bug-reproduce
Turn a known bug into a tight, red-capable reproducer, then prove the reproducer locks that…
YouTube transcripts to summaries, threads, blogs.
$ npx -y skills add Prismer-AI/PrismerCloud --skill prismer-youtube-content --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/prismer-youtube-contentContext preview
The summary Claude sees to decide when to auto-load this skill.
YouTube transcripts to summaries, threads, blogs.
name: prismer-youtube-content
scope: common
category: media
description: "YouTube transcripts to summaries, threads, blogs."
version: 1.0.0
author: Teknium (teknium1), Hermes Agent
license: MIT
platforms: [linux, macos, windows]
metadata:
nativeReplaces: [youtube-content]
hermes:
tags: [YouTube, Video, Transcripts, Media]
related_skills: []Use when the user shares a YouTube URL or video link, asks to summarize a video, requests a transcript, or wants to extract and reformat content from any YouTube video. Transforms transcripts into structured content (chapters, summaries, threads, blog posts).
Extract transcripts from YouTube videos and convert them into useful formats.
Use `uv` so the dependency is installed into the same task-local isolated environment that runs the helper script:
uv pip install youtube-transcript-api==1.2.2
`SKILL_DIR` is the directory containing this SKILL.md file. The script accepts any standard YouTube URL format, short links (youtu.be), shorts, embeds, live links, or a raw 11-character video ID.
# JSON output with metadata uv run python SKILL_DIR/scripts/fetch_transcript.py "https://youtube.com/watch?v=VIDEO_ID" # Plain text (good for piping into further processing) uv run python SKILL_DIR/scripts/fetch_transcript.py "URL" --text-only # With timestamps uv run python SKILL_DIR/scripts/fetch_transcript.py "URL" --timestamps # Specific language with fallback chain uv run python SKILL_DIR/scripts/fetch_transcript.py "URL" --language tr,en
After fetching the transcript, format it based on what the user asks for:
00:00 Introduction — host opens with the problem statement 03:45 Background — prior work and why existing solutions fall short 12:20 Core method — walkthrough of the proposed approach 24:10 Results — benchmark comparisons and key takeaways 31:55 Q&A — audience questions on scalability and next steps
1. **Fetch** the transcript using the helper script with `--text-only --timestamps` via `uv run python`. 2. **Validate**: confirm the output is non-empty and in the expected language. If empty, retry without `--language` to get any available transcript. If still empty, tell the user the video likely has transcripts disabled. 3. **Chunk if needed**: if the transcript exceeds ~50K characters, split into overlapping chunks (~40K with 2K overlap) and summarize each chunk before merging. 4. **Transform** into the requested output format. If the user did not specify a format, default to a summary. 5. **Verify**: re-read the transformed output to check for coherence, correct timestamps, and completeness before presenting.
Repo: Prismer-AI/PrismerCloud
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