youtube-content
Skill546 starsChanged 6 months agoMITView on GitHub
Fetch YouTube video transcripts and transform them into structured content (chapters, summaries, threads, blog posts). 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.
- Installs packages
We split this skill into the rules it gives an agent
3 rules. 2 are common, 0 are distinctive, and 1 is very specific to this project.
2
Common
Most of the 22 edited versions we read keep these.
0
Distinctive
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Common: line 40
After fetching the transcript, format it based on what the user asks for:
- Common: line 63
Chunk if needed: if the transcript exceeds ~50K characters, split into overlapping chunks (~40K with 2K overlap) and summarize each chunk …
- Very specific: line 72
Dependency missing: run pip install youtube-transcript-api and retry.
“Common” is measured: kept by at least 6 in 10 of the 22 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.
Files
YouTube Content Tool
Extract transcripts from YouTube videos and convert them into useful formats.
Setup
pip install youtube-transcript-apiHelper Script
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
python3 SKILL_DIR/scripts/fetch_transcript.py "https://youtube.com/watch?v=VIDEO_ID"
# Plain text (good for piping into further processing)
python3 SKILL_DIR/scripts/fetch_transcript.py "URL" --text-only
# With timestamps
python3 SKILL_DIR/scripts/fetch_transcript.py "URL" --timestamps
# Specific language with fallback chain
python3 SKILL_DIR/scripts/fetch_transcript.py "URL" --language tr,enOutput Formats
After fetching the transcript, format it based on what the user asks for:
- Chapters: Group by topic shifts, output timestamped chapter list
- Summary: Concise 5-10 sentence overview of the entire video
- Chapter summaries: Chapters with a short paragraph summary for each
- Thread: Twitter/X thread format — numbered posts, each under 280 chars
- Blog post: Full article with title, sections, and key takeaways
- Quotes: Notable quotes with timestamps
Example — Chapters Output
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 stepsWorkflow
- Fetch the transcript using the helper script with
--text-only --timestamps. - Validate: confirm the output is non-empty and in the expected language. If empty, retry without
--languageto get any available transcript. If still empty, tell the user the video likely has transcripts disabled. - Chunk if needed: if the transcript exceeds ~50K characters, split into overlapping chunks (~40K with 2K overlap) and summarize each chunk before merging.
- Transform into the requested output format. If the user did not specify a format, default to a summary.
- Verify: re-read the transformed output to check for coherence, correct timestamps, and completeness before presenting.
Error Handling
- Transcript disabled: tell the user; suggest they check if subtitles are available on the video page.
- Private/unavailable video: relay the error and ask the user to verify the URL.
- No matching language: retry without
--languageto fetch any available transcript, then note the actual language to the user. - Dependency missing: run
pip install youtube-transcript-apiand retry.
Other versions23 projects use a version of this.
Word for word · 1
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Edited · 22
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12 more not listed
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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