Guide · Agent skill
How to use the PyTorch Patterns Guide agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The pytorch-patterns skill is a written reference of PyTorch coding conventions that an AI agent reads when you ask it to write or review deep learning code. Once installed, the agent is guided toward code that runs on CPU or GPU, sets random seeds, annotates tensor shapes, and uses sound training loops and data loaders. This guide explains what the skill changes, how to install it, how to trigger it, and what to watch for.
PyTorch Patterns Guideaffaan-m/ECC · MIT Download the skill Quick start
- 1Download the ZIP. Get the pytorch-patterns ZIP from this page.
- 2Unzip into a skills folder. Extract it into ~/.claude/skills/ for personal use or into .claude/skills/ inside your project.
- 3Or upload in the Claude apps. Go to Settings, then Capabilities, then Skills, and upload the skill there.
- 4Install Python dependencies. Make sure Python, PyTorch (torch), numpy and Pillow are available in the environment where you run your code.
- 5Ask for PyTorch work in plain language. Describe a training script, model, data loader or training loop problem. The agent applies the skill when the request matches its scope.
- 6Review the output. Compare the generated code with the good and bad examples in SKILL.md before you run it.
What does the pytorch-patterns skill make the agent do?
A skill is a folder of instructions that an agent can load when a task matches it. This one is a single file, SKILL.md, that describes PyTorch patterns and best practices for training pipelines, model architectures and data loading.
The file is organized as good and bad examples. It covers device-agnostic code, reproducibility, explicit shape management, clean nn.Module structure, weight initialization, training and validation loops with mixed precision, custom datasets, DataLoader configuration, custom collate functions and checkpointing. When the agent writes or reviews code in these areas, it has this reference to follow.
- Device-agnostic code that runs on CPU or GPU
- Seed setup so experiments repeat across runs
- Shape comments on forward passes
- Training and validation loops with mixed precision and gradient clipping
- DataLoader settings for faster loading
- Checkpoints that save model, optimizer and epoch state
Install the pytorch-patterns skill
Download the ZIP for the skill from this page. Unzip it into one of two folders. For a personal setup that applies across your projects, use ~/.claude/skills/. For a single project, use the .claude/skills/ folder inside that project.
If you use the Claude apps instead, you can upload the skill under Settings, then Capabilities, then Skills, rather than placing files in a folder.
- Personal install: unzip into ~/.claude/skills/
- Project install: unzip into .claude/skills/ in your project
- Claude apps: Settings → Capabilities → Skills, then upload the skill
How do you trigger the skill with natural requests?
You do not need a special command. Ask for PyTorch work in plain language, and the agent should apply the skill when the request matches its scope. The skill lists the situations it activates for: writing new PyTorch models or training scripts, reviewing deep learning code, debugging training loops or data pipelines, optimizing GPU memory or training speed, and setting up reproducible experiments.
Name the PyTorch part of your task so the match is clear. The more specific your request, the more likely the agent's suggestions will follow these conventions.
- Write a PyTorch training script for an image classifier with reproducible seeds.
- Review my training loop for mixed precision and gradient clipping issues.
- Why is my DataLoader slow? Suggest settings for num_workers and pin_memory.
- Add shape comments to this forward method and check the tensor dimensions.
Example: what the agent's code looks like
The skill steers the agent toward patterns like the device setup below, which avoids hardcoding .cuda() calls that crash on machines without a GPU.
The DataLoader pattern in the source sets num_workers, pin_memory and persistent_workers explicitly rather than relying on slow defaults.
# Good: Device-agnostic
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = MyModel().to(device)
data = data.to(device)
# Bad: Hardcoded device
model = MyModel().cuda() # Crashes if no GPU
data = data.cuda()What does the skill need and what are its limits?
The skill itself is a text file. It does not run scripts or make network calls. To make use of the code it suggests, you need Python and PyTorch (torch), along with numpy and Pillow, which appear in the examples.
The file covers training and data code only. It does not address deployment, monitoring or security review, so production ML systems need additional review beyond it.
- Requires: Python, PyTorch (torch), numpy and Pillow
- Runs scripts: no
- Uses network: no
- Files: SKILL.md
Tips for getting good results
Treat the skill as a set of conventions, not a guarantee. Review the code the agent produces against the examples in SKILL.md, especially where your project has its own standards.
For reproducibility, the skill's seed pattern sets the PyTorch, CUDA, NumPy and Python random seeds and turns on deterministic cuDNN. Be aware that this setting trades some speed for repeatability.
- Ask the agent to explain which pattern it applied and why.
- Check that the model is set to train mode before training and eval mode before validation, as the source loops do.
- Keep your own project conventions in the same folder so the agent can see them alongside the skill.
What people use it for
- Drafting a training script with device-agnostic setup and a validation loop.
- Reviewing an existing model for shape tracking and weight initialization.
- Speeding up a slow data pipeline by tuning DataLoader options.
- Setting up experiments that need repeatable results across runs.
- Learning idiomatic PyTorch structure by reading the agent's annotated code.
Frequently asked questions
How do I install the pytorch-patterns skill?
Download the ZIP from this page and unzip it into ~/.claude/skills/ for personal use or into .claude/skills/ inside a project. You can also upload it in the Claude apps under Settings, then Capabilities, then Skills.
Is the pytorch-patterns skill free to use?
The skill is released under the MIT licence, as stated on this page. Check the licence file in the source repository for the full terms.
What do I need to use the pytorch-patterns skill?
You need Python and PyTorch (torch), and the examples also use numpy and Pillow. The skill itself does not run scripts or use the network.
Does the skill work with TensorFlow or JAX?
No. The patterns are specific to PyTorch, so they do not apply to non-PyTorch frameworks such as TensorFlow or JAX.
Why isn't the agent using the skill when I ask for PyTorch code?
Name the PyTorch task clearly, such as writing a training loop or reviewing a DataLoader. Confirm the folder is in ~/.claude/skills/ or .claude/skills/, or that the upload under Settings, then Capabilities, then Skills succeeded.
How does this compare to other PyTorch guides or skills?
This skill is a reference of conventions the agent follows for training and data code. It does not generate complete models or cover deployment, monitoring or security, so pick a different resource if you need those.
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Our verdict
Engineers writing or reviewing PyTorch training scripts, researchers who want reproducible experiments, and students learning idiomatic training loop structure will get the most from this skill. Teams building production ML systems that need deployment, monitoring or security review, or anyone using TensorFlow or JAX, should look elsewhere.
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