plugins/ffmpeg-social-video/skills/viral-video-animated-captions/SKILL.md
CapCut-style animated word-level captions for viral video with FFmpeg. PROACTIVELY activate for: (1) Word-by-word caption highlighting, (2) Animated subtitle effects, (3) CapCut-style captions, (4) Karaoke-style text, (5) Bounce/pop text animations, (6) Color-changing words, (7) Emoji integration in captions, (8) Multi-style caption presets, (9) Trending caption styles, (10) Social media caption optimization. Provides: ASS subtitle generation scripts, word-level timing workflows, animation presets, color schemes, font recommendations, and platform-specific caption styles for TikTok, YouTube Shorts, and Instagram Reels.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace viral-video-animated-captionsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill to create burned-in social captions with word-level timing, pop/bounce/scale animations, karaoke highlighting, and platform-appropriate caption styles. This SKILL is a lean orchestrator; detailed ASS styles, scripts, formulas, and presets are preserved in references/caption-styles-and-generation.md.
ffmpeg -i input.mp4 \
-vf "ass=captions.ass" \
-c:v libx264 -preset fast -crf 23 \
-c:a copy \
output_captioned.mp4
If audio copy fails because the output container cannot accept the source audio, re-encode audio with -c:a aac -b:a 128k.
| Style | Effect | Best for | |---|---|---| | Word Pop | Each word scales in with overshoot | High-energy hooks, gaming, comedy | | Highlight Sweep | Full phrase visible, words highlight in sequence | Educational and professional explainers | | Karaoke Fill | Progressive fill across words | Music, voiceover, lyric-style captions | | Typewriter | Characters appear progressively | Dramatic storytelling | | Bounce In | Words move/scale from below | Shorts/Reels emphasis beats |
references/caption-styles-and-generation.md - Full preserved reference: Whisper timestamp commands, ASS structure, WordPop/Sweep/Karaoke/Typewriter/Bounce examples, Python JSON-to-ASS script, bash pipeline, burn commands, style presets, color schemes, emoji integration, platform guidelines, troubleshooting, spring/easing/shake/pulse formulas, accessibility guidance, sources.ffmpeg-captions-subtitles - Subtitle fundamentals and burn-inffmpeg-karaoke-animated-text - ASS karaoke and animated text patternsffmpeg-animation-timing-reference - Timing units, easing, readability, syncviral-video-platform-specs - Export specs and safe zonesviral-video-hook-templates - Hook and retention strategydevelopment
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.