Explorar skills

Comprehensive guide for Hydra configuration management, hierarchical configs, experiment management, Optuna integration, and Lightning integration patterns

Export trained PyTorch models to various formats (ONNX, TorchScript, TensorRT) and upload to model registries (Hugging Face Hub, MLflow). Use when deploying models, sharing trained weights, or preparing for production inference.

Comprehensive validation of ML project structure, configurations, code quality, and training readiness. Use when setting up a new project, before training runs, or debugging configuration issues. Validates config loading, data pipeline, model architecture, and dependencies.

Comprehensive guide for marimo - reactive Python notebooks as pure .py files, uv integration, AI-friendly architecture, reproducible data science workflows, and serverless deployment with WASM

Install Remotion, set up Remotion, add Remotion to a project, install Remotion AI skills, update Remotion, upgrade Remotion packages, check Remotion version, set up video project with Remotion, remotion skills, remotion rules

Service-profile-driven project audit. Auto-fires when the user requests audit, review, code review, pre-launch check, security audit, OWASP/SOLID/12-Factor compliance, project skeleton/bootstrap/setup, or any equivalent in any language (e.g., 점검, 감사, 리뷰, 출시 전 검토, 보안 점검, 골조, 셋업). Reads the full 0–10 section checklist from SPEC.md, filters items by grade (🔴🟠🟡🔵⚪) against the user's service profil

Comprehensive growth hacking strategy including growth loops, AARRR pirate metrics, channel prioritization (Bullseye), viral mechanics (K-factor), ICE experiment scoring, and 90-day experimentation roadmap using Growth Loops, Pirate Metrics, and Traction Bullseye frameworks.

Debug common ML training issues (NaN loss, OOM, slow training, convergence problems) and provide solutions. Use when training fails, metrics don't improve, or encountering errors like NaN loss, CUDA OOM, or slow convergence.

Comprehensive guide for PyTorch Lightning - LightningModule, Trainer, distributed training, PyTorch 2.0 torch.compile integration, Lightning Fabric, and production best practices

Run comprehensive code quality checks with ruff (format, lint) and ty (type checking). Use when checking code quality, fixing linting errors, or ensuring code follows best practices before commits or PRs.

Execute training runs with proper monitoring, checkpointing, and experiment tracking. Use when starting training, resuming training, debugging training issues, or setting up multi-GPU/distributed training with PyTorch Lightning and Hydra.

Hugging Face Transformers with PyTorch Lightning - LightningModule integration, distributed training (FSDP/DeepSpeed), PEFT (LoRA/QLoRA), data pipelines with HF Datasets, evaluation metrics, and common NLP tasks