instagram-reel-extractor

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.