ai-video-editing-skill: AI Agent Skill for automated vlog editing

by znyupup · Agent Tool · ★ 176

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

Featured in: Claude Video Editing & Remotion Skills

Security audit verdict: SAFE · quality 67/100

None of the 43 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 43 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

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.

agentaiai-agentautomationffmpegskillvibe-codingvideo-editingvlogwhisper

Quick Facts

Stars176
Forks27
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score67/100
Open Issues1
Last Updated2026-04-27
Created2026-04-16
Platformspython
Est. Tokens~9k

About ai-video-editing-skill

🎬 Vlog Auto Edit — AI Agent 自动剪辑旅行 Vlog 把一堆手机拍的旅行素材，用 AI Agent 自动剪成一个完整 Vlog。 你只需要提供素材文件夹，剩下的交给 Agent。 Vibe Editing — 不用学剪辑软件，不用自己挑选素材，不用纠结叙事结构。 告诉 AI 你想要什么风格，它帮你从头到尾搞定。 🧑‍💻 by nyx研究所 — GitHub · B站 · 小红书 @nyx研究所 · X / Twitter ✨ 这是什么 这是一份给 AI Agent（Claude Code / Hermes / OpenClaw / GPT 等）使用的 Skill 文件，定义了从原始素材到成品视频的完整自动剪辑工作流。 它不是一个传统的软件程序——而是一份指导 AI Agent 工作的知识文件，包含： 完整的工作流程定义 每个步骤的具体命令和代码 24...

Compatible Skills

These tools work well together with ai-video-editing-skill for enhanced workflows:

  • OpenEdit — semantic(0.47)+complementary+same_lang+similar_pop+shared_platform (66%)
  • oh-my-cassette — semantic(0.42)+complementary+same_lang+similar_pop+shared_platform (65%)
  • video-editing-skill — semantic(0.19)+complementary+shared_fw(openai)+same_lang+similar_pop+shared_platform (65%)
  • capcut-ai-editor — semantic(0.18)+complementary+shared_fw(openai)+same_lang+similar_pop+shared_platform (64%)
  • claude-skills — semantic(0.16)+complementary+shared_fw(openai)+same_lang+similar_pop+shared_platform (64%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 43 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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