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OpenAI 官方发布了一个 Claude Code 插件 codex-plugin-cc,让开发者可以直接在 Claude Code 里调用 Codex 做代码审查、对抗性审查,甚至把任务整个移交给 Codex 执行。 这件事有意思的地方在于:这是 OpenAI 主动把自己的工具送进竞争对手 Anthropic 的地盘。Claude Code 有自己的插件生态,OpenAI 这次正式以官方身份入场,把 Codex 包装成 Claude Code 工作流里的一个"随叫随到的第二意见"。 插件提供三个核心命令:/codex:review 跑一遍标准的只读代码审查;/codex:adversarial-review 做对抗性审查,专门挑战现有实现的隐藏假设,适合迁移、鉴权变更、基础设施脚本这类高风险操作;/codex:rescue 则直接把任务交给 Codex 接管,用于线程卡住或需要换个智能体重新来过的场景。 三个命令都支持后台运行,配合 /codex:status 和 /codex:result 管理。还有个可选的 review gate 功能,能让 Claude Code 在 Codex 审查完成前不退出,不过 Srivastav 提醒这可能导致两个智能体循环调用,快速烧掉使用额度。 技术上,插件通过本地 Codex CLI 和 app server 中转,复用已有的认证、配置和 MCP 设置,不额外起运行时。使用前提是有 ChatGPT 订阅(包括免费版)或 OpenAI API key,加上 Node.js 18.18 以上。
Claude 写代码、Codex 做审查,OpenAI 居然发布了官方 Claude Code 插件「codex-plugin-cc」支持,让 Claude Code 用户无需切换环境,就能直接调用 OpenAI 的 Codex 进行代码审查或任务委托 开源地址:github.com/openai/codex-p… 解决单一模型盲区 同一个模型自我审查容易“漏掉自己制造的假设”。Codex 作为第二 Agent,能提供独立视角,尤其在高风险场景(如迁移、重构、认证逻辑、基础设施脚本)中表现突出。 三种核心使用场景: 1. 常规审查(/codex:review):只读检查当前变更或分支差异,适合日常 PR 前置验证。 2. 对抗性审查(/codex:adversarial-review):可引导 Codex 主动质疑实现逻辑,而非仅找语法问题。支持额外 focus 指令(如“challenge caching design”),特别适合隐藏假设风险高的代码。 3. 救援委托(/codex:rescue):当 Claude 线程卡住时,直接把任务交给 Codex 接管,实现“不同 Agent 第二遍”。 额外实用功能 支持后台长任务(/codex:status、 /codex:result、 /codex:cancel),以及可选的“审查门禁”,强制 Claude 在 Codex 审查通过前不得退出,防止草率结束。 安装与使用 1. 在 Claude Code 中执行: /plugin marketplace add openai/codex-plugin-cc/plugin install codex@openai-codex/reload-plugins 2. 运行 /codex:setup(自动安装 Codex CLI,若 Node.js ≥ 18.18 已存在) 3. 若 Codex 未登录:执行 !codex login 4. 可选开启门禁:/codex:setup --enable-review-gate
大多数开发者用了 10 年 git,从没用过 worktree。 因为以前根本没必要——同时开 5 个分支?谁会这么干? AI 编程代理出现之后,这一切变了。 ─── 现在我同时跑 5-10 个 Claude Code / Codex session,每个代理处理一个任务。但问题来了:每个代理都需要独立的工作目录,不能互相踩文件。 解法:git worktree + pnpm 全局虚拟存储。 一个 bare 仓库作为中心,每个代理一个 worktree: git clone --bare github.com你的/仓库.git repo cd repo git worktree add ./main main git worktree add ./feat-a feat/task-a ← Agent A git worktree add ./fix-b fix/task-b ← Agent B git 对象只有一份,分支却全部同时 checkout。 ─── 但 node_modules 怎么办?每个 worktree 都要装一遍,几百 MB 乘以 10 个代理? pnpm 的 enableGlobalVirtualStore 解决了这个: # pnpm-workspace.yaml enableGlobalVirtualStore: true 开启之后,每个 worktree 的 node_modules 里只有符号链接,指向磁盘上同一份内容寻址存储。 第一次 pnpm install 下载包,后面每个新 worktree 的安装几乎是瞬时的。 10 个代理,磁盘开销接近于 1 个。 ─── pnpm 自己的仓库就是这套配置,还封装了辅助命令: pnpm worktree:new feat/my-feature # 新建 worktree pnpm worktree:new 10834 # 直接拉 PR,支持 fork 分支名的斜杠自动转成目录名,.claude 配置全局共享,省去重复设置。 ─── AI 代理时代的并行开发工作流,不是多开几个终端那么简单。 每个代理需要真正的隔离:独立文件、独立分支、独立依赖树。 git worktree + pnpm virtualstore,是目前最轻量的答案。 ─── 如果觉得有用,可以直接去看 pnpm 11.x 的文档:pnpm.io/11.x/git-workt…
小龙虾真正接近完美的标准 折腾一圈后,你会发现小龙虾要稳定,不是看你写了多少文件,也不是看你 prompt 多华丽。真正接近完美配置的标准,其实就三条。 第一,启动文件越短越好! 尤其是: MEMORY.md HEARTBEAT.md 这两个一长,系统马上变重。 因为它们属于高频加载层,越重越拖。 很多人会误以为写得越全越保险,但在 小龙虾这类架构里,高频加载文件的目标从来不是信息最多,而是信号最强。 所以长篇流水账不应该放这里。原始细节去 daily memory,这里保留蒸馏过的长期有效内容就好。 ⚠️⚠️ 第二,身份、用户、工具、流程必须彻底分层。也就是: - SOUL 只讲气质 - IDENTITY 只讲角色 - USER 只讲你是谁 - TOOLS 只讲能怎么动手 - AGENTS 只讲编排和启动流程 - MEMORY 只讲长期记忆 - HEARTBEAT 只讲轻巡检 不要混着写。一混,系统边界就开始溶掉。最后看起来什么都有,实际什么都不稳。 ⚠️⚠️ 第三,长期记忆靠提炼,不靠堆积。 这点特别关键。 OpenClaw 这套系统本质上,是把 bootstrap 文件当作每次会话都会重载的核心层。所以你不是在写一堆提示词备忘录,你是在设计一个稳定内核。 既然是内核,就不能一直往里堆杂物。 正确做法一定是: - daily memory 持续记录 - 定期把真正重要的内容提炼进 MEMORY.md - 原始日志负责保真 - 长期记忆负责高信号 这三条一旦抓住,很多问题都会自然消失: - 启动变快 - 行为更稳 - 角色更清楚 - 记忆不发散 - 长期维护成本更低 这也是为什么我会觉得,新手安装小龙虾后最先该学的,不是高级多 agent,不是五花八门,而是先把这套核心分层弄明白! 因为你后面装再多东西,如果内核是乱的、系统只会越来越重、越来越飘。
HEARTBEAT.md 不是第二个大脑,它只是轻量巡检器 很多人一看到 HEARTBEAT.md,就会想把它写得很厉害。什么自检,监控、异常修复、上下文维护、任务追踪,全部想塞进去。 但实际上,HEARTBEAT.md 最大的原则只有一个: 轻量! 它不是第二套主人格。不是第二本 AGENTS,也不是隐藏版 MEMORY。
首个为 AI 和自动化而设计的的无头浏览器 Lightpanda,刚开源就暴涨 14000+ GitHub Star! 不基于 Chromium,完全从零开始构建,运行速度比 Chrome 快 11 倍,内存占用少了 9 倍。 GitHub:github.com/lightpanda-io/… 如果有大规模爬取、构建 AI Agent,以及自动化操作 Web 等需求的朋友,可以看下这个项目。
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