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/context-loader)<a href="https://agentmods.dev/rules/mr-chen-05/rules-2.1-optimized/context-loader"><img src="https://agentmods.dev/badge/rules/mr-chen-05/rules-2.1-optimized/context-loader/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/context-loader"><img src="https://agentmods.dev/badge/rules/mr-chen-05/rules-2.1-optimized/context-loader.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 | $0.04705 | $0.04705 |
| Opus 5.5 | $0.01882 | $0.01882 |
| Sonnet 5.5 | $0.00941 | $0.00941 |
| Haiku 4.5 | $0.00470 | $0.00470 |
Grade A, and why
context-loader 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 — 639 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🧠 Context Loader - 智能项目上下文加载器
AI驱动的智能项目上下文分析和加载系统，超越传统上下文加载，提供深度项目理解和智能配置推荐。
🧠 AI Intelligence Core - AI智能核心
智能激活条件
自动激活场景:
- 检测到新项目或项目结构变化
- 用户执行 "/load-context" 命令
- 超级大脑系统推荐上下文分析
- AI助手首次接触项目时
- 项目配置发生重大变更
智能分析维度:
- 项目类型和技术栈识别 (35%)
- 代码结构和架构模式 (25%)
- 开发工具和配置分析 (20%)
- 团队协作模式识别 (15%)
- 项目成熟度评估 (5%)
🚀 Commands - 智能命令
/load-context- AI智能项目上下文加载（推荐）/load-context --deep- 深度分析模式（详细项目分析）/load-context --quick- 快速分析模式（基础信息）/load-context --tech-stack- 技术栈专项分析/load-context --team- 团队协作模式分析/load-context --config- 配置和工具分析
✨ AI-Powered Features - AI驱动功能
🤖 智能项目识别引擎
项目类型识别:
前端项目:
- React/Next.js项目 (package.json + React依赖)
- Vue.js项目 (vue.config.js + Vue依赖)
- Angular项目 (angular.json + Angular CLI)
- 静态网站 (HTML/CSS/JS结构)
- 移动端应用 (React Native/Flutter)
后端项目:
- Node.js项目 (package.json + Express/Koa)
- Python项目 (requirements.txt + Django/Flask)
- Java项目 (pom.xml/build.gradle + Spring)
- .NET项目 (.csproj + ASP.NET)
- Go项目 (go.mod + Gin/Echo)
全栈项目:
- Monorepo结构识别
- 微服务架构检测
- JAMstack项目识别
- 容器化项目分析
特殊项目:
- 开源库项目
- 文档项目
- 配置项目
- 脚本工具项目
技术栈深度分析:
- 主要框架和库识别
- 开发工具链分析
- 构建和部署配置
- 测试框架和策略
- 数据库和存储方案
🔧 智能配置推荐引擎
配置分析:
开发环境:
- IDE配置文件分析
- 编辑器设置检测
- 插件和扩展识别
- 调试配置分析
构建工具:
- Webpack/Vite/Rollup配置
- Babel/TypeScript配置
- ESLint/Prettier设置
- 测试框架配置
部署配置:
- Docker/Kubernetes配置
- CI/CD流水线设置
- 云服务配置
- 环境变量管理
智能推荐:
- 缺失配置自动识别
- 最佳实践配置建议
- 性能优化配置推荐
- 安全配置增强建议
📊 项目健康度评估
健康度指标:
代码质量:
- 代码覆盖率分析
- 技术债务评估
- 代码复杂度统计
- 安全漏洞扫描
项目管理:
- 文档完整性检查
- 版本管理规范性
- 依赖管理健康度
- 测试策略完整性
团队协作:
- 代码审查流程
- 提交规范性
- 分支管理策略
- 问题跟踪效率
评分体系:
- A级 (90-100分): 优秀项目
- B级 (80-89分): 良好项目
- C级 (70-79分): 一般项目
- D级 (60-69分): 需改进项目
- F级 (<60分): 问题项目
🔄 AI Workflow Process - AI工作流程
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 · 639 lines · 4,705 tokens per session scan A 1ce434af3aac
context-loader 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 4,705 tokens to every session, about $0.0188 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
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…
self-improving-obsidian-llm-wiki
LLM Wiki OS operating rules.
memsprout
Cursor rule "memsprout" from memsprout/agents, covering using memsprout, search first, capturing, spaces and topics and files.