learn-from-materials: Turn PDFs, books and papers into interactive learning webpages|将复杂材料转化为可追溯、可测验

by dmoshehun-prog · Codex Skill · ★ 1.1k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 64/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agent-skillsai-agentbookclaude-codecodexeducationknowledge-baselearningmarkdownworkbuddy

Quick Facts

Stars1,062
Forks98
LanguagePython
CategoryCodex Skill
LicenseMIT
Quality Score64/100
Last Updated2026-10-19
Created2026-09-08
Platformsbrowser, claude-code, codex, python
Est. Tokens~17k

About learn-from-materials

English · 中文 learn-from-materials Turn books, PDFs, slides, Word documents, web pages, and multi-file material sets into traceable knowledge bases, interactive learning pages, and synced Markdown. is a cross-agent learning skill built on the open Agent Skills specification.

Compatible Skills

These tools work well together with learn-from-materials for enhanced workflows:

  • Bloom — semantic(0.25)+complementary+rare_topics+same_lang+similar_pop+shared_platform (63%)
  • engram — semantic(0.25)+complementary+rare_topics+same_lang+similar_pop+shared_platform (63%)
  • skills — semantic(0.32)+complementary+same_lang+similar_pop+shared_platform (61%)
  • learn-faster-kit — semantic(0.18)+complementary+rare_topics+same_lang+similar_pop+shared_platform (61%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

View on GitHub → Browse Codex Skill tools