FireRed-OpenStoryline: AI video editing agent that transforms manual editing into intention-driven directing through natural language

by FireRedTeam · MCP Server · ★ 3.6k

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

Featured in: Claude Video Editing & Remotion Skills

How to install FireRed-OpenStoryline

git clone https://github.com/FireRedTeam/FireRed-OpenStoryline.git

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

Security audit verdict: SAFE · quality 65/100

Flagged: sudo usage. 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.

agentchatbotlangchainmcpskillsvideovideo-cutvideo-editingvideo-editing-tools

Quick Facts

Stars3,606
Forks420
LanguagePython
CategoryMCP Server
LicenseApache-2.0
Quality Score65/100
Open Issues33
Last Updated2026-07-31
Created2026-02-07
Platformsmcp, python
Est. Tokens~16k

About FireRed-OpenStoryline

🇨🇳 简体中文 | 🌏 English

Compatible Skills

These tools work well together with FireRed-OpenStoryline for enhanced workflows:

  • autoclip — semantic(0.51)+complementary+rare_topics+same_lang+similar_pop+shared_platform (72%)
  • openclip — semantic(0.48)+complementary+rare_topics+same_lang+shared_platform (61%)
  • bottube — semantic(0.32)+complementary+same_lang+similar_pop+shared_platform (56%)
  • claude-shorts — semantic(0.41)+complementary+rare_topics+same_lang+shared_platform (54%)
  • FunClip — semantic(0.20)+complementary+same_lang+similar_pop+shared_platform (52%)

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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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