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Claude Real Video

Locally extracts scene-aware video keyframes, transcripts, manifests, and optional audio for LLM analysis.

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Install Claude Real Video

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "claude-real-video": {
      "command": "uvx",
      "args": [
        "claude-real-video"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Install not yet sandbox-tested— we haven't run this command through our automated sandbox yet. Not a red flag; we're working through the catalog.

Overview

The claude-real-video MCP server project provides a local Python tool for preparing videos for LLM review. It accepts URLs or local files, detects scene changes, removes near-duplicate frames, and creates a timestamped transcript from subtitles or Whisper. Outputs include keyframes, a manifest, optional contact sheets, and optionally the original soundtrack. Use it when an AI model needs visual video context without uploading the source video for processing.

Use cases

  • Extract meaningful frames from fast-cut videos for LLM review
  • Transcribe lectures or interviews with timestamps
  • Prepare YouTube or social video context locally
  • Save video findings as dated notes in a knowledge base

Key features

  • Scene-change keyframe extraction
  • Sliding-window pixel deduplication
  • Subtitle or Whisper transcription
  • Contact sheet and manifest generation
  • Optional soundtrack preservation
  • Python API and command-line interface

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

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Scanned 6d ago via OSV.dev · claude-real-video (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars2.3kStargazers on the source repository.
Last commit18d agoMost recent push to the default branch.
Directory activity9 viewsConfig copies, upvotes, and views on AllMCPs.

How Claude Real Video works

What claude-real-video does

The claude-real-video MCP server project prepares video material for inspection by Claude, ChatGPT, Gemini, or another model. It does not perform the final LLM analysis itself. Instead, it creates a folder containing selected frames, transcript text, and a manifest that explains the extracted material.

Inputs can be local video files or URLs supported through yt-dlp, including links that require an authorised Netscape cookie file. The tool can also save a full copy of the soundtrack for models that support audio input. A --why prompt records the intended analysis focus in the manifest, while --kb writes the result as a dated Markdown note in a chosen notes directory.

How it works

Video processing is performed locally. URLs are fetched with yt-dlp; local files are copied for processing. ffmpeg then makes one chronological pass that selects scene changes and maintains a frame-density floor. A configurable pixel-difference comparison removes near-duplicates, using a sliding window so that a previously seen shot is not repeatedly emitted after a cutaway.

For text, the tool first checks for an adjacent .srt or .vtt file or an embedded subtitle track. If no subtitles are available, it can use Whisper to transcribe the audio, provided the optional transcription dependencies are installed. Videos without audio are skipped cleanly. The generated MANIFEST.txt brings the frames and text together for downstream model use.

Setup and configuration

Install the core package with pip install claude-real-video. Add the whisper extra when audio transcription is needed. Python 3.10 or newer is required, and ffmpeg plus ffprobe must be installed separately and available on PATH. The project supports macOS, Windows, and Linux.

Run crv with a URL or filename. python -m claude_real_video is an alternative entry point. Important options include --out for the output directory, --scene for scene sensitivity, --fps-floor for minimum sampling density, --max-frames for the frame limit, --lang for Whisper language selection, and --dedup-threshold and --dedup-window for duplicate removal. --no-transcribe, --keep-audio, --report, --why, --kb, and --cookies enable the corresponding optional behaviors.

The claude-real-video MCP server can also be installed as a Claude Code skill by copying the included skill directory into ~/.claude/skills/.

Tools and capabilities

  • Extracts keyframes at scene changes instead of relying only on fixed intervals.
  • Applies pixel-based deduplication and supports a hard frame cap.
  • Produces chronological frames, transcript text, and a manifest.
  • Uses existing subtitles before falling back to Whisper transcription.
  • Creates contact sheets with ordered frames for model review.
  • Saves the original soundtrack with --keep-audio.
  • Generates an HTML keep/drop report with --report.
  • Exposes a Python process function for programmatic use.

Limitations and notes

The project does not itself send video, frames, or transcripts to an LLM; users choose what to provide to another model. Whisper transcription requires the optional dependency and still relies on ffmpeg. URL access depends on yt-dlp support and, for login-gated sources, a user-supplied authorised cookie file. Re-running the command overwrites the output directory. The included instructions also warn users to download only content they are permitted to access.

Read the full README View source on GitHub

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Frequently Asked Questions about Claude Real Video

Install the Python package with `pip install claude-real-video`. Add `pip install "claude-real-video[whisper]"` for Whisper transcription, and install `ffmpeg` separately.

Yes. The documented frame extraction and transcription workflow runs on the user's machine; users decide what generated frames or text to share with an LLM.

Yes. It accepts URLs through `yt-dlp`, and the `--cookies` option supports authorised access to login-gated sources.

It uses existing subtitle tracks when available and can fall back to Whisper when subtitles are absent. Use `--no-transcribe` to skip this step.

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