vox-ai-motion-graphics-generator: Turn any topic into a finished Vox-style paper-collage explainer / motion graphics video

by Anil-matcha · Claude Skill · ★ 181

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

Security audit verdict: SAFE · quality 62/100

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-skillai-motion-graphicsai-videoclaude-codecollageexplainer-videoffmpegfluximage-to-videomotion-graphics

Quick Facts

Stars181
Forks34
LanguagePython
CategoryClaude Skill
Quality Score62/100
Open Issues1
Last Updated2026-09-02
Created2023-11-07
Platformsclaude-code, codex, python
Est. Tokens~15k

About vox-ai-motion-graphics-generator

Vox AI Motion Graphics Generator Turn any topic into a finished Vox-style paper-collage explainer video 📺 Watch the Vox AI Motion Graphics Generator in action → 🚨 The problem with making explainer videos today: ❌ Fragmented workflow — separate tools for scripting, image gen, animation, voice-over, music, and captions ❌ No narrative structure — raw text-to-video models don't understand story...

Compatible Skills

These tools work well together with vox-ai-motion-graphics-generator for enhanced workflows:

  • vox-director — semantic(0.73)+rare_topics+same_lang+similar_pop+shared_platform (75%)
  • mcp-video — semantic(0.42)+complementary+rare_topics+same_lang+similar_pop+shared_platform (74%)
  • kinocut — semantic(0.42)+complementary+rare_topics+same_lang+similar_pop+shared_platform (74%)
  • Orkas-VideoStudio — semantic(0.64)+complementary+rare_topics+similar_pop (73%)

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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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