claude-video-vision: Give Claude the ability to watch and understand videos — Claude Code plugin with frame extraction

by jordanrendric · MCP Server · ★ 1.4k

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

How to install claude-video-vision

/plugin marketplace add https://github.com/jordanrendric/claude-video-vision
/plugin install claude-video-vision

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

Security audit verdict: SAFE · quality 72/100

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.

claude-codeclaude-code-pluginffmpeggeminimcpmcp-servermultimodalvideo-analysiswhisper

Quick Facts

Stars1,368
Forks159
LanguageTypeScript
CategoryMCP Server
LicenseMIT
Quality Score72/100
Open Issues8
Last Updated2026-10-14
Created2026-03-31
Platformsclaude-code, gemini, mcp, node
Est. Tokens~15k

About claude-video-vision

Claude Code Video Vision Give Claude the ability to watch and understand videos. A Claude Code plugin that extracts frames via ffmpeg and processes audio via multiple backends (Gemini API, local Whisper, or OpenAI API). Claude receives frames as images and audio transcription with timestamps — the plugin is a perception layer, not an interpretation layer.

Compatible Skills

These tools work well together with claude-video-vision for enhanced workflows:

  • codex-collab — semantic(0.20)+complementary+same_lang+similar_pop+shared_platform (57%)
  • code-container — semantic(0.18)+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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