| name | instagram-reel-extractor |
| description | Extract transcript, metadata, music, and key frames from Instagram Reels. Use when the user shares an Instagram Reel URL (instagram.com/reel/, instagram.com/p/) and wants the script/transcript, video details, creator info, view counts, comments, music used, or any data from the reel. Also use when the user wants to analyze, repurpose, or reference an Instagram video's content. |
| compatibility | Requires Python 3.10+, ffmpeg, yt-dlp, openai-whisper, and ShazamAPI. macOS recommended (uses Homebrew for setup). |
Instagram Reel Extractor
Extract the spoken transcript, metadata, music, comments, and key frames from an Instagram Reel.
Prerequisites
The following must be installed on the user's machine:
- yt-dlp —
brew install yt-dlp
- ffmpeg —
brew install ffmpeg
- Python packages —
pip3 install openai-whisper ShazamAPI
Or run the setup script: bash setup.sh
If any dependency is missing, tell the user which ones to install before proceeding.
Usage
Run the extraction script with the Reel URL:
python3 scripts/extract_reel.py "REEL_URL"
Save the extraction to a specific directory:
python3 scripts/extract_reel.py "REEL_URL" --save-dir ~/notes/reels
For better transcription accuracy (slower, uses more memory):
python3 scripts/extract_reel.py "REEL_URL" --whisper-model small
For raw JSON output:
python3 scripts/extract_reel.py "REEL_URL" --json
Skip frame extraction (faster, audio/metadata only):
python3 scripts/extract_reel.py "REEL_URL" --no-frames
Adjust frame interval (default every 2 seconds):
python3 scripts/extract_reel.py "REEL_URL" --frame-interval 1.0
What Gets Extracted
- Original URL — link back to the source reel (always included)
- Creator — username and handle
- Metrics — likes, comments count (as of extraction date)
- Music — song title, artist, album, genre, Shazam link (via audio fingerprinting)
- Caption — the original post caption
- Hashtags — all tags from the post
- Top Comments — comment text with author and like count
- Duration — video length
- Upload date
- Transcript — full spoken text from the audio, with timestamps
- Language — auto-detected language of the speech
- Key frames — screenshots extracted every N seconds, saved as JPGs with timestamps
Output
The script outputs a structured markdown summary with the original reel URL at the top. Present this to the user as-is — do not modify the transcript text itself, though you can clean up line breaks and paragraph flow for readability.
The frame images are saved to a frames/ subdirectory inside the working directory. When presenting the extraction, use the Read tool to view the frame images so you can describe what's happening visually at each timestamp (talking head, B-roll, text overlay, product shot, etc.).
When --save-dir is provided, the extraction is automatically saved as <creator>-reel-<upload_date>.md. If a file already exists, a number is appended (e.g., garyvee-reel-2026-04-08-2.md).
Configuring a Default Save Location
To always save extractions to a specific folder, tell Claude: "save reel extractions to ~/my/folder". Claude will pass --save-dir automatically on future runs.
Whisper Model Sizes
| Model | Speed | Accuracy | Memory |
|---|
| tiny | Fastest | Lower | ~1 GB |
| base | Fast | Good | ~1 GB |
| small | Moderate | Better | ~2 GB |
| medium | Slow | Great | ~5 GB |
| large | Slowest | Best | ~10 GB |
Default is base — a good balance for short-form reel content. Suggest small if the user reports transcription quality issues.
Troubleshooting
- Login required errors: Some reels may require authentication. Pass
--cookies-from chrome (or firefox) to use browser cookies.
- No speech detected: The reel may be music-only or use on-screen text instead of speech. Let the user know.
- Slow transcription: Whisper runs on CPU by default. On Apple Silicon Macs, it uses the Neural Engine automatically. For faster runs, suggest the
tiny model.
- Music not identified: Shazam works best with distinct background music. Speech-heavy reels with no music will return no match.
