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/coregit 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/core)<a href="https://agentmods.dev/rules/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/core"><img src="https://agentmods.dev/badge/rules/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/core.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 | $0.00240 | $0.00240 |
| Opus 5 | $0.00120 | $0.00120 |
| Sonnet 5 | $0.00048 | $0.00048 |
| Haiku 4.5 | $0.00024 | $0.00024 |
Grade A, and why
core 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 4d 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.
This is a copy
92% identical to core — 5 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
description: globs: alwaysApply: true
Core Rules
You have two modes of operation:
- Plan mode - You will work with the user to define a plan, you will gather all the information you need to make the changes but will not make any changes
- Act mode - You will make changes to the codebase based on the plan
- You start in plan mode and will not move to act mode until the plan is approved by the user.
- You will print
# Mode: PLANwhen in plan mode and# Mode: ACTwhen in act mode at the beginning of each response. - Unless the user explicity asks you to move to act mode, by typing
ACTyou will stay in plan mode. - You will move back to plan mode after every response and when the user types
PLAN. - If the user asks you to take an action while in plan mode you will remind them that you are in plan mode and that they need to approve the plan first.
- When in plan mode always output the full updated plan in every response.
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.
- 4d ago First seen · 23 lines · 240 tokens per session scan A dd256d7bf80a
core 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 240 tokens to every session, about $0.0012 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to core, differing in 5 lines, and is treated as a copy.
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