feat(simulate): MLflow tracking, Airflow DAG integration, health checks for mlflow/airflow

- sim_runs schema: add judge_mode, n_policies, airflow_dag_run_id, mlflow_run_id columns
- admin health endpoint: add mlflow + airflow checks (Basic auth for Airflow API)
- admin nav: add Simulations page link; rename section label
- runner.py: optional MLflow experiment tracking; multi-policy support
- sim_dag.py: Airflow DAG for offline sim pipeline
- admin simulate page + API client methods for sim runs
- shared-types tsconfig: exclude test files from build

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

services/api/src/db/schema.ts

@@ -112,9 +112,13 @@ export const simRuns = sqliteTable('sim_runs', {
   tasksPerRound: integer('tasks_per_round').notNull().default(8),
   useLlm: integer('use_llm', { mode: 'boolean' }).notNull().default(false),
   status: text('status').notNull().default('pending'),  // 'pending'|'running'|'done'|'failed'
+  judgeMode: text('judge_mode').notNull().default('rule'),
+  nPolicies: integer('n_policies').notNull().default(2),
   summaryJson: text('summary_json'),           // JSON: { [policy]: PolicySummary }
   winner: text('winner'),
   personaBreakdownJson: text('persona_breakdown_json'), // JSON: { [persona]: { [policy]: {reward,n} } }
+  airflowDagRunId: text('airflow_dag_run_id'),
+  mlflowRunId: text('mlflow_run_id'),
   createdAt: text('created_at').notNull(),
   finishedAt: text('finished_at'),
 });