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pytorch-lightning

Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.

foryourhealth111-pixelgithub.com/foryourhealth111-pixelGitHub ↗
claude-codecodexships scriptsApache-2.0
Install
npx skills add foryourhealth111-pixel/Vibe-Skills --skill pytorch-lightning --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 11
SKILL.md size: 6 KB
Bundled scripts: yes
Path: bundled/skills/pytorch-lightning/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,593
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# PyTorch Lightning ## Overview PyTorch Lightning is a deep learning framework that organizes PyTorch code to eliminate boilerplate while maintaining full flexibility. Automate training workflows, multi-device orchestration, and implement best practices for neural network training and scaling across multiple GPUs/TPUs. ## When to Use This Skill This skill should be used when: - Building, training,

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About this skill
What does the pytorch-lightning skill do?

Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.

How do I install it?

Run `npx skills add foryourhealth111-pixel/Vibe-Skills --skill pytorch-lightning --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From foryourhealth111-pixel/Vibe-Skills, a repository with 2,593 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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