agentleFS
utillayerutillayer/youtube-agent-skill/skills/yt-shorts/SKILL.md

yt-shorts

Skill10 starsChanged 30 days agoMITView on GitHub

Find the moments in a long video that work as Shorts on their own, and write each one a new opening. Use for "cut this into shorts", "clip this", "repurpose this video", "what should I clip".

7 other projects use a version of this, 4 of them edited.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

3 rules. 0 are common, 1 is distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 7 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Read ~/.claude/youtube/voice.md if it exists.

    line 35
  2. Very specific:

    Run shorts.py and work from its shortlist.

    line 40
  3. Distinctive:

    It starts on a fresh thought: If the first sentence needs the minute before it, it is not a Short.

    line 42

“Common” is measured: kept by at least 6 in 10 of the 7 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

yt-shorts

A good Short from a long video is not its best moment. It is a moment that makes sense with nothing around it, and those are rarer.

python3 ${CLAUDE_SKILL_DIR}/shorts.py transcript.srt              # top 5, 20-55s each
python3 ${CLAUDE_SKILL_DIR}/shorts.py transcript.srt --top 8 --max 90 --json

The tools live in this skill's folder. ${CLAUDE_SKILL_DIR} is that folder in Claude Code; anywhere it does not expand (claude.ai, Codex), use the folder this SKILL.md was loaded from. It uses ../yt-edit/deadair.py and ../yt-script/hookscore.py, so install all three together.

To fetch a video's own captions:

yt-dlp --skip-download --write-auto-subs --sub-langs en --sub-format vtt -o transcript "<video url>"

Auto-captions have no punctuation, so sentence ends are guesses; the tool says when that is the case. A whisper transcript gives cleaner cuts.

Before you start

Read ~/.claude/youtube/voice.md if it exists. The new opening line is spoken or captioned in the creator's voice, and the "will not claim" list still applies.

Picking

Run shorts.py and work from its shortlist. Each Short needs all three:

  1. It starts on a fresh thought. If the first sentence needs the minute before it, it is not a Short.
  2. Something turns - a claim, then what complicates or proves it.
  3. It ends on a line, not a trailing thought.

The tool checks the first and the third and scores the opening line. Only you can check the turn: read each candidate, drop those without one, and show the best five with their timecodes and first lines so the creator can reject one at a glance.

Length: Shorts can run to three minutes. 20-55 seconds is a choice: short spans finish and loop better. Use --max for longer only when the turn needs the room. Past 60 seconds, any Content ID claim blocks the Short everywhere, so a long span over someone else's music is not an option.

Writing each one

  • A new first line. The long video's line leans on context this viewer never saw. Write the replacement and score it with ${CLAUDE_SKILL_DIR}/../yt-script/hookscore.py.
  • On-screen text for the first two seconds, in different words from the spoken line.
  • A loop: what the last line sets up so the first line answers it.
  • A vertical note: what the 9:16 crop loses from a 16:9 frame, and whether it matters.

Before you hand it over

This skill drafts; you publish. End with the copy-ready block and one question: post it as is, or another pass?

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.