magic-slide: A skill that generates polished self-contained HTML presentations with smooth Magic Move-style slide

by daniel-style · AI Tool · ★ 172

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How to install magic-slide

npx skills add daniel-style/magic-slide

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 67/100

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.

Quick Facts

Stars172
Forks9
LanguagePython
CategoryAI Tool
LicenseMIT
Quality Score67/100
Last Updated2026-08-30
Created2026-04-27
Platformspython
Est. Tokens~16k

About magic-slide

Magic Slide Official website and live demo: magicslide.show Magic Slide is a toolkit for building polished, self-contained HTML presentations with Magic Move transitions, integrated speaker notes, PipeLLM image generation, and high-quality web search for research-backed decks.

Compatible Skills

These tools work well together with magic-slide for enhanced workflows:

  • yaml-to-html-skill — semantic(0.36)+complementary+same_lang+similar_pop+shared_platform (63%)
  • slide-creator — semantic(0.28)+complementary+same_lang+similar_pop+shared_platform (60%)
  • skillware — semantic(0.19)+complementary+same_lang+similar_pop+shared_platform (57%)
  • html-to-editable-pptx — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (56%)

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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:

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