ML Training Recipes is an AI Engineering skill for Claude Code, published by Orchestra-Research in AI-Research-SKILLs.
ML Training Recipes gives PyTorch patterns for choosing architectures, setting optimizers and learning rates, and debugging training issues. Use it when training or fine-tuning neural networks, tuning GPU throughput, or diagnosing loss spikes and OOMs.
ML Training Recipes is cataloged under AI Engineering on DirSkills. ML Training Recipes comes from a repository tagged ai, ai-research, claude, claude-code and claude-skills.
Documentation
Battle-tested patterns for PyTorch training across domains. Drawn from production codebases (Karpathy's autoresearch/nanochat, torchvision, HuggingFace) and modern training practice.
This is the opening of the README. Read the full README on GitHub.
ML Training Recipes is one of 50 skills that DirSkills catalogs from Orchestra-Research/AI-Research-SKILLs, the repository it ships in. Its siblings there include AWQ Quantization, Autoresearch and Axolotl. Each one is a separate skill with its own page in this directory, installs the same way ML Training Recipes does, and is maintained by Orchestra-Research in that same repository. The rest of the collection is listed on the Orchestra-Research/AI-Research-SKILLs page.
ML Training Recipes ranks #347 by stars among the 2451 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of ML Training Recipes against them. Open each page to compare what they document and how they install.
ML Training Recipes is one of 50 skills cataloged on DirSkills from Orchestra-Research/AI-Research-SKILLs.
ML Training Recipes is one of 2451 AI Engineering skills cataloged on DirSkills, ranked #347 by stars among them.