根据您提供的社区讨论，我深入分析了在大型 monorepo 中管理 AI 助手（Claude Code、Cursor、GitHub Copilot 等）配置与规则文件的核心挑战和最佳实践。以下是结构化的总结和建议。
CLAUDE.md, .cursor/rules/, copilot-instructions.md, AGENTS.md），导致重复和维护困难。
| Using LLMs to Analyze a Large C++ Codebase for Defects | |
| Challenges in Large-Scale Code Scanning | |
| Scanning a decade-old C++ codebase for subtle defects (like an upstream variable assignment in an exception branch causing downstream failures) is non-trivial. Large Language Models (LLMs) can understand code logic well when given the right context, but providing that context across a huge codebase is challenging | |
| levelup.gitconnected.com | |
| levelup.gitconnected.com | |
| . A naive approach (e.g. dumping all code into a prompt) is impossible due to context length limits, and pure semantic search may miss critical flows. The key is to break down the analysis using structured techniques so the LLM sees the relevant call chains and data flows without hallucinating or overlooking dependencies. Recent studies and experiences indicate that combining static code structure with LLM reasoning is essential for accurate results | |
| dev.to | |
| siquick.com | |
| . | |
| Graph-Based Approaches (Call Graphs & Knowledge Graphs) |
| https://github.blog/developer-skills/github/completing-urgent-fixes-anywhere-with-github-copilot-coding-agent-and-mobile/ | |
| This included the core purpose of the repository, the tech stack used, architecture constraints, coding standards, testing strategy, dependency management, observability, documentation, error handling, and more. | |
| https://github.com/github/awesome-copilot | |
| https://blog.cloudflare.com/introducing-react-why-we-built-an-elite-incident-response-team/ | |
| Vibe coding：向 AI 模型提供自然语言提示以生成代码的实践可能会产生关键漏洞，这些漏洞可以被利用者通过远程代码执行（RCE）、内存损坏和 SQL 注入等技术进行利用。 | |
| https://simonwillison.net/2025/Oct/7/vibe-engineering/#atom-everything |
| [ | |
| { | |
| "id": 1754581181413, | |
| "name": "播客中文总结", | |
| "system": "你是一位有帮助的助手。", | |
| "before": "我会发送给你一段播客转录，其中可能存在拼写错误，尽量理解即可。", | |
| "after": "深入总结以上播客内容，简体中文输出。" | |
| }, | |
| { | |
| "id": 1754581181414, |
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>TypeScript Match Engine Playground</title> | |
| <style> | |
| body { font-family: sans-serif; display: flex; flex-direction: column; height: 100vh; margin: 0; } | |
| .main-container { display: flex; flex: 1; overflow: hidden; } /* Changed from .container to .main-container */ |
| import os | |
| import json | |
| import time | |
| from datetime import datetime | |
| from openai import OpenAI | |
| import argparse | |
| from typing import Dict, List, Any, Optional, Union, TypedDict, cast | |
| from dataclasses import dataclass | |
| @dataclass |
original: https://github.com/wujianguo/openai-proxy
和 pyhttpdbg 一起用，可以观察app到底给大模型发送了什么
pyhttpdbg --script proxy.py
| createWithNestedDefaults = (messageType) => { | |
| const message = messageType.create(); | |
| console.log(message); | |
| // 遍历消息的属性 | |
| for (const [key, field] of Object.entries(messageType.fields)) { | |
| // 检查该属性是否是嵌套的消息类型 | |
| console.log(field) |
| // ==UserScript== | |
| // @name Mermaid Diagram Renderer | |
| // @namespace http://tampermonkey.net/ | |
| // @version 0.1 | |
| // @description Render Mermaid diagrams from selected text | |
| // @author Claude | |
| // @match *://*/* | |
| // @require https://cdnjs.cloudflare.com/ajax/libs/mermaid/8.14.0/mermaid.min.js | |
| // @grant GM_addStyle | |
| // ==/UserScript== |