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Overview

Skill szsip239/peter-zhou/skills/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.From its SKILL.md

Install
npx -y skills add szsip239/peter-zhou --skill overview

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
  • runs commandsInstructs the agent to run 3 commands, including `scripts/knowledge_overview.py overview` and 2 more.

SKILL.md

1.8 KB, 322 tokens by cl100k_base, as published. Nobody here has run it

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.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Keep looking

Skills are one crate of 325,949. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.