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peter-zhou-overview

Compute Peter Zhou learning overview reports. Use for total wrong counts, subject breakdown, mastery progress, repeat-correction markers, weak knowledge tags, correction candidates, and recent source/correction activity.

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Quellinformationen

Repository
szsip239/peter-zhou
Letzte Quellaktivität
23. Juli 2026 um 14:09
Erkannte Sprache von SKILL.md
Englisch
Sterne
1
Forks
0

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SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
peter-zhou-overview
description
Compute Peter Zhou learning overview reports. Use for total wrong counts, subject breakdown, mastery progress, repeat-correction markers, weak knowledge tags, correction candidates, and recent source/correction activity.
# Overview Use this subskill when the user asks for progress, mastery, weak points, or what to practice next. Core contract: - Compute reports on demand from canonical JSON stores with `scripts/knowledge_overview.py overview`. - For Agent-chat status, progress, general Peter Zhou startup, or “what next” requests, run `scripts/chat_dashboard.py --data-dir <dir> --top-n 3 --json`. Send its `markdown` field directly, preserving emoji, action numbering, and links to existing local assets. Retain `actions` for routing: execute each structured `execution` contract and use its natural-language `prompt` only as fallback. - When the Dashboard offers `⏱ 今日 10 分钟复习`, route it to the resumable `agent_chat_review` workflow rather than generating a paper. - The dashboard discovers deep KnowledgeModule courseware from validated runtime metadata. It must ignore unvalidated metadata, missing HTML, and references that resolve outside `data/`; do not add these modules to the lightweight `knowledge-pages/index.json`. - Check `scripts/learning_profile.py show --data-dir <dir> --json` when the user asks for personalized priorities or study advice. If the profile is missing, overview can still run, but personalized recommendations should ask the first-use interview first. - Count only `is_wrong=true` records for final wrong-question statistics. - Derive mastery from correction counters; do not maintain aggregate tables. - Sort weak tags by unmastered count, then low tag mastery, then total wrong count.
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