peter-zhou-mistake-bank
Store, validate, review, confirm, and query Peter Zhou mistake-bank records. Use for RawMistakeRecord persistence, SourceDocument status, is_wrong review, id allocation, crop refs, and script-owned JSON writes.
Datos de origen
- Repositorio
- szsip239/peter-zhou
- Última actividad en el origen
- 20 de julio de 2026 a las 14:54
- Idioma detectado de SKILL.md
- inglés
- Estrellas
- 1
- Forks
- 0
Opciones de instalación
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SKILL.md
Instrucciones de origen · Vista previa de solo lectura- name
- peter-zhou-mistake-bank
- description
- Store, validate, review, confirm, and query Peter Zhou mistake-bank records. Use for RawMistakeRecord persistence, SourceDocument status, is_wrong review, id allocation, crop refs, and script-owned JSON writes.
# Mistake Bank
Use this subskill when a recognized result must become canonical local data.
Core contract:
- Persist one JSON file per subject under the mistake bank.
- Keep `SourceDocument` state separate from subject mistake records.
- Keep `is_wrong=false` records for audit, but exclude them from final stats and practice by default.
- Generate review reports from canonical data; do not persist separate long-lived review tables.
- Allocate ids and write JSON only through `scripts/allocate_id.py` and `scripts/json_store.py`; see `references/schema.md`.
- After every successful `scripts/intake_recognition.py ingest`, present the returned `review_report` as the next skill step for user review. Focus the user on `needs_review=true`, low-confidence answers, ambiguous teacher marks, and crop screenshots. If the user does not review, leave the source status as `processed`; only `apply-review --confirm` moves it to `confirmed`.
- Use `scripts/intake_recognition.py review-report` to regenerate the same derived report, `apply-review` for user corrections/confirmation, and `repair-crops` for script-owned screenshot bbox fixes.
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