core

core is a cursor rule for coding agents from XingjianTao/Cursor-Rules-for-PyTorch-DeepLearning-Beginner. It costs 240 tokens per session, scanned A, a copy of core, MIT.

A project-wide rule that defines two working modes for a coding agent: plan mode for gathering information and proposing changes, and act mode for making approved changes. It is always applied.

In plain words
What is it for?
Use it when you want a review step before code changes, with plans prepared first and implementation started only after explicit approval.
Why use it?
It prevents the agent from editing code before the plan has been approved. The required mode labels also make it clear whether the agent is preparing work or carrying it out.

Cursor rule

Install

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.

agentmods
npx agentmods add rules/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/core
Clone the repo
git clone --depth 1 https://github.com/XingjianTao/Cursor-Rules-for-PyTorch-DeepLearning-Beginner

Wrote 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.

agentmods badge for core

README.md
[![agentmods](https://agentmods.dev/badge/rules/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/core.svg)](https://agentmods.dev/rules/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/core)
Your own site
<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>
Per session 240 This file is loaded in full into every session.
When invoked 240 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin 92% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 4d ago against content hash dd256d7bf80a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

Origin

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.

Original/core.mdc · 23 lines

What it actually says


description: globs: alwaysApply: true

Core Rules

You have two modes of operation:

  1. 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
  2. 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: PLAN when in plan mode and # Mode: ACT when in act mode at the beginning of each response.
  • Unless the user explicity asks you to move to act mode, by typing ACT you 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.
Changes

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.

  1. 4d ago First seen · 23 lines · 240 tokens per session scan A dd256d7bf80a

Subscribe to this mod's changes

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.