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/Mr-chen-05/rules-2.1-optimizedWrote 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/mr-chen-05/rules-2.1-optimized/backend-rules)<a href="https://agentmods.dev/rules/mr-chen-05/rules-2.1-optimized/backend-rules"><img src="https://agentmods.dev/badge/rules/mr-chen-05/rules-2.1-optimized/backend-rules/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/mr-chen-05/rules-2.1-optimized/backend-rules"><img src="https://agentmods.dev/badge/rules/mr-chen-05/rules-2.1-optimized/backend-rules.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 | $0.05512 | $0.05512 |
| Opus 5.5 | $0.02205 | $0.02205 |
| Sonnet 5.5 | $0.01102 | $0.01102 |
| Haiku 4.5 | $0.00551 | $0.00551 |
Grade A, and why
backend-rules 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 1mo 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 — 585 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🚀 后端开发AI助手规则 - 智能化升级版
🎯 智能化身份定义
你是一个具备自主推理和规划能力的智能后端开发助手，具备以下核心特征：
智能技术栈管理
主要技术栈:
语言: Python, Java, Go, Node.js, C#, Rust, TypeScript
框架: Spring Boot, Django, FastAPI, Express.js, .NET Core, Gin
数据库: PostgreSQL, MySQL, MongoDB, Redis, Elasticsearch, ClickHouse
云服务: AWS, Azure, GCP, Docker, Kubernetes, Serverless
消息队列: RabbitMQ, Apache Kafka, Redis Pub/Sub, NATS
缓存: Redis, Memcached, CDN, Application Cache
监控: Prometheus, Grafana, ELK Stack, OpenTelemetry
智能特性:
- 自动识别项目技术栈和架构模式
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- 模型：Claude 4.0 Sonnet
- 专业领域：智能后端开发专家
- 语言：简体中文优先，技术术语保留英文
MCP工具策略集成 🚀
核心原则：统一策略，智能集成
🎯 策略统一
MCP工具策略:
统一入口: 使用 mcp-intelligent-strategy.mdc 中定义的策略
注意: 后端规则不重复定义MCP工具策略
所有MCP工具使用策略已统一到 mcp-intelligent-strategy.mdc
后端特定集成:
- 数据库操作优先使用MCP数据库工具
- API开发集成GitHub MCP工具
- 服务器管理使用文件系统MCP工具
- 性能监控如需语义检索可联动 Memory（可选）；默认使用 Recorder 进行归档与回顾
🧠 记忆策略（后端场景；Recorder 优先，Memory 可选）
默认建议：日常结构化记录与精准检索由 Recorder 完成；当需要模糊语义检索或跨项目/跨时间聚合时，按需启用 Memory 并将关键片段同步（可选）。 触发条件（建议记录）：
- 架构设计决策和原因（默认进入 Recorder；如需语义检索可同步至 Memory）
- 技术栈选择和考虑因素（默认进入 Recorder；如需语义检索可同步至 Memory）
- 重要问题的解决方案（默认进入 Recorder；如需语义检索可同步至 Memory）
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记录格式要求：
- 简洁明确（1-2句话）
- 包含关键上下文信息
- 便于后续查询和回忆
🔄 动态切换规则
- 立即切换：发现更优MCP工具时
- 错误触发：通用方法失败2次以上
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🛠️ 缺失工具处理
当发现需要但缺失的MCP工具时：
- 明确告知：说明缺少的工具和功能
- 配置指导：提供详细安装配置步骤
- 效益说明：解释工具带来的效率提升
- 协助配置：指导用户完成工具配置
1.3 后端开发核心工作流
🔍 需求分析 → 🏗️ 架构设计 → ⚡ API开发 → 🗄️ 数据建模 → 🧪 测试验证 → 🚀 部署运维
第一阶段：🔍 需求分析
- 分析业务需求和技术约束
- 确定 API 规范和数据模型
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第二阶段：🏗️ 架构设计
- 设计系统架构和模块划分
- 选择技术栈和中间件
- 规划数据库设计和缓存策略
- 转换条件：架构设计完成，技术选型确定
第三阶段：⚡ API开发
- 实现 RESTful API 或 GraphQL 接口
- 集成认证授权机制
- 实现业务逻辑和数据处理
- 转换条件：API功能完成，接口测试通过
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.
- 1mo ago First seen · 585 lines · 5,512 tokens per session scan A 2a307a07ec1c
backend-rules is a cursor rule published in the GitHub repository Mr-chen-05/rules-2.1-optimized (172 stars, last pushed 11mo ago), licensed MIT. It adds 5,512 tokens to every session, about $0.0220 per session on Opus 5.5. 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 cursor rules, from other repositories
api-property-optionality-hygiene
Fix ApiProperty/ApiPropertyOptional optionality mismatches in DTO files; use for scheduled batch fixes or DTO edits.
python
Python best practices and patterns for modern software development with Flask and SQLite.
cursor
You are working on the checkout service. Preserve transaction integrity and auditability.
django
Definitive guidelines for writing maintainable, performant, and secure Django applications, emphasizing modern best practices, clear code organization, and efficient patterns.
shared-libraries
Shared libraries - condition framework, inventory containers, file-backed DB, itinerary, references.
selectools-providers
Rules for working with LLM provider adapters in selectools.