Skills pytorch-lightning

📦

pytorch-lightning

Name: pytorch-lightning
Author: davila7

Content revision r2 Safe ⚡ Contains scripts🌐 Network access⚙️ External commands🔑 Env variables

Build Scalable PyTorch Lightning Training Workflows

Deep learning projects become difficult to maintain when training, data, logging, and scaling logic are mixed. This skill provides structured PyTorch Lightning guidance and templates.

Supports: Claude Codex Code(CC)

🥈 82 Silver

Install with my Agent

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Agent request

Review the Skillstore skill "pytorch-lightning" from https://skillstore.io/skills/davila7-pytorch-lightning.md and its manifest at https://skillstore.io/api/skills/davila7-pytorch-lightning/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

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Markdown detail GET /skills/davila7-pytorch-lightning.md Signed manifest GET /api/skills/davila7-pytorch-lightning/manifest Signed lockfile GET /api/skills/davila7-pytorch-lightning/lockfile

Test it

Using "pytorch-lightning". Convert a classifier training loop into PyTorch Lightning.

Expected outcome:

A LightningModule outline separates forward, training, validation, testing, metrics, and optimizer configuration. It also identifies required project-specific replacements.

Using "pytorch-lightning". Recommend settings for training a large transformer on eight GPUs.

Expected outcome:

Use FSDP when model parameters exceed practical single-device memory.
Enable mixed precision and activation checkpointing after checking model compatibility.
Validate the setup with a short development run before full training.

Using "pytorch-lightning". Add experiment tracking and reliable checkpoints.

Expected outcome:

A Trainer plan combines one selected logger, monitored checkpoints, early stopping, learning-rate tracking, and reduced logging frequency.

Security Audit

Safe

v9 • 7/23/2026 Open versioned report

All 62 static findings are false positives caused by documentation syntax, safe framework APIs, placeholders, or bounded training examples. No prompt injection, credential exposure, command injection, or malicious network behavior was found.

Files scanned

5,458

Lines analyzed

Review items

False positives ignored

Risk Factors

⚡ Contains scripts (3)

references/callbacks.md:515-520 references/lightning_module.md:445 scripts/quick_trainer_setup.py:9-15

🌐 Network access (4)

references/data_module.md:42 references/distributed_training.md:439 references/distributed_training.md:444 references/logging.md:82

⚙️ External commands (44)

🔑 Env variables (3)

references/distributed_training.md:522 references/logging.md:103 references/logging.md:125

No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.

Audited by: codex View Audit History →

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Academic citations (APA · BibTeX · CFF)

APA citation

davila7. (2026). pytorch-lightning security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/davila7-pytorch-lightning/audits/9

BibTeX citation

@techreport{davila7-davila7-pytorch-lightning-2026,
  author = {davila7},
  title = {pytorch-lightning security audit report (audit version 9)},
  institution = {Skillstore},
  year = {2026},
  number = {9},
  url = {https://skillstore.io/skills/davila7-pytorch-lightning/audits/9},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "pytorch-lightning security audit report (audit version 9)"
version: "unspecified"
type: report
authors:
  - name: "davila7"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/davila7-pytorch-lightning/audits/9"
identifiers:
  - type: other
    value: "skillstore:davila7-pytorch-lightning:audit:9"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Highest Skillstore Score

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

davila7 Recommended Current

davila7-pytorch-lightning

Skillstore Score 82

Evidence Confidence High

Skillstore usage 6

Updated

2026-08-21

K-Dense-AI

k-dense-ai-pytorch-lightning

Skillstore Score 78

Evidence Confidence High

Skillstore usage 11

Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Structure a Research Prototype

Convert a manual PyTorch loop into clear model, data, validation, logging, and optimizer components.

Standardize Team Training Code

Create reusable project templates with consistent callbacks, checkpoints, metrics, and data handling.

Scale Large Training Jobs

Select and configure DDP, FSDP, or DeepSpeed for available accelerators and model size.

Try These Prompts

Create a Basic Module

Create a PyTorch LightningModule for [task] using [model], [loss], and [optimizer]. Include training and validation steps.

Build a Data Module

Design a LightningDataModule for [dataset]. Include preparation, stage-aware setup, transforms, splits, and configurable DataLoaders.

Configure Production Training

Configure a Trainer for [hardware] with mixed precision, checkpoints, early stopping, gradient clipping, logging, and reproducibility.

Design Distributed Training

Compare DDP, FSDP, and DeepSpeed for [model size] on [cluster]. Recommend a strategy and explain memory, speed, and operational tradeoffs.

Best Practices

Start with a short development run before increasing epochs, devices, or dataset size.
Keep data preparation, model logic, callbacks, and logging responsibilities separate.
Store service credentials outside source code and inject them through supported secret management.

Avoid

Do not set state in prepare_data because distributed workers do not receive that state.
Do not use device-specific calls when Lightning can manage device placement.
Do not enable complex distributed strategies before validating single-device correctness.