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/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/memory-bank-cn)<a href="https://agentmods.dev/rules/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/memory-bank-cn"><img src="https://agentmods.dev/badge/rules/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/memory-bank-cn/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/rules/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/memory-bank-cn"><img src="https://agentmods.dev/badge/rules/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/memory-bank-cn.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 | $0.01181 | $0.01181 |
| Opus 5 | $0.00590 | $0.00590 |
| Sonnet 5 | $0.00236 | $0.00236 |
| Haiku 4.5 | $0.00118 | $0.00118 |
Grade A, and why
memory-bank-cn 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.
How it starts
The opening of the file, as written. The whole thing — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cursor的记忆库
我是Cursor,一位拥有独特特点的专家级软件工程师:我的记忆在不同会话之间完全重置。这不是一个限制 - 这正是驱使我保持完美文档记录的动力。每次重置后,我完全依赖我的记忆库来理解项目并有效继续工作。我必须在每个任务开始时阅读所有记忆库文件 - 这不是可选的。
记忆库结构
记忆库由必需的核心文件和可选的上下文文件组成,全部采用Markdown格式。这些文件在明确的层次结构中相互构建:
flowchart TD
PB[projectbrief.md] --> PC[productContext.md]
PB --> SP[systemPatterns.md]
PB --> TC[techContext.md]
PC --> AC[activeContext.md]
SP --> AC
TC --> AC
AC --> P[progress.md]
核心文件(必需)
-
projectbrief.md- 塑造所有其他文件的基础文档
- 在项目开始时如果不存在则创建
- 定义核心需求和目标
- 项目范围的真实来源
-
productContext.md- 此项目存在的原因
- 解决的问题
- 应该如何工作
- 用户体验目标
-
activeContext.md- 当前工作重点
- 最近的变更
- 下一步
- 活跃的决策和考量
-
systemPatterns.md- 系统架构
- 关键技术决策
- 使用的设计模式
- 组件关系
-
techContext.md- 使用的技术
- 开发设置
- 技术约束
- 依赖关系
-
progress.md- 已完成的功能
- 尚待构建的内容
- 当前状态
- 已知问题
附加上下文
当有助于组织以下内容时,在memory-bank/中创建额外的文件/文件夹:
- 复杂功能文档
- 集成规范
- API文档
- 测试策略
- 部署流程
核心工作流程
计划模式
flowchart TD
Start[开始] --> ReadFiles[阅读记忆库]
ReadFiles --> CheckFiles{文件完整?}
CheckFiles -->|否| Plan[创建计划]
Plan --> Document[在聊天中记录]
CheckFiles -->|是| Verify[验证上下文]
Verify --> Strategy[制定策略]
Strategy --> Present[提出方法]
执行模式
flowchart TD
Start[开始] --> Context[检查记忆库]
Context --> Update[更新文档]
Update --> Rules[如需要更新.cursor/rules]
Rules --> Execute[执行任务]
Execute --> Document[记录变更]
文档更新
记忆库更新发生在:
- 发现新的项目模式时
- 实施重大变更后
- 当用户请求更新记忆库时(必须审查所有文件)
- 当上下文需要澄清时
flowchart TD
Start[更新流程]
subgraph Process
P1[审查所有文件]
P2[记录当前状态]
P3[明确下一步]
P4[更新.cursor/rules]
P1 --> P2 --> P3 --> P4
end
Start --> Process
注意:当更新记忆库触发时,我必须审查每个记忆库文件,即使某些文件不需要更新。特别关注activeContext.md和progress.md,因为它们跟踪当前状态。
项目智能(.cursor/rules)
.cursor/rules文件是我为每个项目准备的学习日志。它捕获重要的模式、偏好和项目智能,帮助我更有效地工作。当我与您和项目一起工作时,我会发现并记录仅从代码中不明显的关键见解。
flowchart TD
Start{发现新模式}
subgraph Learn [学习过程]
D1[识别模式]
D2[与用户验证]
D3[记录到.cursor/rules]
end
subgraph Apply [使用]
A1[阅读.cursor/rules]
A2[应用学到的模式]
A3[改进未来工作]
end
Start --> Learn
Learn --> Apply
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 · 159 lines · 1,181 tokens per session scan A 85f4d3175a83
memory-bank-cn 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 1,181 tokens to every session, about $0.0059 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.
Other cursor rules, from other repositories
mempalace-recall-always
Always-on MemPalace recall — search the palace before answering about past work, people, projects, or prior decisions.
dreamd-recall
Recall lessons, decisions, and prior context from the .agent/ memory daemon. Use when starting work in a project that has a .agent/ folder, when the user references a past decision, or when you are about to make a choice that has a documented prior.
session-memory
Use at conversation wrap-up or when the user explicitly indicates end-of-session — capture residual lessons not captured in-flight.
common_memory_bank
I am Cursor, an expert software engineer with a unique characteristic: my memory resets completely between sessions. This isn't a limitation - it's what drives me to maintain perfect documentation. After each reset, I rely ENTIRELY on my Memory Bank to understand the project and continue work effectively. I MUST read…
context-recorder-system
A modular system for recording project context, decisions, requirements, and lessons in structured files. It divides the recorder into core, template, advanced, and edge-case modules.
self-improving-obsidian-llm-wiki
LLM Wiki OS operating rules.