-                "target_resource": target,
-                "namespace": namespace,
-                "risk_level": "medium",
-                "blast_radius": {
-                    "affected_pods": 0,
-                    "estimated_downtime": "0",
-                    "related_services": [],
-                    "data_impact": "NONE"
-                },
-                "reasoning": "[MOCK] 水平擴展可分散負載，無停機風險",
-                "deviation_analysis": f"[MOCK] CPU 使用率 {metrics.get('cpu_percent', 95)}%，超出基準線達 +4.5σ",
-                "confidence": 0.90,
-                "affected_services": [target]
-            }
-        else:
-            # 通用異常處理
-            mock_response = {
-                "action_title": f"重新啟動 {target} 服務",
-                "description": f"[MOCK RCA] {target} 發生異常: {message}。建議重啟服務以恢復正常運作。",
-                "suggested_action": "RESTART_DEPLOYMENT",
-                "kubectl_command": f"kubectl rollout restart deployment/{target} -n {namespace}",
-                "target_resource": target,
-                "namespace": namespace,
-                "risk_level": "critical" if severity == "critical" else "medium",
-                "blast_radius": {
-                    "affected_pods": 3,
-                    "estimated_downtime": "~1 min",
-                    "related_services": ["dependent-services"],
-                    "data_impact": "NONE"
-                },
-                "reasoning": f"[MOCK] 根據告警 {alert_type} 判斷需要重啟服務",
-                "deviation_analysis": "[MOCK] 監控指標顯示異常",
-                "confidence": 0.75,
-                "affected_services": [target]
-            }
-
-        logger.info(
-            "mock_llm_response_generated",
-            action_title=mock_response["action_title"],
-            risk_level=mock_response["risk_level"],
-            is_mock=True,
-        )
-
-        return json.dumps(mock_response)
-
-    # =========================================================================
-    # Fallback Chain
-    # =========================================================================
-
-    async def _call_with_fallback(self, prompt: str, alert_context: dict | None = None) -> tuple[str, str, bool]:
-        """
-        依 AI_FALLBACK_ORDER 順序呼叫 AI
-
-        若 MOCK_MODE=True，直接回傳模擬結果。
-        若所有 Provider 失敗，fallback 到 Mock。
-        """
-        # Mock Mode: 開發測試用
-        if settings.MOCK_MODE:
-            logger.info("mock_mode_enabled", using="mock_llm")
-            return self._generate_mock_response(alert_context or {}), "mock", True
-
-        for provider in settings.AI_FALLBACK_ORDER:
-            logger.info("ai_provider_attempt", provider=provider)
-
-            if provider == "ollama":
-                response, success = await self._call_ollama(prompt)
-            elif provider == "gemini":
-                response, success = await self._call_gemini(prompt)
-            elif provider == "claude":
-                response, success = await self._call_claude(prompt)
-            else:
-                logger.warning("unknown_ai_provider", provider=provider)
-                continue
-
-            if success:
-                logger.info("ai_provider_success", provider=provider)
-                return response, provider, True
-
-            logger.warning("ai_provider_failed_fallback", provider=provider)
-
-        # 所有 Provider 失敗時，fallback 到 Mock (優雅降級)
-        logger.warning("all_providers_failed_using_mock", fallback="mock_llm")
-        return self._generate_mock_response(alert_context or {}), "mock_fallback", True
-
-    # =========================================================================
-    # Response Parsing (防禦性解析)
-    # =========================================================================
-
-    def _extract_json_from_response(self, text: str) -> str | None:
-        """從 LLM 回應中提取 JSON"""
-        # 嘗試直接解析
-        try:
-            json.loads(text)
-            return text
-        except json.JSONDecodeError:
-            pass
-
-        # 嘗試從 markdown code block 提取
-        patterns = [
-            r"```json\s*([\s\S]*?)\s*```",
-            r"```\s*([\s\S]*?)\s*```",
-            r"\{[\s\S]*\}",
-        ]
-
-        for pattern in patterns:
-            match = re.search(pattern, text)
-            if match:
-                candidate = match.group(1) if "```" in pattern else match.group(0)
-                try:
-                    json.loads(candidate)
-                    return candidate
-                except json.JSONDecodeError:
-                    continue
-
-        return None
-
-    def _parse_analysis_result(self, raw_response: str) -> ClawBotDecision | None:
-        """
-        解析 LLM 分析結果 - 使用 Pydantic Schema Enforcement
-
-        關鍵：blast_radius 為 REQUIRED，使用 AIBlastRadius Pydantic 模型驗證
-        """
-        json_str = self._extract_json_from_response(raw_response)
-        if not json_str:
-            logger.error("json_extraction_failed", raw_response=raw_response[:200])
-            return None
-
-        try:
-            data = json.loads(json_str)
-
-            # Step 1: 確保 blast_radius 存在且為正確格式
-            if "blast_radius" not in data or not isinstance(data["blast_radius"], dict):
-                data["blast_radius"] = {
-                    "affected_pods": 1,
-                    "estimated_downtime": "~30s",
-                    "related_services": data.get("affected_services", []),
-                    "data_impact": "NONE"
-                }
-            else:
-                # 確保 blast_radius 內的必填欄位存在
-                br = data["blast_radius"]
-                if "affected_pods" not in br:
-                    br["affected_pods"] = 1
-                if "estimated_downtime" not in br:
-                    br["estimated_downtime"] = "~30s"
-                if "related_services" not in br:
-                    br["related_services"] = data.get("affected_services", [])
-                if "data_impact" not in br:
-                    br["data_impact"] = "NONE"
-
-            # Step 2: 填補其他可選欄位
-            if "action_title" not in data:
-                data["action_title"] = data.get("action", "未知操作")
-            if "target_resource" not in data:
-                data["target_resource"] = "unknown"
-            if "suggested_action" not in data:
-                data["suggested_action"] = "NO_ACTION"
-
-            # Step 3: 使用 Pydantic 驗證 (會自動正規化 risk_level, data_impact 等)
-            decision = ClawBotDecision(**data)
-
-            logger.info(
-                "pydantic_validation_success",
-                action_title=decision.action_title,
-                risk_level=decision.risk_level.value,
-                blast_radius_pods=decision.blast_radius.affected_pods,
-            )
-
-            return decision
-
-        except Exception as e:
-            logger.error(
-                "pydantic_validation_failed",
-                error=str(e),
-                json_str=json_str[:300],
-            )
-            return None
-
-    # =========================================================================
-    # Main Analysis Methods
-    # =========================================================================
-
-    async def analyze_alert(self, alert_context: dict) -> tuple[LLMAnalysisResult | None, str, str]:
-        """
-        分析告警並產生 RCA 結果
-
-        Args:
-            alert_context: 告警上下文 (alert_type, severity, target_resource, etc.)
-
-        Returns:
-            (analysis_result, ai_provider, raw_response)
-        """
-        # 格式化告警為 Prompt
-        alert_json = json.dumps(alert_context, ensure_ascii=False, indent=2)
-        full_prompt = CLAWBOT_SYSTEM_PROMPT + "\n" + alert_json
-
-        logger.info(
-            "clawbot_alert_analysis_start",
-            alert_type=alert_context.get("alert_type"),
-            target=alert_context.get("target_resource"),
-        )
-
-        # 呼叫 LLM
-        raw_response, provider, success = await self._call_with_fallback(full_prompt, alert_context)
-
-        if not success:
-            logger.error("clawbot_all_providers_failed")
-            return None, provider, raw_response
-
-        logger.info(
-            "clawbot_llm_response_received",
-            provider=provider,
-            response_length=len(raw_response),
-        )
-
-        # 解析結果
-        result = self._parse_analysis_result(raw_response)
-
-        if result:
-            logger.info(
-                "clawbot_analysis_complete",
-                action_title=result.action_title,
-                risk_level=result.risk_level,
-                confidence=result.confidence,
-                provider=provider,
-            )
-        else:
-            logger.warning(
-                "clawbot_analysis_parse_failed",
-                raw_response=raw_response[:300],
-            )
-
-        return result, provider, raw_response
-
-    # Legacy method for backwards compatibility
-    def _parse_decision(self, raw_response: str) -> ClawBotDecision | None:
-        """解析 LLM 回應為 ClawBotDecision (向後相容)"""
-        json_str = self._extract_json_from_response(raw_response)
-        if not json_str:
-            return None
-
-        try:
-            data = json.loads(json_str)
-            risk_mapping = {"high": "critical", "severe": "critical", "warning": "medium"}
-            if "risk_level" in data:
-                risk = str(data["risk_level"]).lower()
-                data["risk_level"] = risk_mapping.get(risk, risk)
-
-            return ClawBotDecision(**data)
-        except Exception as e:
-            logger.error("decision_parse_failed", error=str(e))
-            return None
-
-    def _format_status_for_llm(self, host_statuses: dict[str, Any]) -> str:
-        """將主機狀態格式化為精簡文本"""
-        lines = []
-        for host_key, host_data in host_statuses.items():
-            if isinstance(host_data, dict):
-                status = host_data.get("status", "unknown")
-                if status != "healthy":
-                    lines.append(f"{host_key}:{status}")
-        return "\n".join(lines[:4]) if lines else "OK"
-
-    async def analyze(self, host_statuses: dict[str, Any]) -> tuple[ClawBotDecision | None, str, str]:
-        """分析主機狀態 (Legacy 方法)"""
-        status_text = self._format_status_for_llm(host_statuses)
-        full_prompt = CLAWBOT_SYSTEM_PROMPT + "\n" + status_text
-
-        raw_response, provider, success = await self._call_with_fallback(full_prompt, {})
-        if not success:
-            return None, provider, raw_response
-
-        decision = self._parse_decision(raw_response)
-        return decision, provider, raw_response
-
-
-# =============================================================================
-# Singleton
-# =============================================================================
-
-_clawbot: ClawBotService | None = None
-
-
-def get_clawbot() -> ClawBotService:
-    """取得全域 ClawBot 實例"""
-    global _clawbot
-    if _clawbot is None:
-        _clawbot = ClawBotService()
-    return _clawbot
-
-
-async def close_clawbot() -> None:
-    """關閉 ClawBot 連線"""
-    global _clawbot
-    if _clawbot:
-        await _clawbot.close()
-        _clawbot = None
--- a/apps/api/src/services/graph_rag.py
+++ b/apps/api/src/services/graph_rag.py
@@ -392,7 +392,7 @@ class TopologyGraph:
        """
        完整分析: Blast Radius + Root Cause

-        ClawBot 主要呼叫這個方法，一次取得:
+        OpenClaw 主要呼叫這個方法，一次取得:
        1. 向上追溯: 誰會受影響
        2. 向下追溯: 誰是根本原因

--- a/apps/api/src/services/host_aggregator.py
+++ b/apps/api/src/services/host_aggregator.py
@@ -7,7 +7,7 @@ Hosts:
 - 192.168.0.110: DevOps 金庫 (Harbor, GH Runner)
 - 192.168.0.112: Kali Security (Scanner API)
 - 192.168.0.120: K3s Master (awoooi-prod namespace)
- 192.168.0.188: AI+Web 中心 (Nginx, PostgreSQL, Redis, Ollama, ClawBot, SigNoz)
+- 192.168.0.188: AI+Web 中心 (Nginx, PostgreSQL, Redis, Ollama, OpenClaw, SigNoz)

 Features:
 - asyncio.gather for parallel fetching
@@ -311,7 +311,7 @@ HOST_CONFIGS = {
            ("PostgreSQL", 5432, "tcp", None),
            ("Redis", 6380, "tcp", None),
            ("Ollama", 11434, "http", "/api/tags"),
-            ("ClawBot", 8089, "http", "/health"),
+            ("OpenClaw", 8089, "http", "/health"),
            ("SigNoz", 3301, "http", "/api/v1/health"),
        ],
    },
--- a/apps/api/src/services/notifications/discord.py
+++ b/apps/api/src/services/notifications/discord.py
@@ -185,7 +185,7 @@ class DiscordWebhookProvider(NotificationProvider):

            # 建構 Discord Webhook Payload
            payload = {
-                "username": "AWOOOI ClawBot",
+                "username": "AWOOOI OpenClaw",
                "avatar_url": "https://i.imgur.com/your-avatar.png",  # 可替換
                "embeds": [self._build_embed(message)],
            }
@@ -252,7 +252,7 @@ class DiscordWebhookProvider(NotificationProvider):

            # 發送測試訊息
            test_payload = {
-                "username": "AWOOOI ClawBot",
+                "username": "AWOOOI OpenClaw",
                "content": "🔔 **AWOOOI 連線測試** - leWOOOgo Notification System 已就緒！",
            }

--- a/docs/LOGBOOK.md
+++ b/docs/LOGBOOK.md
@@ -41,6 +41,7 @@

 | 時間 | 事件 | 負責人 |
 |------|------|--------|
+| 2026-03-24 12:40 | **🔧 CD 修復**: turbo.json 快取邊界 + CD workflow (kustomize/namespace) + Alertmanager 指向 AWOOOI + 部署驗證鐵律 (HARD_RULES + Skills) | 資深顧問 |
 | 2026-03-24 10:30 | **🔴🔴 禁止 Mock 測試鐵律**: 統帥明確指示「全面禁止！！！」Mock 測試 + 移除 `test_stats_api.py` 與 `test_webhook_telegram_integration.py` + 新增 `feedback_no_mock_testing.md` | Claude Code |
 | 2026-03-24 10:15 | **📊 Statistics API 完成**: 6 端點 (summary/timeline/trends/top-resources/feedback/themes) + PostgreSQL date_trunc 優化 + Redis 快取 (5分鐘 TTL) + 12 領域主題萃取 | Claude Code |
 | 2026-03-24 10:00 | **🔧 Y/n 決策重置修復**: DecisionManager 活躍事件自動建立新 Decision (原本返回舊 COMPLETED 導致按鈕永久禁用) | Claude Code |