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Score a bundled model's answers against the gold finance QA set and report honest metrics + calibration. Usage /evaluate [predictions.json]
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Baseado na classificação ocupacional SOC
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| name | evaluate |
| description | Score a bundled model's answers against the gold finance QA set and report honest metrics + calibration. Usage /evaluate [predictions.json] |
Run the full evaluation and write a grounded report. Default predictions file:
data/candidates_overconfident.json. The tools own every number; you only run them,
read them back, and explain them.
Score (scorer agent):
python -m tools.score --gold data/gold.json --pred <predictions.json> --out reports/_scored.json
Record accuracy, mean_f1, n, n_correct.
Calibrate (calibration-analyst agent):
python -m tools.calibration --scored reports/_scored.json
Record ece, mean_confidence, the confident_wrong list, and the reliability bins.
Report (reviewer agent): write reports/<model>.md with, every figure cited from
the tools above:
Rules: report the numbers honestly; never round away a failure. Do not call an
overconfident model "well calibrated" because its accuracy looks acceptable. If the
confident_wrong list is non-empty, it leads the report.
Try these to start:
/evaluate → evaluates data/candidates_overconfident.json; expect ~0.5 accuracy, high ECE, five confident-wrong cases./evaluate data/candidates_calibrated.json → a model whose confidence tracks its accuracy; expect low ECE and no confident-wrong cases.