Explorar skills

Simula uma entrevista estruturada e produz uma transcrição realista. Utiliza um Protocolo de Entrevista e um perfil de persona do entrevistado, pesquisando a persona online ou sintetizando traços, para gerar uma transcrição com orçamento de tempo onde o entrevistado fala autenticamente, incluindo disfluências.

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.

Rasputin Omnitool é um pacote de habilidades OpenClaw com um loop de agente planejador/executor/revisor e 12 ferramentas (pesquisa, navegador, sandbox, multimídia). É um agente equivalente ao Manus, construído a partir de software de código aberto.

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.

Build and validate user personas and segment definitions grounded in real research data. Use this skill when a team needs to define who they are building for with enough specificity to guide product decisions.

6-model R&D Council debate — multi-round argument, cross-examination, consensus memo. Triggers: "/debate", "council", "R&D meeting", "model debate", "6 models discuss". NOT FOR: simple questions (just ask), code review (use review), brainstorming. Produces: executive memo with consensus position from 6 AI models.

Market research and validation agent. Analyzes competitors, identifies target users, and validates product-market fit before any code is written. Triggers on: market research, competitor analysis, validate idea, target audience.

Complete guide for Weights & Biases (W&B) - experiment tracking, hyperparameter sweeps, artifact management, model registry, and PyTorch Lightning integration

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.

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.

Setup development environment with modern Python tooling (uv/pixi), install dependencies, and configure development tools (ruff, ty, pytest). Use when setting up new ML projects, configuring environments, or installing dependencies.