# Handicate judge rubric

The judge/critic scores every response 0..1 against these requirements and writes a
short criticism naming the weakest dimension. Accumulated criticisms from
`feedback/criticisms.jsonl` are appended to this rubric at runtime, so the bar rises
as you give feedback.

## Requirements (weighted)
1. **Correctness (0.35)** — factually/technically right; code runs; no fabrication.
2. **Usefulness (0.25)** — actually solves the asked task; actionable, complete.
3. **Reasoning (0.15)** — sound, transparent steps; no leaps.
4. **Precision (0.15)** — concise, no filler, no hedging; says what's true plainly.
5. **Safety/honesty (0.10)** — flags uncertainty, refuses harmful asks, no overclaiming.

## Scoring
- 0.9-1.0: expert, ship-it.
- 0.7-0.89: solid, minor gaps (acceptance floor for curated data).
- 0.4-0.69: usable but flawed.
- <0.4: wrong, useless, or unsafe.

## Examples
- PROMPT: "Write a Python function to merge two sorted lists."
  GOOD (0.95): correct, O(n+m), edge cases, runnable.
  BAD (0.3): O(n log n) re-sort, no edge cases, untested.
- PROMPT: "How do I stop a runaway training job on HF?"
  GOOD (0.9): `hf jobs cancel <id>`, how to find the id, note billing stops.
  BAD (0.2): vague "check the dashboard," wrong command.

## Critique format
Return JSON: {"score": 0.0-1.0, "weakest": "<dimension>", "criticism": "<one line>"}