feat: Add evaluation hooks, skill adaptation and team pipeline config

- Add EvaluationHook for post-execution agent evaluation
- Add SkillAdaptationHook for dynamic skill adaptation
- Add team/ directory with team coordination logic
- Add TEAM_PIPELINE.yaml for smoke_fullstack pipeline config
- Update RuntimeView, TraderView and RuntimeSettingsPanel UI
- Add runtimeApi and websocket services
- Add runtime_state.json to smoke_fullstack state

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

backend/enrich/llm_enricher.py

@@ -4,6 +4,7 @@
 from __future__ import annotations
 
 import asyncio
+import logging
 from concurrent.futures import ThreadPoolExecutor
 from typing import Any
 
@@ -12,6 +13,8 @@ from pydantic import BaseModel, Field
 from backend.config.env_config import canonicalize_model_provider, get_env_bool, get_env_str
 from backend.llm.models import create_model
 
+logger = logging.getLogger(__name__)
+
 
 class EnrichedNewsItem(BaseModel):
     """Structured output schema for one enriched article."""
@@ -156,7 +159,8 @@ def analyze_news_row_with_llm(row: dict[str, Any]) -> dict[str, Any] | None:
     ]
     try:
         response = _run_async(model(messages=messages, structured_model=EnrichedNewsItem))
-    except Exception:
+    except Exception as e:
+        logger.warning(f"LLM enrichment failed: {e}")
         return None
 
     payload = _normalize_enrichment_payload(getattr(response, "metadata", None))
@@ -268,7 +272,8 @@ def analyze_range_with_llm(payload: dict[str, Any]) -> dict[str, Any] | None:
         response = _run_async(
             model(messages=messages, structured_model=RangeAnalysisPayload),
         )
-    except Exception:
+    except Exception as e:
+        logger.warning(f"LLM enrichment failed: {e}")
         return None
 
     metadata = getattr(response, "metadata", None)