Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add magic3007/dotfiles --skill pytorch-lightninggit clone --depth 1 https://github.com/magic3007/dotfilesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/magic3007/dotfiles/pytorch-lightning)<a href="https://agentmods.dev/skills/magic3007/dotfiles/pytorch-lightning"><img src="https://agentmods.dev/badge/skills/magic3007/dotfiles/pytorch-lightning/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/magic3007/dotfiles/pytorch-lightning"><img src="https://agentmods.dev/badge/skills/magic3007/dotfiles/pytorch-lightning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00071 | $0.01711 |
| Opus 5.5 | $0.00028 | $0.00684 |
| Sonnet 5.5 | $0.00014 | $0.00342 |
| Haiku 4.5 | $0.00007 | $0.00171 |
Grade A, and why
pytorch-lightning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
86% identical to pytorch-lightning — 99 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
Current upstream: lightning 2.6.4 (PyPI, May 2026). Docs: lightning.ai/docs/pytorch/stable. Use import lightning as L (the pytorch-lightning package name still installs the same library).
Installation
uv pip install lightning
Optional extras:
uv pip install lightning[extra] # loggers, strategies, etc.
uv pip install wandb mlflow # specific loggers as needed
When to Use This Skill
This skill should be used when:
- Building, training, or deploying neural networks using PyTorch Lightning
- Organizing PyTorch code into LightningModules
- Configuring Trainers for multi-GPU/TPU training
- Implementing data pipelines with LightningDataModules
- Working with callbacks, logging, and distributed training strategies (DDP, FSDP, DeepSpeed)
- Structuring deep learning projects professionally
Core Capabilities
1. LightningModule - Model Definition
Organize PyTorch models into six logical sections:
- Initialization -
__init__()andsetup() - Training Loop -
training_step(batch, batch_idx) - Validation Loop -
validation_step(batch, batch_idx) - Test Loop -
test_step(batch, batch_idx) - Prediction -
predict_step(batch, batch_idx) - Optimizer Configuration -
configure_optimizers()
Quick template reference: See scripts/template_lightning_module.py for a complete boilerplate.
Detailed documentation: Read references/lightning_module.md for comprehensive method documentation, hooks, properties, and best practices.
2. Trainer - Training Automation
The Trainer automates the training loop, device management, gradient operations, and callbacks. Key features:
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/best_practices.md 16 KB
- references/callbacks.md 15 KB
- references/data_module.md 14 KB
- references/distributed_training.md 14 KB
- references/lightning_module.md 13 KB
- references/logging.md 14 KB
- references/trainer.md 14 KB
- scripts/quick_trainer_setup.py 13 KB runs code
- scripts/template_datamodule.py 9.1 KB runs code
- scripts/template_lightning_module.py 5.7 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 192 lines · 71 tokens per session scan A 97fdb677fc10
pytorch-lightning is a skill published in the GitHub repository magic3007/dotfiles (11 stars, last pushed yesterday), licensed MIT. It adds 71 tokens to every session and 1,711 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to pytorch-lightning, differing in 99 lines, and is treated as a copy.
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