plugins/ffmpeg-social-video/skills/viral-video-platform-specs/SKILL.md
Complete platform upload specifications for viral video creation across TikTok, YouTube Shorts, Instagram Reels, Facebook Reels, Snapchat Spotlight, and Twitter/X. PROACTIVELY activate for: (1) Platform-specific video encoding, (2) Upload requirements lookup, (3) Multi-platform export, (4) File size optimization, (5) Aspect ratio conversion, (6) Duration limits, (7) Audio requirements, (8) Caption/subtitle specs, (9) Thumbnail requirements, (10) Quality vs compatibility trade-offs. Provides: Comprehensive spec tables, FFmpeg presets per platform, optimal encoding settings, file size calculators, batch export workflows, and 2025-2026 platform-specific algorithm insights.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace viral-video-platform-specsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill to choose export settings for TikTok, YouTube Shorts, Instagram Reels, Facebook Reels, Snapchat Spotlight, Twitter/X, Pinterest, LinkedIn, and mobile-browser playback. This SKILL is a lean orchestrator; detailed platform tables, commands, and recipes are preserved in references/platform-details-and-recipes.md.
| Platform | Aspect | Recommended resolution | Practical duration | Max size | Default codec | |---|---:|---:|---:|---:|---| | TikTok | 9:16 | 1080x1920 | 21-34s | 287 MB iOS | H.264 + AAC | | YouTube Shorts | 9:16 | 1080x1920 | 50-59s | 256 GB | H.264 or VP9 | | Instagram Reels | 9:16 | 1080x1920 | 7-30s | 4 GB | H.264 + AAC | | Facebook Reels | 9:16 | 1080x1920 | 15-30s | 4 GB | H.264 + AAC | | Snapchat Spotlight | 9:16 | 1080x1920 | <=60s | 300 MB | H.264 + AAC | | Twitter/X | 9:16, 16:9, 1:1 | 1080x1920 | <=140s | 512 MB | H.264 + AAC |
ffmpeg -i input.mp4 \
-vf "scale=1080:1920:force_original_aspect_ratio=decrease,pad=1080:1920:(ow-iw)/2:(oh-ih)/2:black,setsar=1,fps=30" \
-c:v libx264 -preset medium -crf 22 -profile:v high -level 4.1 \
-c:a aac -b:a 192k -ar 48000 -ac 2 \
-pix_fmt yuv420p -movflags +faststart \
output_social.mp4
Adjust loudness, duration, bitrate, and metadata per platform from the reference file.
ffmpeg -version; prefer current stable builds for security and codec fixes.-pix_fmt yuv420p and -movflags +faststart for compatibility.ffprobe before upload.yuv420p, and faststart matter more than platform upload limits.references/platform-details-and-recipes.md - Full preserved reference: per-platform spec tables, FFmpeg presets, two-pass examples, multi-platform export script, file-size calculation, codec comparison, mobile-safe playback, color grading, text/caption readability, verification commands, sources.viral-video-animated-captions - CapCut-style caption generationviral-video-hook-templates - Hook patterns and retention tacticsffmpeg-animation-timing-reference - Timing, readability, sync, and platform pacingffmpeg-command-syntax - Correct option placementdevelopment
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.