How do I install the Youtube Transcribe Transcribe Youtube skill?

Run npx skillmds@latest add connect0459/youtube-transcribe-transcribe-youtube in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Youtube Transcribe Transcribe Youtube safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Youtube Transcribe Transcribe Youtube?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Youtube Transcribe Transcribe Youtube free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Youtube Transcribe Transcribe Youtube?

connect0459 (@connect0459) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Youtube Transcribe Transcribe Youtube by connect0459

Name: Youtube Transcribe Transcribe Youtube
Author: connect0459

Youtube Transcribe Transcribe Youtube

End-to-end YouTube → SRT + Markdown workflow. Downloads audio via yt-dlp, transcribes with whisper-transcribe via ffmpeg, and converts the SRT to a structured Markdown article. Use whenever the user wants to transcribe a YouTube video, get a transcript of a YouTube talk, convert YouTube speech to text, or produce a readable article from a YouTube video — including phrases like "transcribe this YouTube video", "get the transcript of this talk", "YouTubeの文字起こし", "YouTubeの動画を書き起こして", "make an article from this video", or any time the user shares a YouTube URL and asks for text, transcript, or summary. Requires /connect0459-agent-skills:whisper-transcribe-setup to have been run first.

connect0459 Updated Sep 17, 2026

File contents

youtube-transcribe-transcribe-youtube

Full YouTube → SRT → Markdown pipeline, running in a forked context so large transcript artifacts stay out of the calling session's prompt cache.

Script

<skill-path>/scripts/transcribe-youtube <youtube-url> [--language CODE] [--model NAME]

The script handles audio download and SRT generation. You (Claude) handle the SRT → Markdown conversion and offer translation.

Workflow

Steps 1–2: run the script (shell)

<skill-path>/scripts/transcribe-youtube <url> [--language CODE] [--model NAME]

The script:

Downloads audio from the YouTube URL via yt-dlp --extract-audio to ~/Downloads.
Runs local-transcribe (ffmpeg + whisper-transcribe) on the audio → .srt file.
Prints audio: <path> and srt: <path> so you know the artifact locations.

Step 3: SRT → Markdown (you do this, in this forked context)

Read the .srt file with the Read tool. Convert it to a structured Markdown article:

Title (H1): infer from the video title embedded in the filename Title [id].ext, or ask.
Introduction: 1–2 sentences summarising what the talk is about.
Body: convert each SRT segment to readable prose, grouped into logical sections with H2 headings.
- Remove filler words ("um", "uh", etc.).
- Fix obvious transcription errors where the context makes the correction clear.
- Preserve technical terms, names, and code verbatim.
- Do not add information the speaker did not say.
Conclusion: 2–3 sentences on the key takeaways.

Output file: same stem as the .srt, with .md extension — e.g.: ~/Downloads/Talk Title [abc123].srt → ~/Downloads/Talk Title [abc123].md

Write the Markdown with the Write tool. Then print wrote <md-path>.

Step 4: offer translation (do not auto-run)

After writing the Markdown, offer:

Translation available: run /connect0459-agent-skills:youtube-transcribe-translate <md-path> --to ja for a Japanese version, or --to en for English.

Do not start the translation automatically — the user decides.

Output

Both files are durable artifacts on disk:

.srt — canonical subtitle artifact, timestamped, downgradeable to plain text
.md — readable article form

Running inside the Claude Code sandbox

whisper-transcribe auto-detects the sandbox via SANDBOX_RUNTIME=1 and falls back to CPU mode. CPU transcription is 5–10× slower than Metal; a 30-minute talk takes ~5–10 minutes. The fork handles this blocking wait — the parent session is unblocked while the fork runs.

---name: youtube-transcribe-transcribe-youtubedescription: End-to-end YouTube → SRT + Markdown workflow. Downloads audio via yt-dlp, transcribes with whisper-transcribe via ffmpeg, and converts the SRT to a structured Markdown article. Use whenever the user wants to transcribe a YouTube video, get a transcript of a YouTube talk, convert YouTube speech to text, or produce a readable article from a YouTube video — including phrases like "transcribe this YouTube video", "get the transcript of this talk", "YouTubeの文字起こし", "YouTubeの動画を書き起こして", "make an article from this video", or any time the user shares a YouTube URL and asks for text, transcript, or summary. Requires /connect0459-agent-skills:whisper-transcribe-setup to have been run first.---# youtube-transcribe-transcribe-youtubeFull YouTube → SRT → Markdown pipeline, running in a forked context so large transcript artifacts stay out of the calling session's prompt cache.## Script```bash<skill-path>/scripts/transcribe-youtube <youtube-url> [--language CODE] [--model NAME]```The script handles audio download and SRT generation. You (Claude) handle the SRT → Markdown conversion and offer translation.## Workflow### Steps 1–2: run the script (shell)```bash<skill-path>/scripts/transcribe-youtube <url> [--language CODE] [--model NAME]```The script:1. Downloads audio from the YouTube URL via `yt-dlp --extract-audio` to `~/Downloads`.2. Runs `local-transcribe` (ffmpeg + whisper-transcribe) on the audio → `.srt` file.3. Prints `audio: <path>` and `srt: <path>` so you know the artifact locations.### Step 3: SRT → Markdown (you do this, in this forked context)Read the `.srt` file with the Read tool. Convert it to a structured Markdown article:- **Title** (H1): infer from the video title embedded in the filename `Title [id].ext`, or ask.- **Introduction**: 1–2 sentences summarising what the talk is about.- **Body**: convert each SRT segment to readable prose, grouped into logical sections with H2 headings. - Remove filler words ("um", "uh", etc.). - Fix obvious transcription errors where the context makes the correction clear. - Preserve technical terms, names, and code verbatim. - Do not add information the speaker did not say.- **Conclusion**: 2–3 sentences on the key takeaways.Output file: same stem as the `.srt`, with `.md` extension — e.g.:`~/Downloads/Talk Title [abc123].srt` → `~/Downloads/Talk Title [abc123].md`Write the Markdown with the Write tool. Then print `wrote <md-path>`.### Step 4: offer translation (do not auto-run)After writing the Markdown, offer:> Translation available: run `/connect0459-agent-skills:youtube-transcribe-translate <md-path> --to ja` for a Japanese version, or `--to en` for English.Do not start the translation automatically — the user decides.## OutputBoth files are durable artifacts on disk:- `.srt` — canonical subtitle artifact, timestamped, downgradeable to plain text- `.md` — readable article form## Running inside the Claude Code sandbox`whisper-transcribe` auto-detects the sandbox via `SANDBOX_RUNTIME=1` and falls back to CPU mode.CPU transcription is 5–10× slower than Metal; a 30-minute talk takes ~5–10 minutes. The fork handles this blocking wait — the parent session is unblocked while the fork runs.

Frequently asked questions