feat(airflow): integrate bench harness into bench_collect DAG

New DAG (`ml/pipelines/bench_dag.py`) with three linked tasks:
1. collect.py — generates candidates, logs to MLflow
2. export_for_judge — exports pending runs for Claude Code scoring
3. compare — generates leaderboard by (model, prompt) cell

Config via dag_run.conf supports all collect.py options (models, prompts,
n_tips, n_scenarios, temperature, experiment name, max_model_b).

New admin API endpoints (`services/api/src/routes/bench.ts`):
- GET /api/bench/experiments — list tip-bench-* experiments
- POST /api/bench/run — trigger DAG with custom config
- GET /api/bench/runs/:experiment — list runs in experiment
- GET /api/bench/leaderboard/:experiment — leaderboard by (model, prompt)

All endpoints require admin auth. Human judge (Claude Code) scores are
applied manually post-export; future enhancement: add webhook to DAG.

Admin UI can now trigger and monitor benchmarks from a dashboard panel.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

services/api/src/index.ts

@@ -16,6 +16,7 @@ import { recommenderRouter } from './routes/recommender.js';
 import { userRouter } from './routes/user.js';
 import { pushRouter } from './routes/push.js';
 import { adminRouter, adminInternalRouter } from './routes/admin.js';
+import benchRouter from './routes/bench.js';
 import { mkdir } from 'fs/promises';
 import { dirname } from 'path';
 import { requireAuth } from './middleware/session.js';
@@ -66,6 +67,7 @@ app.use('/api/user', userRouter);
 app.use('/api/push', pushRouter);
 app.use('/api/admin', adminRouter);
 app.use('/api/admin', adminInternalRouter);
+app.use('/api/bench', requireAuth as any, requireAdmin as any, benchRouter);
 
 app.use('/api/ml', requireAuth as any, requireAdmin as any, async (req: Request, res: Response) => {
   const mlUrl = config.ML_SERVING_URL;