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 G1Joshi/Agent-Skills --skill pytorchgit clone --depth 1 https://github.com/G1Joshi/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/g1joshi/agent-skills/pytorch)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/pytorch"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/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 5.1 | $0.00017 | $0.00309 |
| Opus 5 | $0.00009 | $0.00154 |
| Sonnet 5 | $0.00003 | $0.00062 |
| Haiku 4.5 | $0.00002 | $0.00031 |
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 8d 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.
What it actually says
PyTorch
PyTorch is the dominant framework for research and production AI. v2.5 (2025) solidifies torch.compile and introduces FlexAttention.
When to Use
- Research: 99% of new papers (Arxiv) use PyTorch.
- Production: Recommended for almost all new DL projects.
- Performance:
torch.compileprovides C++ level speed with Python ease.
Core Concepts
torch.compile
Just-in-Time (JIT) compilation of your model.
model = torch.compile(model) -> 2x speedup.
Dynamic Graphs (Eager Mode)
Debug line-by-line (print(tensor.shape) works).
Fabric / Lightning
High-level wrappers to simplify training loops and multi-GPU setup.
Best Practices (2025)
Do:
- Use
torch.compile: It is now stable and essential for H100 performance. - Use
FlashAttention: Use the scaled dot product attention (SDPA) kernel for Transformers. - Use PyTorch 2.x: PyTorch 1.x is legacy.
Don't:
- Don't code
.cuda()manually: Use.to(device)or Fabric to handle device placement.
References
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.
- 8d ago First seen · 46 lines · 17 tokens per session scan A 995cc5717c41
pytorch is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 6mo ago), licensed MIT. It adds 17 tokens to every session and 309 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
accelerate
Run PyTorch training across GPUs with minimal changes.
telnyx-stt-python
Transcribe audio to text via the OpenAI-compatible transcription endpoint. Supports multiple models, languages, and keyword biasing. Also lists available speech-to-text providers and service types.
databricks-python-sdk
Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. Use when working with databricks-sdk, databricks-connect, or Databricks APIs.
telnyx-ai-inference-python
Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides Python SDK examples.
cursor-pytorch
Cursor IDE rules for pytorch.
pytorch-patterns
PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.