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/yt-viral

@a2feb21
by Jake Schincarioljakeschincariol/youtube-agent-skill707 stars
179

Find what is actually working in the user's niche on YouTube and rank it by how far each video beat its own channel, then name the formula. Use for "what's working right now", "find viral videos in my niche", "why did this blow up", competitor research, or a swipe file.

Use this Skill: https://skilld.dev/gh/jakeschincariol/youtube-agent-skill/yt-viral

Nothing lands on disk. Nothing to clean up.

Fork this Skill

Edit a local copy. It keeps the author and licence.

SKILL.md

≈70 tokens for metadata: the name and description. ≈357 when used: this file.

Description uses 3.5% of example budget

Before choosing a Skill, your Agent reads its name and description. All available Skills share that space.

  • A shorter description leaves more room for other Skills. This entry exceeds our 1% size suggestion.

In our Claude Code example, all Skill names and descriptions share 8,000 characters. This Skill uses ≈281 characters, or 3.5%.

The 1% threshold is a size suggestion. Longer descriptions can still fit.

Your model, settings, and other Skills decide how much text your Agent can read.

Example settings and source

The example uses a 200k-token context and default Claude Code settings. The count includes the name, description, separators, and when_to_use when present. Codex also counts local file paths.

Skit's source and limits: Codex 0.160.1, Claude Code 2.1.292.

yt-viral

Raw view counts rank channel size, not ideas. This ranks by multiple over each channel's own median, which is the only version of the question that is about the video.

python3 swipe.py collected.json --min 2.0

Collecting the input

You need at least four videos per channel or a median means nothing, and the tool will skip the channel and tell you it did. Collect them however the user prefers - yt-dlp --flat-playlist -J against a channel URL is the fastest, the public page works, a manual list works.

[{"channel":"...","title":"...","views":412000,"url":"...","duration":613}]

Read, do not scrape. Public listings only, never a logged-in session, never the user's own account credentials.

Reading the output

The multiple is the signal. The formula line is a judgement about the TITLE, matched against the 21 formulas - it is not a claim about why the video worked, and you should say so when you present it.

What to hand back: the top five with their multiples, the formula each used, and the ONE structural thing they share. Then the harder line - which of those the user could actually make this week, in their voice, with what they have.

The gate

Nothing here publishes. This skill writes and you publish. Every output ends in a block the user copies, and the last line of every run is the question: ship it, or change it?

Source: SKILL.md on GitHub

Skill behaviors

skilld matched fixed text patterns in SKILL.md and file names. Patterns miss obfuscated code.

skilld run checks every file with the same patterns. It asks for approval before it loads a Skill with a behavior marked Needs approval.

Third-party checks

1 warning19d3 checks · Risk SAFE
  • Gen Agent Trust Hub19d

    The skill processes YouTube video data to identify high-performing content. It is generally safe but susceptible to indirect prompt injection via untrusted video titles and allows the agent to read arbitrary JSON files via command-line arguments.

  • Socket19d

    No alerts

  • Snyk19d

    Risk: MEDIUM · 1 issue

Provenance

Signed by skilld at a2feb21. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 1 hour ago.

Activeupdated 3 weeks ago

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