Slim eval rubric to 3 levels with one reference per axis ...

Five anchors per axis (10/30/50/75/100, even after the earlier shift)
were both redundant and amplified the model's snap-to-round-numbers
prior. Cut to three level descriptions per axis (weak / typical /
strong) with a single non-round reference score (53) on `typical`.
Re-state the scale as open-ended with no upper bound to make the
"future Navi may exceed past ceilings" intent explicit.

- rubric_v1.yaml: anchors → levels (5 → 3 per axis), reference score
  53 only on typical, scale framed as fully open-ended.
- judge.py: render_rubric_for_prompt walks the new `levels` shape and
  surfaces the reference score only when present.
- expert prompts (strict_critic, pragmatist, tech_lead): drop the
  example output blocks (their concrete numbers were misleading the
  judges), rewrite the scale paragraph for the new structure.
- schema.py: docstring no longer pins ">100" as the open-scale marker.

User intent: dynamics, not absolute scores. Weekly aggregates over
three averaged experts smooth individual snap-to-5 into continuous
trends; the rubric is a calibration aid, not a grading ceiling.

Co-Authored-By: Claude Opus 4.7 <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 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>