Implement and debug large-model training with PyTorch FSDP, which splits training state across devices. Consult documentation and code patterns for sharding choices, mixed precision, and checkpoints when building or reviewing distributed training code.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Fully sharded data-parallel training for large models.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Assistance with pytorch-fsdp development, generated from official documentation.
This skill should be triggered when:
The full common-patterns catalog (~157k chars of runnable FSDP snippets) lives in
references/common-patterns.md — load it with read_file when you need wrapping,
sharding-strategy, checkpoint, or mixed-precision examples. Start there rather than
reconstructing FSDP incantations from memory.
This skill includes comprehensive documentation in references/:
Use view to read specific reference files when detailed information is needed.
Start with the getting_started or tutorials reference files for foundational concepts.
Use the appropriate category reference file (api, guides, etc.) for detailed information.
The quick reference section above contains common patterns extracted from the official docs.
Organized documentation extracted from official sources. These files contain:
Add helper scripts here for common automation tasks.
Add templates, boilerplate, or example projects here.
To refresh this skill with updated documentation:
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Set up the 1Password CLI and use stored passwords or API keys in commands and configuration templates. Choose an authentication method, retrieve secret references, and pass credentials to processes through environment variables.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Build an Excel workbook linking the income statement, balance sheet, and cash flow statement. Populate historical figures, create formula-based forecasts, compare scenarios, and check that balances and cash totals agree across all three statements.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Adapt existing PyTorch training scripts to use multiple GPUs or machines with limited code changes. The skill guides configuration, launch commands, mixed precision, which uses lower-precision numbers, and DeepSpeed or FSDP settings for distributing training work and memory.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Configure Hermes to run AI models through Actual Computer on your own hardware. Set up either the hosted relay or a local connection, select models, verify responses, and troubleshoot request errors or empty output.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Test an app from the perspective of a frustrated, less technical user. Find confusing steps and barriers, separate actionable usability problems from personal complaints, and produce screenshots, suggested fixes, and prioritized tickets.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Resolve Git merge conflicts between two agents by comparing both changes and their stated goals. Combine compatible work, explain choices where designs differ, and verify the merged result with project builds or tests.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.