Extract Youtube Transcript

extract-transcript Skill

When invoked, follow these steps in order. Do not skip steps.

Step 0 — Verify and install dependencies

Check each tool and install if missing. Stop and report clearly if any install fails.

youtube-transcript-api

pip install youtube-transcript-api

Always needed — used in Step 3 to check for an existing YouTube transcript.

yt-dlp

yt-dlp --version

If not found:

pip install yt-dlp

Whisper

whisper --help

If not found:

pip install openai-whisper

Whisper is only used if no YouTube transcript is found in Step 3. Install it regardless — it is fast and needed as a fallback.

ffmpeg

ffmpeg -version

If not found, install via winget (Windows 11):

winget install --id Gyan.FFmpeg -e

If winget fails, stop and tell the user:

"ffmpeg is required but could not be installed automatically. Please download it from https://ffmpeg.org/download.html, extract it, and add the bin/ folder to your system PATH. Then re-run this skill."

python-docx

pip install python-docx

Always needed — used in Step 6 to generate the summary .docx.

After any install, re-verify the tool is available before continuing. If it still fails, stop and report the error.

Step 1 — Get the YouTube URL

If the user already provided a URL in their message, use it. If not, ask:

"Please provide the YouTube URL you'd like to transcribe."

Step 2 — Create the base output folder

Create doc summaries output inside the current working directory if it does not already exist:

mkdir -p "doc summaries output"

The per-video subfolder will be created in Step 3 once the video title is known.

Step 3 — Resolve video title and check for a YouTube transcript

This step does two things in one script:

1. Fetches the video title from YouTube metadata (always needed for the subfolder name and filenames).
2. Checks whether YouTube already provides a transcript. If yes, saves it to disk and skips audio download entirely.

Write and run this Python script:

import re, os, subprocess, json
from youtube_transcript_api import YouTubeTranscriptApi
from youtube_transcript_api._errors import NoTranscriptFound, TranscriptsDisabled

url = "<URL>"
base_output = "doc summaries output"

# --- Fetch video title ---
meta = subprocess.run(
    ["yt-dlp", "--dump-json", "--skip-download", url],
    capture_output=True, text=True
)
raw_title = json.loads(meta.stdout)["title"]

# Sanitize title for use as a folder/file name (strip Windows-illegal characters)
illegal = r'\/:*?"<>|'
title = "".join(c for c in raw_title if c not in illegal).strip()

# --- Create per-video subfolder ---
video_folder = os.path.join(base_output, title)
os.makedirs(video_folder, exist_ok=True)

# --- Check for YouTube transcript ---
video_id = re.search(r"(?:v=|youtu\.be/)([^&?/]+)", url).group(1)
ytt = YouTubeTranscriptApi()

try:
    transcript_list = ytt.list(video_id)

    # Prefer manual English captions → auto-generated English → anything available
    transcript_obj = None
    source = None
    for t in transcript_list:
        if not t.is_generated and t.language_code.startswith("en"):
            transcript_obj = t
            source = "manual"
            break
    if transcript_obj is None:
        for t in transcript_list:
            if t.is_generated and t.language_code.startswith("en"):
                transcript_obj = t
                source = "auto-generated"
                break
    if transcript_obj is None:
        transcript_obj = next(iter(transcript_list))
        source = "auto-generated" if transcript_obj.is_generated else "manual"

    fetched = transcript_obj.fetch()
    text = " ".join(entry.text for entry in fetched)

    txt_path = os.path.join(video_folder, f"{title}.txt")
    with open(txt_path, "w", encoding="utf-8") as f:
        f.write(text)

    print(f"TRANSCRIPT_FOUND|{source}|{title}|{video_folder}|{txt_path}")

except (NoTranscriptFound, TranscriptsDisabled):
    print(f"NO_TRANSCRIPT|{title}|{video_folder}")
except Exception as e:
    print(f"NO_TRANSCRIPT|{title}|{video_folder}|error: {e}")

If the output starts with TRANSCRIPT_FOUND:

• Parse: source, title, video_folder, txt_path from the pipe-delimited output.
• Tell the user: "YouTube transcript found ({source}). Skipping audio download and Whisper."
• Skip Steps 4 and 5. Go directly to Step 6.

If the output starts with NO_TRANSCRIPT:

• Parse: title, video_folder from the output.
• Tell the user: "No YouTube transcript available — downloading audio and transcribing with Whisper."
• Continue to Step 4.

Step 4 — Download audio

(Only reached if Step 3 found no transcript.)

Download the MP3 directly into the video subfolder resolved in Step 3:

yt-dlp -x --audio-format mp3 "<URL>" -o "doc summaries output/<title>/%(title)s.%(ext)s"

Replace <title> with the sanitized title from Step 3. If yt-dlp reports an error (private video, age-restricted, unavailable), tell the user and stop.

Step 5 — Transcribe

(Only reached if Step 3 found no transcript.)

Find the .mp3 file inside doc summaries output/<title>/. Run Whisper on it, outputting the transcript into the same subfolder:

whisper "doc summaries output/<title>/<filename>.mp3" --output_format txt --output_dir "doc summaries output/<title>" --device cuda

If CUDA is unavailable (no NVIDIA GPU), retry with CPU:

whisper "doc summaries output/<title>/<filename>.mp3" --output_format txt --output_dir "doc summaries output/<title>" --device cpu

Let the user know transcription is running. GPU typically finishes in under a minute; CPU may take several minutes for longer videos.

After Whisper finishes, locate the .txt it created inside the subfolder and set txt_path to that file's full path for use in Step 6.

Step 6 — Generate summary .docx and .txt

Read the transcript from txt_path. Generate two summary files inside doc summaries output/<title>/ using a single Python script:

Both files must contain the same content in these sections:

1. Title — video title
2. Overview — 2-3 sentence summary of what the video is about
3. Detailed Summary — subsections matching the main topics covered in the video
4. Key Points — numbered list, each with a bold label (in the .docx) and a 1-2 sentence explanation
5. Main Takeaways — bulleted list of the most actionable/important conclusions
6. Tools or Concepts Referenced — 2-column table (Name | Description) of every tool, platform, or concept mentioned

Plain text formatting (.txt)

Use plain ASCII so the file is clean in any editor:

================================================
VIDEO TITLE HERE
================================================

OVERVIEW
--------
Paragraph text here.

DETAILED SUMMARY
----------------

  Topic Heading
  ~~~~~~~~~~~~~
  Paragraph text here.

KEY POINTS
----------
1. Bold Label — Explanation sentence here.
2. Bold Label — Explanation sentence here.

MAIN TAKEAWAYS
--------------
• Takeaway one
• Takeaway two

TOOLS AND CONCEPTS REFERENCED
------------------------------
Name                  | Description
----------------------|---------------------------------------------
Tool name             | Description here.

Save both files to doc summaries output/<title>/.

Step 7 — Report results

When complete, tell the user:

1. The full path of the video subfolder: <cwd>/doc summaries output/<title>/
2. List every file inside it:
   • <title>.mp3 — downloaded audio (only present if Whisper path was taken)
   • <title>.txt — raw transcript
   • <title> - Summary.txt — plain text summary (open in VS Code)
   • <title> - Summary.docx — formatted summary (upload to Google Drive)
3. Whether the transcript came from YouTube (manual or auto-generated) or was generated by Whisper.
4. Print the first ~300 characters of the raw transcript so the user can verify it looks correct.
5. Remind the user they can upload the .docx to Google Drive and it will open automatically as a Google Doc.

Error handling reference