JosiahSiegel/python-video-pipeline
plugins/python-master/skills/python-video-pipeline/SKILL.md
Expert guide to end-to-end Python video pipelines combining FFmpeg, OpenCV, PyAV, ffmpegcv, Decord, VidGear, and Modal.com for scalable GPU-accelerated workflows. PROACTIVELY activate for: (1) FFmpeg+OpenCV in same pipeline (decode, process, re-encode); (2) color-format mismatches (BGR vs RGB across OpenCV, PIL, PyAV, FFmpeg); (3) frame dim ordering (HWC vs CHW) between OpenCV and ML frameworks; (4) audio stream loss in filter chains; (5) memory mgmt for long/large videos (streaming vs in-memory); (6) choosing ffmpegcv/Decord/VidGear/PyAV for performance; (7) GPU decode/encode on Modal.com; (8) parallel + chunk-based processing on Modal; (9) transcoding pipelines on serverless; (10) HLS generation on Modal; (11) upload -> process -> transcode -> HLS workflows; (12) batch sizing for GPU memory, pixel formats, large-video streaming. Provides: library selection matrix, integration recipes, Modal.com deployment examples, GPU pipeline tuning, production-ready workflow examples.
$ install --global
skillsauthnpx skillsauth add JosiahSiegel/claude-plugin-marketplace python-video-pipelineInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- python-video-pipeline
- description:
- |
- PROACTIVELY activate for:
- (1) FFmpeg+OpenCV in same pipeline (decode, process, re-encode); (2) color-format mismatches (BGR vs RGB across OpenCV, PIL, PyAV, FFmpeg); (3) frame dim ordering (HWC vs CHW) between OpenCV and ML frameworks; (4) audio stream loss in filter chains; (5) memory mgmt for long/large videos (streaming vs in-memory); (6) choosing ffmpegcv/Decord/VidGear/PyAV for performance; (7) GPU decode/encode on Modal.com; (8) parallel + chunk-based processing on Modal; (9) transcoding pipelines on serverless; (10) HLS generation on Modal; (11) upload -> process -> transcode -> HLS workflows; (12) batch sizing for GPU memory, pixel formats, large-video streaming.
- Provides:
- library selection matrix, integration recipes, Modal.com deployment examples, GPU pipeline tuning, production-ready workflow examples.
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
- Start by selecting the pipeline architecture: simple OpenCV, FFmpeg plus OpenCV pipes, PyAV frame processing, ffmpegcv/Decord/VidGear, or Modal for scalable execution.
- Normalize color and shape conventions at every library boundary: OpenCV BGR/HWC, PIL RGB, PyAV RGB, FFmpeg pixel formats, and ML CHW tensors.
- Probe media metadata before processing so FPS, resolution, frame count, audio presence, and codec assumptions are explicit.
- Process long videos as streams, batches, or chunks; avoid accumulating all frames unless inputs are small and bounded.
- Re-mux or preserve audio after frame-level processing, since OpenCV-only workflows usually produce video-only outputs.
- On Modal or GPU infrastructure, tune batch size, pixel format, decode/encode acceleration, volume usage, and timeout boundaries.
Key Gotchas
- BGR/RGB mismatches silently produce wrong colors across OpenCV, FFmpeg, PyAV, PIL, and ML frameworks.
- Frame dimensions are usually HWC in NumPy/OpenCV but may need CHW for deep learning frameworks.
- OpenCV
VideoWriteroutput may not preserve the source audio; plan an explicit FFmpeg audio re-mux step. - Parallel frame processing must restore original frame order before reconstruction.
- Chunked processing needs timestamp and concat handling; audio is usually handled after chunk recombination.
Reference Map
- references/video-pipeline-complete-patterns.md - Full original pipeline guide covering library selection, integration gotchas, FFmpeg/OpenCV pipes, ffmpegcv, VidGear, Decord, Modal GPU workflows, chunking, transcoding, HLS, end-to-end workflows, and optimization tips.
- references/modal-video-patterns.md - Additional Modal-specific video patterns already maintained for this skill.
Response Guidance
- Preserve the user's existing framework, library, and tooling choices unless there is a clear compatibility or performance reason to suggest an alternative.
- Give copy-pasteable code only for the exact task at hand; otherwise point to the relevant reference section.
- Call out tradeoffs, failure modes, and verification steps for production workflows.
- Prefer accessible, maintainable, measurable solutions over clever micro-optimizations.
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