processing-media_skill

This skill analyzes and edits video using ffmpeg, translating natural language commands into edits while ensuring quality.
  • Python

1

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 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 git-fg/thecattoolkit --skill processing-media

  • SKILL.md1.5 KB

Overview

This skill handles video editing, ffmpeg-based processing, and visual analysis to turn raw footage into polished deliverables. It encodes editing intents into reproducible workflows and enforces quality standards for color, motion, and audio. Use it to automate conversions, generate edit decision lists, and validate final outputs programmatically.

How this skill works

The skill inspects input media to extract specs (codec, frame rate, resolution) and performs frame-level visual analysis for shot detection, stabilization needs, and color grading cues. Natural language edit requests are translated into parameterized EDLs and ffmpeg command sequences which are executed to render edits. Outputs are verified with automated checks for artifacts, audio balance, and expected metadata.

When to use it

  • Converting multi-rate footage to a single target frame rate and codec
  • Applying style-driven color grading and stabilization across clips
  • Automating repetitive edits from a written edit plan or storyboard
  • Generating low-resolution proxies and final deliverables
  • Running automated visual QA to detect artifacts or incorrect renders

Best practices

  • Analyze source specs and create proxies before heavy processing
  • Translate commands into explicit EDLs to keep edits reproducible
  • Prefer lossless or high-bitrate intermediates for color work
  • Run self-verification checks for audio levels, frame integrity, and metadata
  • Keep style presets (cinematic, vlog, corporate) as parameter templates

Example use cases

  • Convert 60fps action footage to 24fps slow-motion with motion blur and stabilization
  • Batch grade a set of corporate training videos to a neutral branded palette while preserving dialogue clarity
  • Produce social clips with fast cuts, audio ducking, and bright, saturated color from long-form footage
  • Generate an EDL from a director’s text notes and render the timeline with ffmpeg commands
  • Detect dropped frames or compression artifacts in a delivered master and flag failures

FAQ

The skill reads common codecs and containers and normalizes varied frame rates into target timelines; uncommon formats may require pre-transcoding.

How does verification work?

Verification runs programmatic checks: artifact detection, audio loudness and ducking validation, frame-count consistency, and metadata comparison against expected values.

Can I customize style presets?

Yes. Styles are exposed as parameter templates (color curves, LUTs, stabilization settings, cut pacing) that you can modify and reuse.

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