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Read a YouTube Studio audience-retention export and find where viewers actually leave, then say what to change. Use for "why do people stop watching", "my retention is bad", a pasted retention chart or CSV, or "fix my pacing".

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

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SKILL.md

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yt-retention

The retention graph is the only honest feedback YouTube gives you. Almost nobody exports it.

python3 retention.py retention.csv --duration 600
python3 retention.py retention.csv --transcript transcript.srt

Getting the file: Studio -> a video -> Analytics -> Engagement -> the audience-retention chart -> the download icon -> "Audience retention".

Three different problems

  • HOOK LEAK - what is lost in the first 30 seconds. Under 25% is healthy. This is always the first thing to fix and it is always the first fifteen seconds of script, never the edit.
  • CLIFFS - single steep drops. A cliff is a moment: a topic change with no signposting, a sponsor read, a long setup. With --transcript the tool prints what was being said there, which is what makes the report actionable instead of interesting.
  • SLIDE - the steady bleed across the middle. A flat slide is pacing. The fix is cutting, not rewriting.

What to hand back

Name the single biggest leak and one change for it. Not a list of five. Then, only if asked, the rest. And if the hook leak is healthy and the slide is flat, say the video is fine and the problem is packaging - send them to /yt-package.

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

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

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  • Gen Agent Trust Hub19d

    The yt-retention skill is designed to analyze YouTube audience retention data from CSV and transcript files. The skill is generally safe but carries minor risks associated with the dynamic loading of local modules and the ingestion of untrusted transcript data which provides a potential surface for indirect prompt injection.

  • Socket19d

    No alerts

  • Snyk19d

    Risk: LOW · No issues

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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