feat(serving): replace MLflow run logging with native trace spans

Convert ml-serving from isolated MLflow runs to nested traces using
mlflow.start_span_no_context(). The recommend endpoint now emits a full
span tree: recommend (CHAIN) → build_context (TOOL), agent:* (AGENT) ×N,
llm_orchestrator (LLM). Compute and infer endpoints each emit a single span.

Supporting changes:
- mlflow-skinny>=3.1.0 added to requirements
- MLflow configured with --serve-artifacts + mlflow-artifacts:/ default root
  for cross-container artifact proxy (spans now persist from ml-serving)
- --allowed-hosts extended to include mlflow:5000 (SDK includes port in Host)
- science_destiny slider wired through prompts.py and recommend endpoint
- Config page exposes science/destiny slider (0=data-driven, 100=intuitive)
- Tip page shows rationale inline on tap

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

services/api/src/routes/agent-outputs.ts

@@ -166,7 +166,7 @@ export async function computeAndStore(userId: string, agentId: string): Promise<
     method: 'POST',
     headers: { 'Content-Type': 'application/json' },
     body: JSON.stringify({ user_id: userId, tasks, profile, feedback_history: feedbackHistory, agent_prefs: agentPrefs }),
-    signal: AbortSignal.timeout(15_000),
+    signal: AbortSignal.timeout(60_000),
   });
 
   if (!mlResp.ok) {