Explore skills
5,464 skills found
Category alert
Get new Pesquisa e Web skills every Monday
ml-wandb-tracking
Complete guide for Weights & Biases (W&B) - experiment tracking, hyperparameter sweeps, artifact management, model registry, and PyTorch Lightning integration
ml-pytorch-geometric
Complete guide for PyTorch Geometric (PyG) - graph neural networks, message passing, large-scale distributed graph learning, Lightning integration, and heterogeneous graphs
ml-project-init
Initialize a new ML research project using the ML Research template with PyTorch Lightning, Hydra, and modern Python tooling. Use when starting a new ML project from scratch.
ml-profile
Profile ML training performance to identify bottlenecks (data loading, compute, memory usage) and optimize GPU utilization. Use when training is slow, GPU utilization is low, or experiencing memory issues.
ml-cli-tools
Building professional CLIs with Typer and Rich - type-safe argument parsing, progress bars, model visualization, Hydra integration, RichHandler logging, and multi-process handling for ML workflows
ml-train
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.
ml-lint
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.
ml-lightning-basics
Comprehensive guide for PyTorch Lightning - LightningModule, Trainer, distributed training, PyTorch 2.0 torch.compile integration, Lightning Fabric, and production best practices
ml-debug
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.
ml-hydra-config
Comprehensive guide for Hydra configuration management, hierarchical configs, experiment management, Optuna integration, and Lightning integration patterns
generate-insights
Distill raw observations and research data into meaningful, actionable product insights. Use this skill when a team has research data and needs to move from observations to insights that can drive decisions.
synthesize-qualitative-research
Synthesize user interviews, JTBD analysis, and qualitative data into actionable product insights. Use this skill when a team has raw qualitative data and needs structured insights.