Video Context

video-context

Claude reads images, not videos. This skill turns any video into scene-change frames + audio transcript so Claude can reason about it.

Standalone — works on any source: local file, public URL, or private URL with auth.

When to use

User shares a video and wants Claude to understand its content. Examples:

• "Analyze this video: "
• "What does this recording show? /path/to/file.mp4"
• "The bug is in this Loom: https://loom.com/share/..."
• Bug ticket (Jira/Asana/GitHub/Linear) contains a video attachment.

How to use

${CLAUDE_PLUGIN_ROOT}/skills/video-context/extract.sh 

Script auto-resolves the source:

| Source | How it's fetched | |---|---| | Local file path | Used directly. | | Public URL (Loom, YouTube, Vimeo, public CDN, raw mp4) | yt-dlp first, falls back to curl. | | Private URL needing auth | curl with VIDEO_AUTH_HEADER env var. |

Private URL with auth

Set VIDEO_AUTH_HEADER to whatever the host needs:

# Bearer token
VIDEO_AUTH_HEADER="Authorization: Bearer $TOKEN" \
  ${CLAUDE_PLUGIN_ROOT}/skills/video-context/extract.sh "https://host.example/asset/123"

# Basic auth (e.g. Atlassian email + API token)
VIDEO_AUTH_HEADER="Authorization: Basic $(echo -n "$EMAIL:$TOKEN" | base64)" \
  ${CLAUDE_PLUGIN_ROOT}/skills/video-context/extract.sh "https://your-org.atlassian.net/rest/api/3/attachment/content/12345"

If user shares a private URL without a token, ask for one — or tell them to download the video and paste the local path.

After it runs

Script prints WORKDIR, frame paths, transcript path. Then:

1. Read each frame — Claude sees JPEGs natively.
2. Read the transcript at $WORKDIR/transcript.txt.
3. Combine frames + transcript to answer the user.

Tuning (env vars)

| Var | Default | What | |---|---|---| | SCENE_THRESHOLD | 0.4 | Lower = more frames. Screen recordings often need 0.2. | | MAX_FRAMES | 20 | Hard cap; trimmed evenly across timeline. | | FRAME_WIDTH | 1280 | Downscale to save tokens. | | WHISPER_MODEL | /opt/homebrew/share/whisper-cpp/ggml-base.en.bin | whisper-cpp model path. |

Requirements

• ffmpeg (required)
• yt-dlp (recommended — handles Loom/YouTube/Vimeo)
• whisper-cpp + model (optional — for audio transcript)

Run /video-context:setup once for full install on macOS or Linux (apt/dnf/pacman/zypper; builds whisper-cpp from source). On macOS, extract.sh also auto-installs missing binaries via brew on first run; the whisper model (~150MB) is only fetched by setup.

Windows: install manually.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: vusallyv
• Source: vusallyv/video-context-plugin
• License: MIT

Install and usage instructions live in the source repository linked above.