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 agentmods add rules/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/pytorchgit clone --depth 1 https://github.com/XingjianTao/Cursor-Rules-for-PyTorch-DeepLearning-BeginnerWrote 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/rules/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/pytorch)<a href="https://agentmods.dev/rules/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/pytorch"><img src="https://agentmods.dev/badge/rules/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/pytorch.svg" alt="Measured on agentmods" 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 | $0.04719 | $0.04719 |
| Opus 5 | $0.02360 | $0.02360 |
| Sonnet 5 | $0.00944 | $0.00944 |
| Haiku 4.5 | $0.00472 | $0.00472 |
Grade A, and why
pytorch 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 382 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PyTorch Best Practices and Coding Standards
This document provides comprehensive guidelines for developing PyTorch projects, encompassing code organization, performance optimization, security, testing methodologies, and common pitfalls. Adhering to these best practices will result in more readable, maintainable, and efficient PyTorch code.
Library Information:
- Name: PyTorch
- Category: ai_ml
- Subcategory: machine_learning
1. Code Organization and Structure
1.1 Directory Structure Best Practices
A well-organized directory structure enhances code maintainability and collaboration. Here's a recommended structure for PyTorch projects:
project_root/ ├── data/ │ ├── raw/ │ ├── processed/ │ └── ... ├── models/ │ ├── layers.py │ ├── networks.py │ ├── losses.py │ ├── ops.py │ └── model_name.py ├── src/ │ ├── data/ │ │ ├── datasets.py │ │ ├── dataloaders.py │ │ └── transforms.py │ ├── models/ │ │ └── ... (model-related code) │ ├── utils/ │ │ └── ... (utility functions) │ └── visualization/ │ └── ... ├── notebooks/ │ └── ... (Jupyter notebooks for experimentation) ├── tests/ │ ├── unit/ │ ├── integration/ │ └── ... ├── scripts/ │ └── train.py │ └── eval.py ├── configs/ │ └── ... (Configuration files, e.g., YAML) ├── README.md ├── requirements.txt ├── .gitignore └── ...
data/: Stores raw and processed datasets.models/: Contains PyTorch model definitions, layers, and custom loss functions. Separate network architectures, individual layers/blocks, and operations into different files.src/: Holds the main source code, including data loading, model definitions, utility functions, and visualization tools. It's common to split thesrc/folder further based on responsibilities.notebooks/: Jupyter notebooks for experimentation and exploration. Use notebooks for initial exploration and prototyping but transition finalized code to Python scripts.tests/: Unit, integration, and end-to-end tests.scripts/: Training, evaluation, and deployment scripts. The main training script should import model definitions.configs/: Configuration files for hyperparameter settings and other parameters.
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.
- 5d ago First seen · 382 lines · 4,719 tokens per session scan A 2b520e1ebecb
pytorch is a cursor rule published in the GitHub repository XingjianTao/Cursor-Rules-for-PyTorch-DeepLearning-Beginner (3 stars, last pushed 1y ago), licensed MIT. It adds 4,719 tokens to every session, about $0.0236 per session on Opus 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 2026-08-31.
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