Add eval system Phase 5 — debug UI ...

Self-contained SPA at /debug/eval (route already wired in 8e0eed6).
Single index.html in the existing debug/ style — vanilla JS, embedded
CSS, no framework, no build step. Four tabs:

- Sessions — filterable table (profile / status / limit), eval status
  pill, headline avg scores, click-through to detail
- Detail — session metadata + every stored eval run, axes laid out as
  axis × expert grids with inline averages, expert comments, button to
  re-evaluate this single session
- Stats — weekly per-axis means table, optional complexity-bucket split
- Run — form to trigger any scope (unevaluated / single / all), live
  status panel polling /eval/run/{id} every 2.5s, run history with
  click-to-attach

Hash routing: #detail/<session_id> deep-links to a session.

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

Add eval system Phase 4 — read endpoints and background runner ...

REST surface for the debug UI:
- GET /eval/sessions  — overview list with eval status / latest avg /
  feedback counts (single SQL: sessions ⨝ feedback ⨝ latest run)
- GET /eval/sessions/{id} — session detail with all evaluations
- GET /eval/stats — weekly per-axis means; optional complexity-bucket split
- POST /eval/run — fire-and-forget background eval, returns run_id
- GET /eval/run/{id}, GET /eval/runs — poll progress and history

Pulled the runner loop out of cli into runner.py so both the CLI and
the REST endpoint share the same loop. State for in-flight runs lives
in an in-memory registry (single-process, cleared on restart).

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

Add eval system Phase 3 — judge runner end to end ...

Fills in the stubs from Phase 2:
- judge.render_session: full transcript with tool_call/tool_result folding,
  reactions inlined per assistant block, planning_logs appendix, no
  compression-summary substitution
- judge.run_expert: real LLM call, fence-tolerant JSON parse, single retry
  with corrective nudge on schema or parse error
- judge.evaluate_session: asyncio.gather across the three experts
- db.EvalDB: insert_evaluation_run (txn), list_evaluations,
  evaluated_session_ids, feedback_by_index helper
- cli `run` (filters: --session, --since, --limit, --re-evaluate-all,
  --dry-run, --model, --backend) and `show` (groups by eval_run_id, prints
  per-expert axes plus averaged scores)

Verified end-to-end against a real 10-message secretary session:
all three experts returned valid JSON first try; spread between strict
critic and the others surfaced as expected.

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

Add eval system Phase 2 — rubric, expert prompts, judge skeleton ...

Drafts the v1 rubric (7 axes, anchors at 10/30/50/75/100, open scale),
three independent expert prompts (strict_critic / pragmatist / tech_lead)
that all return the same JSON shape, and the orchestration scaffolding:
schema.py (pydantic models), judge.py (rubric loader, score averaging,
fence-tolerant JSON parser, new_run_metadata), cli.py with argparse for
run / show / stats. Real LLM calls and transcript rendering land in
Phase 3 — the stubs raise NotImplementedError.

`python -m debug.eval` works as the entry point. Anchor `examples` are
left empty for now; user fills them with real session_ids later without
bumping rubric_version.

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

Add eval system Phase 1 — message feedback signal ...

Spec at docs/eval_system.md describes the full LLM-as-judge plan;
this commit lands only the in-app feedback layer:
- debug/eval/ Python package with EvalDB (asyncpg) and FastAPI router
  exposing /eval/feedback (set / clear / list)
- message_feedback postgres table keyed by (session_id, message_index)
- thumbs up / down on each completed assistant block in the webclient,
  optimistic update with rollback on failure

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