fix(api): 全面清除假信心分數 - 遵循 feedback_confidence_truthfulness.md

🔴 違規修正: 規則匹配/Expert System 不是 AI 分析，confidence 必須 = 0.0

修正檔案:
- agents/action_planner.py: 0.9 → 0.0
- agents/blast_radius.py: 0.85/0.5/0.9 → 0.0
- agents/security.py: 計算公式 → 0.0
- signoz_webhook.py: 0.7 → 0.0
- auto_approve.py: default 0.5 → 0.0
- ci_auto_repair.py: 整個計算函數 → return 0.0
- error_analyzer_service.py: default 0.5 → 0.0
- intent_classifier.py: 計算公式 → 0.0
- openclaw.py: default 0.5 → 0.0
- resource_resolver.py: 0.8 → 0.0
- k8s_naming.py: 0.9/0.7 → 0.0

只有 LLM 真實分析返回的 confidence 才能 > 0

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

apps/api/src/services/openclaw.py

@@ -1010,7 +1010,7 @@ class OpenClawService:
                     raw_confidence=data.get("confidence"),
                     forcing_collab=True,
                 )
-                data["confidence"] = 0.5  # 低信心分數
+                data["confidence"] = 0.0  # 🔴 LLM 未返回信心度，設為 0
                 data["primary_responsibility"] = "COLLAB"  # 強制協作處理
 
             # Step 3: 使用 Pydantic 驗證 (會自動正規化 risk_level, data_impact 等)
@@ -1204,7 +1204,7 @@ Trace URL: {signoz_trace_url}
 ## 🔍 Expert System Initial Diagnosis
 - **Matched Rule**: {expert_context.get('initial_diagnosis', 'unknown')}
 - **Diagnosis**: {expert_context.get('diagnosis_description', 'N/A')}
-- **Confidence**: {expert_context.get('expert_confidence', 0.5):.0%}
+- **Confidence**: {expert_context.get('expert_confidence', 0.0):.0%}
 - **Requires Human Review**: {'Yes' if expert_context.get('requires_human_review') else 'No'}
 - **Suggested Diagnosis Commands**:
 {diagnosis_cmds_str}