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讀取規格文件轉換為 AI coding 實作文件（.md），產出技術規格、後端實作指示、前端實作指示等結構化文件。 使用時機：當使用者提供規格文件並要求產生實作文件、轉換規格為 coding 文件時觸發。 關鍵字：規格轉換、spec to md、產生實作文件、讀取規格、分析需求、coding 文件、開發文件、 to md、toMD、toMd、TOMD、specification, 規格文件, 需求文件, 需求轉換, 實作指示, 實作文件, 技術規格, 文件生成, 文件產生, 前端實作, 後端實作, convert, 轉換。

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.

Complete guide for PyTorch Geometric (PyG) - graph neural networks, message passing, large-scale distributed graph learning, Lightning integration, and heterogeneous graphs

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.

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

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.

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

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

Comprehensive guide for Pixi package manager - Python environment management, CUDA/GPU support, PyPI integration, Docker/Pixi-Pack deployment, and best practices for ML research

Comprehensive guide for building Python monorepos with uv workspaces - unified dependency resolution, shared lock files, editable installs, testing strategies, Docker optimization, and CI/CD patterns for managing multiple packages in a single repository

Full-stack implementation agent that writes complete, production-ready code following an approved architecture and schema. Triggers on: write the code, implement features, build the app, code the MVP, generate codebase.