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Cut-AI: Skill for Claude Code

.claude/skills/viral-clips/SKILL.md

viral-clips is a skill for Claude Code from AI-Nate/Cut-AI. It costs 52 tokens per session (1,326 once invoked), scanned A, original, MIT.

A video-editing workflow for Cut-AI that uses a VTT subtitle transcript and an MP4 recording to find highlights and prepare short-form clips.

In plain words
What is it for?
Use it to identify highlights, cut clips, add English and Chinese subtitles, extract clip transcripts, and draft content for seven platforms.
Why use it?
It turns a long recording into selected clips and related publishing material without requiring you to choose every segment manually.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter.

This is AI-Nate/Cut-AI's own configuration. It tells Claude Code how to work on Cut-AI itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything Cut-AI configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is mkdir -p output/<SESSION> && source venv/bin/activate && python script/cut_ai.py analyze --transcript <VTT> --output <HIGHLIGHTS>.

not rated 22repo 9mo ago A scan 52 tokens original MIT
View source ↗ AI-Nate/Cut-AI
Reuse

Borrowing it

Nothing to install: this file belongs to AI-Nate/Cut-AI. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/AI-Nate/Cut-AI/main/.claude/skills/viral-clips/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/AI-Nate/Cut-AI

Made for: Claude Code.

Wrote this? Show the measurements

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agentmods badge for viral-clips

README.md
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Your own site
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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.

agentmods 80×15 button for viral-clips

Your own site · 80×15
<a href="https://agentmods.dev/skills/ai-nate/cut-ai/viral-clips"><img src="https://agentmods.dev/badge/skills/ai-nate/cut-ai/viral-clips.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 52 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,326 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00052 $0.01326
Opus 5.5 $0.00021 $0.00530
Sonnet 5.5 $0.00010 $0.00265
Haiku 4.5 $0.00005 $0.00133

Measured 2mo ago against content hash f36808cfc67b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

viral-clips scanned grade A with 0 findings 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 2mo 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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/viral-clips/SKILL.md · 133 lines

How it starts

The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are running the Cut-AI viral clips pipeline. This is a 3-phase automation that takes a recording session directory and produces viral short-form clips with subtitles and platform-specific content drafts.

The user passed this data directory: $ARGUMENTS

Setup

  1. Derive the session name from the directory basename (e.g., data/020725 -> session = 020725).
  2. Auto-detect the .vtt transcript file and .mp4 video file inside the $ARGUMENTS directory using Glob. There should be exactly one of each. If there are multiple, ask the user which to use.
  3. Set these variables for the rest of the pipeline:
    • VTT = path to the .vtt file
    • MP4 = path to the .mp4 file
    • SESSION = session name
    • HIGHLIGHTS = output/<SESSION>/highlights_<SESSION>.json
    • OUTPUT_DIR = output/<SESSION>

Phase 1: Analyze Transcript

First create the output directory, then run the Gemini analysis to identify highlight clips:

mkdir -p output/<SESSION> && source venv/bin/activate && python script/cut_ai.py analyze --transcript <VTT> --output <HIGHLIGHTS>

After the command completes:

  • Display a summary table of all highlights found (number, title, time range, category, viral score).
  • STOP and ask the user to review <HIGHLIGHTS>. Tell them:
    • "Review and edit <HIGHLIGHTS> if needed (adjust timestamps, remove/reorder clips, edit titles). Reply go when ready to proceed to Phase 2."
  • Do NOT proceed until the user confirms.

Phase 2: Cut Video with Dual-Language Subtitles

Once the user confirms, generate English and Chinese subtitle clips:

source venv/bin/activate && python script/cut_ai.py clip_dual --video <MP4> --highlights <HIGHLIGHTS> --subtitles <VTT> --output <OUTPUT_DIR>

This produces:

  • <OUTPUT_DIR>/English/ - clips with burned-in English subtitles
  • <OUTPUT_DIR>/Chinese/ - clips with burned-in bilingual English + Chinese subtitles

After completion, report how many clips were generated in each directory.

Read the full file on GitHub · 133 lines

Changes

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.

  1. 2mo ago First seen · 133 lines · 52 tokens per session scan A f36808cfc67b

Subscribe to this mod's changes

viral-clips is a skill published in the GitHub repository AI-Nate/Cut-AI (22 stars, last pushed 9mo ago), licensed MIT. It adds 52 tokens to every session and 1,326 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Creative and media
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