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Claude Video

Claude Video is a self-contained agent skill for answering questions about video URLs or local video files with both visual frames and a timestamped transcript.

The skill checks for captions first, downloads video or audio when needed, uses ffmpeg to select frames, and can fall back to a Groq or OpenAI Whisper API when a video has no captions. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.

What it is

A video-input skill for agents

Claude Video packages a SKILL.md file and Python scripts that use yt-dlp, ffmpeg, captions, and optional Whisper transcription to turn a video into material an image-capable coding agent can read.

Why it stands out

Frames and words stay on one timeline

The output pairs timestamped transcript segments with selected frames. Detail modes, focused time ranges, forced timestamps, and frame deduplication help control how much visual context reaches the agent.

Availability

Public skill with several install paths

The repository documents Claude Code marketplace installation, a downloadable skill for claude.ai, and Agent Skills installation for Codex, Cursor, Copilot, Gemini CLI, and other hosts documented by the project.

Why it matters

What makes it useful

A transcript can explain what was said while missing a broken interface, a chart change, a visual joke, or the exact moment a demonstration stops working. Claude Video gives an agent both streams, which makes screen-recording diagnosis, visual walkthroughs, and questions about a specific moment more practical.

Notable points

What stands out

The project is young: its changelog begins with version 0.1.0 in April 2026 and lists version 0.2.0 in June 2026. The repository includes a pytest suite and documents fixes for Windows, non-Claude skill installation, subprocess argument handling, frame selection, and long-audio transcription.

Before using

What to review

Whether the agent can run Python and shell commands, and whether yt-dlp plus ffmpeg are installed or acceptable to add.

Whether the video source may be downloaded, and whether its platform rules and the rights attached to the recording permit the intended use.

Whether native captions are enough. When they are missing, the optional Whisper fallback sends extracted audio to Groq or OpenAI unless it is disabled.

How much context the selected detail mode may consume. The project warns that capped frame modes become sparse on videos longer than about 10 minutes and recommends focused ranges when possible.

Where temporary frames, audio, captions, and transcripts are created, what the agent can read, and when those working files should be removed.

Reader fit

Who may find it relevant

Claude, Codex, Cursor, Copilot, or Gemini CLI users who want an agent to inspect video evidence as part of a practical task.

Builders reviewing screen recordings, tutorials, presentations, product demonstrations, or time-specific visual details.

Less relevant for readers who want a finished video player, editor, generative video model, or fully local transcription stack with no optional provider calls.

Editorial note

Why LifeHubber lists it

LifeHubber lists Claude Video because it turns a common agent blind spot into a concrete, adjustable workflow: captions when text is enough, frames when the picture matters, focused time ranges when a full scan would waste context, and an explicit choice about whether missing captions should trigger a cloud transcription service.

Source links

Source materials

Reader note

Before relying on this entry

LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.

What to explore next

Choose the next layer around the video.

Claude Video handles a question about one recording. Continue with a wider source-access layer, a realtime video-language model, or a local transcription utility when the workflow needs a different boundary.

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