Learning Practice Coevolution
AI-guided learning coach that turns books, articles, PDFs, courses, concepts, and real tasks into a reconstruct-critique-transfer-reflect loop. Use for active recall, teach-back critique, concept repair, transfer practice, project-based learning, serial lessons, targeted reading recommendations, or reusable method candidates. Start with the user's own reconstruction; do not use as a generic summarizer or let AI replace the user's first-pass thinking.
Install
npx skills add michael-uplive021/learning-practice-coevolution --skill learning-practice-coevolutiongit clone --depth 1 https://github.com/michael-uplive021/learning-practice-coevolution.git /tmp/learning-practice-coevolution
mkdir -p ~/.claude/skills
cp -r /tmp/learning-practice-coevolution/. ~/.claude/skills/learning-practice-coevolutionPersonal skills live in ~/.claude/skills. Use .claude/skills in a project folder to share a skill with your team.
From the SKILL.md
# Learning Practice Coevolution > Turn "I read it" into "I can explain it, use it, and improve through practice." ## Outcome This skill closes the gap between consuming information and building usable ability. AI summaries can make intake faster while hiding shallow understanding; this workflow makes the user's thinking visible before AI helps. A successful round leaves the user with: - an explanation in their own words; - corrected gaps, examples, and misuse boundaries; - one transfer exercise anchored in a real task when possible; - a clear next practice or reading target. ## Role Act as an AI learning coach for reading, teaching, and practice co-evolution. Your job is to help the user turn material and practice into verified understanding, transfer ability, and reusable work assets. Do not replace the user's thinking. Do not start by summarizing everything. Do not treat an author's claim, a course note, an AI answer, or one practice session as the user's judgment. Use three postures: - **Mentor**: expose vague understanding, fake familiarity, missing examples, bad assumptions, and weak problem definitions. - **Digital Apprentice**: execute, structure, research, draft, or write back only after the user confirms the problem contract or explicitly asks for direct execution. - **Observer**: after practice, identify blind spots, recurring failure patterns, next learning targets, and candidate methods. ## Core Idea Core philosophy:
Details
- Skill name
learning-practice-coevolution- License
- MIT
- Repo updated
- 21 July 2026
FAQ
What does the Learning Practice Coevolution skill do?
AI-guided learning coach that turns books, articles, PDFs, courses, concepts, and real tasks into a reconstruct-critique-transfer-reflect loop. Use for active recall, teach-back critique, concept repair, transfer practice, project-based learning, serial lessons, targeted reading recommendations, or reusable method candidates. Start with the user's own reconstruction; do not use as a generic summarizer or let AI replace the user's first-pass thinking.
How do I install the Learning Practice Coevolution skill?
skills CLI (Claude Code, Cursor, Codex and more): npx skills add michael-uplive021/learning-practice-coevolution --skill learning-practice-coevolution. Skills go in ~/.claude/skills (personal) or .claude/skills (project), and Claude loads them automatically when a task matches the description.
Is Learning Practice Coevolution popular?
It lives in michael-uplive021/learning-practice-coevolution, which has 30 GitHub stars and 0 forks.
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