Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.
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 K-Dense-AI/scientific-agent-skills --skill pytorch-lightninggit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skillsWrote 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/k-dense-ai/scientific-agent-skills/pytorch-lightning)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/pytorch-lightning"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/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/k-dense-ai/scientific-agent-skills/pytorch-lightning"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/pytorch-lightning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.02310 |
| Opus 5.5 | $0.00028 | $0.00924 |
| Sonnet 5.5 | $0.00014 | $0.00462 |
| Haiku 4.5 | $0.00007 | $0.00231 |
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 25d 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- pytorch-lightning — 100% identical, 83 lines differ
- pytorch-lightning — 86% identical, 101 lines differ
- pytorch-lightning — 86% identical, 0 lines differ
- pytorch-lightning — 86% identical, 101 lines differ
- pytorch-lightning — 86% identical, 99 lines differ
- pytorch-lightning — 80% identical, 112 lines differ
- pytorch-lightning — 80% identical, 112 lines differ
- pytorch-lightning — 80% identical, 112 lines differ
How it starts
The opening of the file, as written. The whole thing — 209 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.
Reviewed release: Lightning 2.6.6, including its checkpoint-loading security fixes. CPU tests use Python 3.11, Torch 2.14.1 and Lightning 2.6.6. Use import lightning as L with the lightning distribution. The alternative pytorch-lightning distribution exposes import pytorch_lightning as pl; do not mix class namespaces within one application. GPU, TPU, multi-node and online logger examples are source-verified illustrations, not executed training validation.
Installation
uv pip install "lightning==2.6.6"
Optional extras:
uv pip install "lightning[extra]==2.6.6" # 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.
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 5.2 KB
- references/callbacks.md 5.4 KB
- references/data_module.md 5.3 KB
- references/distributed_training.md 7.4 KB
- references/lightning_module.md 5.6 KB
- references/logging.md 7.7 KB
- references/trainer.md 5.6 KB
- scripts/quick_trainer_setup.py 14 KB runs code
- scripts/template_datamodule.py 6.7 KB runs code
- scripts/template_lightning_module.py 6.1 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.
- 25d ago Changed · +1 lines d8829e74e8a1
- 26d ago Changed a73cef52643a
- 1mo ago First seen · 208 lines · 71 tokens per session scan A eacd09ffc22a
pytorch-lightning is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (54,777 stars, last pushed yesterday), licensed MIT. It adds 71 tokens to every session and 2,310 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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prompt-library
Use when looking for a ready-made agent or task template (code review, support, data analysis, translation, extraction) instead of writing one from scratch.
prompt-creation
Use when writing a new system prompt from scratch, structuring it with Anthropic's 9-element format, or choosing emphasis levels and few-shot examples.
prompt-testing
Use when comparing two prompt variants, defining quality/efficiency/robustness metrics, or deciding whether to adopt a challenger prompt over a baseline.
prompt-optimization
Use when auditing an existing prompt for clarity, structure, or completeness issues, fixing vague instructions, or producing a before/after optimization report.