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.

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.

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.

peter-zhou-mistake-bank