# 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.

- Skill: `szsip239/peter-zhou-overview` (Agent Skill)
- Install (CLI): `npx skillmds@latest add szsip239/peter-zhou-overview`
- Raw SKILL.md: https://api.skillmd.com/api/skills/szsip239/peter-zhou-overview/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: szsip239 (https://skillmd.com/u/szsip239)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/szsip239/peter-zhou-overview

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# 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.