plugins/ffmpeg-python/skills/ffmpeg-python-integration-reference/SKILL.md
Authoritative Python-FFmpeg parameter integration reference ensuring type safety, accurate parameter mappings, and proper unit conversions. PROACTIVELY activate for: (1) ffmpeg-python library usage, (2) Python subprocess FFmpeg calls, (3) Caption/subtitle parameter mapping (drawtext, ASS), (4) Color format conversions (BGR, RGB, ABGR, ASS &HAABBGGRR), (5) Time unit conversions (seconds, centiseconds, milliseconds), (6) Type safety validation (int, float, string), (7) Coordinate systems, (8) Parameter range enforcement, (9) Frame pipe handling, (10) Error detection for type mismatches. Provides: Complete parameter type reference, color format conversion tables, time unit conversion formulas, validation patterns, working Python examples with proper typing.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace ffmpeg-python-integration-referenceInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when Python code is constructing FFmpeg commands, filters, ASS subtitles, or raw-frame pipes and the risk is a type, unit, color, or stream-mapping bug. This SKILL is a lean orchestrator; detailed tables and full code examples live in references/python-ffmpeg-reference.md.
ffmpeg-python stream graphs and filter argumentssubprocess calls to FFmpegdrawtext, ASS/SSA, karaoke, and animated caption parameter mappingfontsize and crf are integers; bitrates are strings with units such as 5M or 192k; FFmpeg expressions are strings.x="(w-tw)/2", not Python variables named w or tw.ffmpeg-python, a video filter chain usually drops audio unless you map input_file.audio into the output.&HAABBGGRR.| FFmpeg / ASS parameter | Python type | Format / range | Common failure |
|---|---:|---|---|
| -crf | int or str | H.264/H.265 0-51 | passing 18.5 |
| -b:v, -b:a | str | 5M, 1000k, 192k | raw integer without unit |
| fontsize | int | practical 12-200 | passing '24' |
| fontcolor | str | white, #FFFFFF, 0xFFFFFF | RGB tuple/list |
| ASS colour | str | &HAABBGGRR | using RGB byte order |
| x, y, alpha, enable | str for expressions | '(w-tw)/2, between(t,1,5) | unquoted expression |
ffmpeg-python for command graphs, subprocess for full CLI parity and pipes, PyAV for frame-level library access.references/python-ffmpeg-reference.md - Full preserved reference: color conversion functions, ASS style structures, karaoke/animation helpers, drawtext parameter tables, ffmpeg-python examples, subprocess pipe patterns, pitfalls, validation helpers, full working examples.ffmpeg-opencv-integration - OpenCV/NumPy frame pipelines and BGR/RGB handoffffmpeg-pyav-integration - PyAV frame-level API patternsffmpeg-captions-subtitles - Subtitle extraction, burn-in, and stylingffmpeg-animation-timing-reference - Timing units, readability, easing, and syncdevelopment
This skill should be used when the user asks to train, debug, scale, or improve ML models. PROACTIVELY activate for: (1) PyTorch, TensorFlow/Keras, JAX, Flax, Hugging Face Trainer/Accelerate training loops, (2) distributed training, DDP/FSDP/DeepSpeed, TPU/GPU setup, (3) mixed precision AMP/bf16, gradient accumulation, checkpointing, seeding, (4) overfitting, imbalance, loss functions, regularization, LR schedules, warmup, (5) memory optimization, gradient checkpointing, offloading, quantization-aware training. Provides: reproducible training best practices across deep learning and classical ML.
development
This skill should be used when the user asks to productionize, track, version, govern, monitor, or automate ML systems. PROACTIVELY activate for: (1) MLflow, Weights & Biases, Neptune, Comet, ClearML experiment tracking, (2) model registry, model versioning, artifact lineage, reproducibility, (3) Kubeflow, SageMaker Pipelines, Vertex AI Pipelines, Azure ML pipelines, Databricks workflows, (4) CI/CD, continuous training/evaluation, A/B tests, canary/shadow deployments, (5) drift detection, model monitoring, data validation, responsible AI governance. Provides: end-to-end MLOps architecture and operational safeguards.
development
This skill should be used when the user asks to optimize, export, serve, compress, or accelerate ML inference. PROACTIVELY activate for: (1) latency, throughput, p95/p99, batching, concurrency, KV cache, memory, or cost issues, (2) quantization INT8/INT4, GPTQ, AWQ, bitsandbytes, pruning, sparsity, distillation, (3) ONNX export, ONNX Runtime, TensorRT, TorchScript, torch.compile, XLA, OpenVINO, Core ML, TFLite, (4) Triton, TorchServe, TF Serving, BentoML, Seldon, KServe configuration, (5) edge deployment, CPU/GPU/TPU/Inferentia serving. Provides: hardware-aware inference optimization and safe benchmarking.
testing
This skill should be used when the user asks to tune hyperparameters, run sweeps, optimize search spaces, or use AutoML. PROACTIVELY activate for: (1) Optuna, Ray Tune, FLAML, AutoGluon, Hyperopt, Nevergrad, KerasTuner, W&B sweeps, (2) grid search, random search, Bayesian optimization, TPE, Gaussian processes, evolutionary search, (3) ASHA, Hyperband, successive halving, multi-fidelity optimization, population-based training, (4) learning-rate finder, batch-size search, early stopping, pruning, (5) reproducible sweep design and experiment analysis. Provides: budget-aware hyperparameter search strategy.