vidiq_skill

This skill helps you download, analyze, and extract clips or GIFs from any video URL using ffmpeg and yt-dlp.
  • Python

2.6k

GitHub Stars

2

Bundled Files

2 months ago

Catalog Refreshed

3 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill openclaw/skills --skill vidiq

  • _meta.json286 B
  • SKILL.md1.6 KB

Overview

This skill provides AI-powered video intelligence to download, analyze, and manipulate any video from a URL. It supports YouTube, TikTok, Instagram, X (Twitter), direct video links and local files, combining yt-dlp and ffmpeg for robust media handling. Use it to extract frames, create clips or GIFs, detect scene changes, and run vision-model analysis for content insights. Downloaded media and intermediate outputs are cached for fast reuse.

How this skill works

The tool downloads the source video (or reads a local file) and caches it to avoid repeated downloads. It uses ffmpeg to extract frames, audio, clips, GIFs, mosaics, and to detect scene changes. Extracted frames can be fed into a vision model to generate descriptions, answer questions about visual content, or produce summaries. Commands are script-driven so you can automate batching and integrate the outputs into downstream analysis pipelines.

When to use it

  • When you need quick visual summaries or thumbnails from a remote video URL.
  • To clip specific segments or produce short GIFs for previews or social sharing.
  • When performing automated content analysis with a vision model for moderation or indexing.
  • To extract audio for transcription or podcasting workflows.
  • When archiving or backing up videos from multiple social platforms for research.

Best practices

  • Extract more frames for long videos to get better coverage for AI analysis.
  • Use scene detection with a threshold tuned to your content (e.g., 0.3) to identify logical cuts.
  • Cache and reuse downloaded video files to save bandwidth and speed up repeated analyses.
  • Generate GIF palettes via ffmpeg for optimized small-file animated previews.
  • Keep timestamps in HH:MM:SS format when specifying frames or clips for consistency.

Example use cases

  • Create a 30-second highlight clip from a long live stream by specifying start and end timestamps.
  • Extract 10 evenly spaced frames from a product demo and run a vision model to produce descriptive captions.
  • Detect scene boundaries in a vlog to automate chapter markers for publishing.
  • Produce a compact GIF preview of a key moment for social sharing while keeping file size small.
  • Pull audio from interviews as MP3 for transcription and republishing as a podcast episode.

FAQ

YouTube, TikTok, Instagram, X/Twitter, direct video URLs and local video files are supported via yt-dlp and ffmpeg.

Where are downloads and frames stored?

Downloads and intermediate outputs are cached under /tmp/vidiq/ and frames under /tmp/vidiq/frames_*/ for reuse.

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