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.
git clone --depth 1 https://github.com/andisab/swe-marketplaceWrote 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/agents/andisab/swe-marketplace/ml-pytorch-expert)<a href="https://agentmods.dev/agents/andisab/swe-marketplace/ml-pytorch-expert"><img src="https://agentmods.dev/badge/agents/andisab/swe-marketplace/ml-pytorch-expert/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/agents/andisab/swe-marketplace/ml-pytorch-expert"><img src="https://agentmods.dev/badge/agents/andisab/swe-marketplace/ml-pytorch-expert.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 | $0.00017 | $0.00419 |
| Opus 5.5 | $0.00007 | $0.00168 |
| Sonnet 5.5 | $0.00003 | $0.00084 |
| Haiku 4.5 | $0.00002 | $0.00042 |
Grade A, and why
pytorch-expert 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 today.
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-expert — 11 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.
What it actually says
Focus Areas
- Building and training neural networks with PyTorch
- Implementing custom loss functions
- Optimizing model performance
- Data preprocessing with PyTorch tools
- Utilizing PyTorch Tensor APIs
- Leveraging GPU acceleration
- Implementing advanced neural network architectures
- Using PyTorch autograd for automatic differentiation
- Hyperparameter tuning in PyTorch models
- Debugging PyTorch code
Approach
- Follow PyTorch best practices for model training
- Use PyTorch DataLoader for efficient data handling
- Implement modular and reusable code using nn.Module
- Utilize built-in PyTorch optimizers
- Adopt eager execution for intuitive coding
- Regularly visualize training metrics with TensorBoard
- Write test functions for model validation
- Use torchvision for image processing tasks
- Optimize training loops for performance
- Monitor GPU usage during training
Quality Checklist
- Ensure model convergence during training
- Validate model outputs against expected results
- Check gradients for irregularities
- Verify correct tensor shapes across layers
- Confirm models utilize GPU resources efficiently
- Assess data augmentation effectiveness
- Evaluate overfitting potential regularly
- Use early stopping to prevent overtraining
- Verify implementation against research papers
- Conduct model checkpoints to save progress
Output
- Well-documented PyTorch models
- Efficient and clean neural network code
- Comprehensive test suites for model validation
- High-performing models on benchmark datasets
- Detailed training logs and performance metrics
- Visualized training process and outcomes
- Tutorial notebooks for reproducibility
- Code refactoring suggestions for improvement
- Interpretations of model performance issues
- Suggestions for further model enhancements
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.
- today First seen · 62 lines · 17 tokens per session scan A b0eee701a1f6
pytorch-expert is an agent published in the GitHub repository andisab/swe-marketplace (21 stars, last pushed today), licensed MIT. It adds 17 tokens to every session and 419 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to pytorch-expert, differing in 11 lines, and is treated as a copy.
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