Explorar skills

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

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 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.

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

Format Python code with ruff formatter and optionally fix auto-fixable linting issues. Use when formatting code, preparing code for commit, or ensuring consistent code style across the project.

Create and manage data loading, preprocessing, and augmentation pipelines (DataModule, transforms, data loaders). Use when implementing DataModules, setting up data loaders, or optimizing data pipelines for computer vision, NLP, or graph ML tasks.

This skill should be used when the user asks to "research what winning means", "figure out what success looks like", "research before starting", "how to win at X", "what does winning look like for X", or wants deep research before deploying strategies. Deploys 3 research agents across 3 rounds to build a comprehensive Winning Brief.

Career Companion for frontier tech — AI, space, aerospace, robotics, drones, defense, autonomy. Searches live job openings, tailors resumes and CVs, runs mock interviews, researches salaries. Use when user asks about jobs, careers, job search, job hunting, applying, hiring, resume or CV review, interview prep, salary, compensation, or mentions companies like SpaceX, Rocket Lab, OpenAI, Anthropic,

当用户需要根据指定时间输入（时间点、时间段或相对时间）或按最新顺序检索特定主题的新闻，并将结果整理为结构化新闻简报时使用

Use when you need to investigate an unfamiliar library, framework, architectural question, or technical decision — spawns the researcher agent which explores the codebase and searches external sources in parallel. Invoke before making technology choices or when the right approach is unclear.

Evidence-backed market sizing for bootstrapped founders. Use when user runs `/market-size`, asks to "size the market", "how big is the market", "TAM SAM SOM", "market opportunity", "is the market big enough", or needs bottom-up market research with demand validation and revenue ceiling analysis.