Python Video Pipeline Skill

Use this skill for end-to-end Python video pipelines that combine decoding, OpenCV/PyAV/frame processing, FFmpeg encoding, serverless execution, GPU acceleration, HLS output, and large-file orchestration.

When to Use This Skill

Use when the user asks for tasks covered by the frontmatter triggers, especially implementation guidance, debugging, architecture choices, production hardening, or performance-sensitive decisions in this domain. Start from this orchestrator, then load the focused reference file that matches the requested detail level.

Core Workflow

1. Start by selecting the pipeline architecture: simple OpenCV, FFmpeg plus OpenCV pipes, PyAV frame processing, ffmpegcv/Decord/VidGear, or Modal for scalable execution.
2. Normalize color and shape conventions at every library boundary: OpenCV BGR/HWC, PIL RGB, PyAV RGB, FFmpeg pixel formats, and ML CHW tensors.
3. Probe media metadata before processing so FPS, resolution, frame count, audio presence, and codec assumptions are explicit.
4. Process long videos as streams, batches, or chunks; avoid accumulating all frames unless inputs are small and bounded.
5. Re-mux or preserve audio after frame-level processing, since OpenCV-only workflows usually produce video-only outputs.
6. On Modal or GPU infrastructure, tune batch size, pixel format, decode/encode acceleration, volume usage, and timeout boundaries.

Key Gotchas