memory-bank-taskmaster-cn

memory-bank-taskmaster-cn is a cursor rule for Cursor from XingjianTao/Cursor-Rules-for-PyTorch-DeepLearning-Beginner. It costs 1,975 tokens per session, scanned A, original, MIT.

A project memory system for Cursor, an AI coding tool, that uses linked Markdown files and can also track work with Taskmaster, a task-management tool. It records project context, technical decisions, current work, progress, and task status.

In plain words
What is it for?
Maintain files such as projectbrief.md, productContext.md, systemPatterns.md, techContext.md, activeContext.md, and progress.md. Keep activeContext.md updated when Taskmaster task statuses or project priorities change.
Why use it?
Cursor forgets earlier sessions, so important decisions and next steps can otherwise be lost. Keeping the memory files and task list synchronized gives later sessions a reliable starting point.

Cursor rule for Cursor

Written for Cursor: a Cursor rule (.mdc).

Good fit Maintain files such as projectbrief.md, productContext.md, systemPatterns.md, techContext.md, activeContext.md, and progress.md. Keep activeContext.md updated when Taskmaster task statuses or project priorities change.

Compare 6 cursor rules from other repositories ↓
Install with agentmods
npx agentmods add rules/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/memory-bank-taskmaster-cn
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.

Clone the repo
git clone --depth 1 https://github.com/XingjianTao/Cursor-Rules-for-PyTorch-DeepLearning-Beginner

Made for: Cursor.

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 memory-bank-taskmaster-cn

README.md
[![agentmods](https://agentmods.dev/badge/rules/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/memory-bank-taskmaster-cn.svg)](https://agentmods.dev/rules/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/memory-bank-taskmaster-cn)
Your own site
<a href="https://agentmods.dev/rules/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/memory-bank-taskmaster-cn"><img src="https://agentmods.dev/badge/rules/xingjiantao/cursor-rules-for-pytorch-deeplearning-beginner/memory-bank-taskmaster-cn.svg" alt="Measured on agentmods" height="20"></a>
Per session 1,975 This file is loaded in full into every session.
When invoked 1,975 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found 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 5.1 $0.01975 $0.01975
Opus 5 $0.00988 $0.00988
Sonnet 5 $0.00395 $0.00395
Haiku 4.5 $0.00198 $0.00198

Measured 8d ago against content hash b15b4b4fe9ab, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

memory-bank-taskmaster-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.

Original-cn/memory-bank-taskmaster-cn.mdc · 208 lines

How it starts

The opening of the file, as written. The whole thing — 208 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,一位专业软件工程师,拥有一个独特的特性:我的记忆在会话之间完全重置。这不是一个限制——这正是我保持完美文档的动力。每次重置后,我完全依赖我的记忆库来理解项目并有效地继续工作。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。文件按明确的层次结构构建:

flowchart TD
    PB[projectbrief.md] --> PC[productContext.md]
    PB --> SP[systemPatterns.md]
    PB --> TC[techContext.md]
    
    PC --> AC[activeContext.md]
    SP --> AC
    TC --> AC
    
    PB & PC & SP & TC --> PRD[prd.txt]

核心文件(必需)

  1. projectbrief.md

    • 塑造所有其他文件的基础文档
    • 在项目开始时若不存在则创建
    • 定义核心需求和目标
    • 项目范围的真实来源
  2. productContext.md

    • 这个项目存在的原因
    • 解决的问题
    • 它应该如何工作
    • 用户体验目标
  3. activeContext.md

    • 当前工作重点
    • 最近的变更
    • 下一步计划
    • 活跃的决策和考量
  4. systemPatterns.md

    • 系统架构
    • 关键技术决策
    • 使用的设计模式
    • 组件关系
  5. techContext.md

    • 使用的技术
    • 开发设置
    • 技术约束
    • 依赖关系

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
  • 部署程序

Taskmaster工具同步

当使用Taskmaster工具(MCP或task-master CLI命令)时,Taskmaster工具和记忆库必须保持同步。尤其是关于任务状态管理,必须严格遵守以下原则:

任务状态变更流程

变更任务状态时,必须执行以下步骤:

  1. 使用Taskmaster命令(例如set_task_status)更改任务状态
  2. 更新activeContext.md文件
    • 反映当前任务关注点的变化
    • 更新下一步和考量

同步检查清单

在任何任务相关操作后,始终检查以下项目:

  • Taskmaster中的任务状态是否正确更改?
  • activeContext.md中是否更新了当前任务的重点?
  • 如果任务之间的依赖关系有变化,是否反映在文档中?

定期一致性检查

每次开始或结束工作会话时,检查Taskmaster的状态是否与记忆库的内容匹配:

# 检查任务状态
task-master list
# 通过比较activeContext.md检查一致性

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 --> SyncCheck[检查:与Taskmaster的同步状态]
    SyncCheck --> Update[更新文档]
    Update --> Rules[如需要更新.cursorrules]
    Rules --> Execute[执行任务]
    Execute --> UpdateStatus[更新任务状态]
    UpdateStatus --> SyncMemory[同步记忆库]
    SyncMemory --> Document[记录变更]

Read the full file on GitHub · 208 lines

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. 8d ago First seen · 208 lines · 1,975 tokens per session scan A b15b4b4fe9ab

Subscribe to this mod's changes

memory-bank-taskmaster-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 1,975 tokens to every session, about $0.0099 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.