Borrowing it
Nothing to install: this file belongs to mit-network/everything-claude-code. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mit-network/everything-claude-code/main/.agents/skills/video-editing/SKILL.mdgit clone --depth 1 https://github.com/mit-network/everything-claude-codeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mit-network/everything-claude-code/video-editing)<a href="https://agentmods.dev/skills/mit-network/everything-claude-code/video-editing"><img src="https://agentmods.dev/badge/skills/mit-network/everything-claude-code/video-editing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mit-network/everything-claude-code/video-editing"><img src="https://agentmods.dev/badge/skills/mit-network/everything-claude-code/video-editing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00072 | $0.02479 |
| Opus 5.5 | $0.00029 | $0.00992 |
| Sonnet 5.5 | $0.00014 | $0.00496 |
| Haiku 4.5 | $0.00007 | $0.00248 |
Grade A, and why
video-editing scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
resp = requests.post( This is a copy
98% identical to video-editing — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 309 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Video Editing
AI-assisted editing for real footage. Not generation from prompts. Editing existing video fast.
When to Activate
- User wants to edit, cut, or structure video footage
- Turning long recordings into short-form content
- Building vlogs, tutorials, or demo videos from raw capture
- Adding overlays, subtitles, music, or voiceover to existing video
- Reframing video for different platforms (YouTube, TikTok, Instagram)
- User says "edit video", "cut this footage", "make a vlog", or "video workflow"
Core Thesis
AI video editing is useful when you stop asking it to create the whole video and start using it to compress, structure, and augment real footage. The value is not generation. The value is compression.
The Pipeline
Screen Studio / raw footage
→ Claude / Codex
→ FFmpeg
→ Remotion
→ ElevenLabs / fal.ai
→ Descript or CapCut
Each layer has a specific job. Do not skip layers. Do not try to make one tool do everything.
Layer 1: Capture (Screen Studio / Raw Footage)
Collect the source material:
- Screen Studio: polished screen recordings for app demos, coding sessions, browser workflows
- Raw camera footage: vlog footage, interviews, event recordings
- Desktop capture via VideoDB: session recording with real-time context (see
videodbskill)
Output: raw files ready for organization.
Layer 2: Organization (Claude / Codex)
Use Claude Code or Codex to:
- Transcribe and label: generate transcript, identify topics and themes
- Plan structure: decide what stays, what gets cut, what order works
- Identify dead sections: find pauses, tangents, repeated takes
- Generate edit decision list: timestamps for cuts, segments to keep
- Scaffold FFmpeg and Remotion code: generate the commands and compositions
Example prompt:
"Here's the transcript of a 4-hour recording. Identify the 8 strongest segments
for a 24-minute vlog. Give me FFmpeg cut commands for each segment."
This layer is about structure, not final creative taste.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 309 lines · 72 tokens per session scan A daa74807d8a5
video-editing is a skill published in the GitHub repository mit-network/everything-claude-code (88 stars, last pushed 6mo ago), licensed MIT. It adds 72 tokens to every session and 2,479 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 98% identical to video-editing, differing in 2 lines, and is treated as a copy.
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