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.
npx agentmods add rules/mr-chen-05/rules-2.1-optimized/root-cause-analysisgit clone --depth 1 https://github.com/Mr-chen-05/rules-2.1-optimizedWhat 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 | $0.04960 | $0.04960 |
| Opus 5 | $0.02480 | $0.02480 |
| Sonnet 5 | $0.00992 | $0.00992 |
| Haiku 4.5 | $0.00496 | $0.00496 |
Grade A, and why
root-cause-analysis 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 2d 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 — 701 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🔍 Root Cause Analysis - 智能根因分析
AI驱动的根因分析系统，集成五个为什么方法论、智能问题分类和解决方案推荐引擎。
🧠 AI Intelligence Core - AI智能核心
智能激活条件
自动激活场景:
- 检测到重复性问题或Bug
- 用户执行 "/root-cause" 命令
- code-quality-check发现严重问题
- 系统故障或异常发生
- 超级大脑系统推荐根因分析
智能分析维度:
- 问题复杂度和影响范围 (30%)
- 历史问题模式匹配 (25%)
- 系统关联性分析 (20%)
- 解决方案可行性 (15%)
- 预防措施有效性 (10%)
🚀 Commands - 智能命令
/root-cause- AI智能根因分析（推荐）/root-cause --issue <description>- 分析指定问题/root-cause --five-whys- 使用五个为什么方法/root-cause --pattern- 模式识别分析/root-cause --solution- 生成解决方案建议/root-cause --prevent- 生成预防措施
✨ AI-Powered Features - AI驱动功能
🤖 智能问题分析引擎
问题识别:
技术问题:
- 代码Bug和逻辑错误
- 性能瓶颈和资源问题
- 安全漏洞和风险
- 架构设计缺陷
- 依赖冲突和版本问题
流程问题:
- 开发流程不规范
- 测试覆盖不足
- 部署流程问题
- 团队协作障碍
- 文档维护不及时
环境问题:
- 开发环境配置
- 生产环境差异
- 第三方服务依赖
- 网络连接问题
- 硬件资源限制
智能分类:
- 根本原因 vs 表面现象
- 系统性问题 vs 偶发问题
- 技术问题 vs 管理问题
- 内部问题 vs 外部依赖
🔧 五个为什么方法论
AI增强的五个为什么:
Why 1 - 现象识别:
- 详细描述问题现象
- 收集相关数据和日志
- 确定问题影响范围
- 建立问题时间线
Why 2 - 直接原因:
- 分析直接触发因素
- 识别相关系统组件
- 检查配置和设置
- 分析用户操作
Why 3 - 系统原因:
- 分析系统设计缺陷
- 检查流程和规范
- 识别架构问题
- 分析依赖关系
Why 4 - 管理原因:
- 分析管理流程
- 检查团队协作
- 识别资源分配
- 分析决策过程
Why 5 - 根本原因:
- 识别根本性问题
- 分析文化和理念
- 检查组织结构
- 确定改进方向
📊 智能模式识别
历史模式分析:
- 相似问题识别
- 问题发生频率分析
- 解决方案效果评估
- 预防措施有效性
关联性分析:
- 问题间的因果关系
- 系统组件关联性
- 时间序列相关性
- 环境因素影响
预测性分析:
- 潜在问题预警
- 风险评估和量化
- 影响范围预测
- 解决时间估算
🔄 AI Workflow Process - AI工作流程
Phase 1: 问题收集阶段
Step 1 - 问题定义:
- 收集问题描述和现象
- 整理相关日志和数据
- 确定问题严重级别
- 建立问题档案
Step 2 - 环境分析:
- 分析问题发生环境
- 检查系统配置状态
- 收集相关依赖信息
- 建立环境快照
Step 3 - 数据收集:
- 收集错误日志和堆栈
- 获取性能监控数据
- 收集用户操作记录
- 整理相关文档
Phase 2: 智能分析阶段
Step 4 - AI分析:
- 执行智能问题分类
- 进行模式匹配分析
- 识别关联性和依赖
- 生成分析假设
Step 5 - 五个为什么:
- 引导式问题深挖
- AI辅助原因推理
- 验证分析假设
- 确定根本原因
Step 6 - 验证分析:
- 验证根因分析结果
- 检查逻辑一致性
- 评估解决方案可行性
- 确认分析完整性
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.
- 2d ago First seen · 701 lines · 4,960 tokens per session scan A 97d903fbfb7d
root-cause-analysis is a cursor rule published in the GitHub repository Mr-chen-05/rules-2.1-optimized (172 stars, last pushed 9mo ago), licensed MIT. It adds 4,960 tokens to every session, about $0.0248 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 2026-08-30.
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