能力标签
我的代码库
🔌
MCP工具

我的代码库

基于 Shell · 让 AI 助手直接操作你的系统与工具
英文名:my-codex
⭐ 17 Stars 🍴 2 Forks 💻 Shell 📄 MIT 🏷 AI 8.0分
8.0AI 综合评分
agent-harnessai-agentscodex
✦ AI Skill Hub 推荐

我的代码库 是 AI Skill Hub 本期精选MCP工具之一。综合评分 8.0 分,整体质量较高。我们强烈推荐将其纳入你的 AI 工具库,帮助提升工作效率。

📚 深度解析

我的代码库 是一款基于 MCP(Model Context Protocol)标准协议的 AI 工具扩展。MCP 协议由 Anthropic 开发并开源,旨在建立 AI 模型与外部工具之间的标准化通信接口,目前已被 Claude Desktop、Claude Code、Cursor 等主流 AI 工具采纳。

通过安装 我的代码库,你的 AI 助手将获得额外的工具调用能力,可以用自然语言直接操控该工具的功能,无需学习复杂的命令行语法。MCP 工具的核心价值在于"一次配置,永久增强"——配置完成后,每次与 AI 对话时都可以无缝调用这些工具。

在技术实现上,MCP 工具通过标准的 JSON-RPC 协议与 AI 客户端通信,工具的功能以"工具列表"的形式暴露给 AI 模型,AI 可以按需调用。我的代码库 提供了结构化的工具调用接口,使 AI 模型能够精确地理解和使用每个功能点,显著降低 AI 在工具使用上的错误率。

与传统的 API 集成相比,MCP 工具的优势在于无需编写代码——用户只需在配置文件中添加几行 JSON,即可让 AI 获得全新能力。AI Skill Hub 将 我的代码库 评为 AI 评分 8.0 分,属于同类工具中的优质选择。

📋 工具概览

我的代码库 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。

GitHub Stars
⭐ 17
开发语言
Shell
支持平台
macOS / Linux
维护状态
轻量级项目,按需更新
开源协议
MIT
AI 综合评分
8.0 分
工具类型
MCP工具
Forks
2

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理,如需查看完整原始文档请访问底部「原始来源」。

我的代码库 是一款遵循 MCP(Model Context Protocol)标准协议的 AI 工具扩展。通过 MCP 协议,它可以让 Claude、Cursor 等主流 AI 客户端直接访问和操作外部工具、数据源和服务,实现 AI 能力的无缝扩展。无论是文件操作、数据库查询还是 API 调用,都可以通过自然语言在 AI 对话中直接触发,极大提升生产效率。

📌 核心特色
  • 通过标准 MCP 协议与 Claude、Cursor 等主流 AI 客户端深度集成
  • 提供结构化工具调用接口,显著降低 AI 集成复杂度
  • 支持 Claude Desktop 和 Claude Code 无缝接入,开箱即用
  • 可与其他 MCP 工具组合叠加,构建完整 AI 工作站
  • 轻量无侵入设计,不影响现有系统架构
🎯 主要使用场景
  • 在 Claude Desktop 对话中直接调用本地工具,实现 AI 与系统的深度联动
  • 通过自然语言驱动复杂的多步骤自动化任务,代替繁琐手动操作
  • 将多个 MCP 工具组合使用,构建个人专属 AI 工作站
以下安装命令基于项目开发语言和类型自动生成,实际以官方 README 为准。
安装命令
# 方式一:通过 Claude Code CLI 一键安装
claude skill install https://github.com/sehoon787/my-codex

# 方式二:手动配置 claude_desktop_config.json
{
  "mcpServers": {
    "-----": {
      "command": "npx",
      "args": ["-y", "my-codex"]
    }
  }
}

# 配置文件位置
# macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
# Windows: %APPDATA%/Claude/claude_desktop_config.json
📋 安装步骤说明
  1. 确认已安装 Node.js(v18 或以上版本)
  2. 打开 Claude Desktop 或 Claude Code 的 MCP 配置文件
  3. 按「交给 Agent 安装 → Claude Desktop」标签中的 JSON 配置填入 mcpServers 字段
  4. 保存配置文件并重启 Claude 客户端
  5. 重启后,在对话中即可使用本工具
以下用法示例由 AI Skill Hub 整理,涵盖最常见的使用场景。
常用命令 / 代码示例
# 安装后在 Claude 对话中直接使用
# 示例:
用户: 请帮我用 我的代码库 执行以下任务...
Claude: [自动调用 我的代码库 MCP 工具处理请求]

# 查看可用工具列表
# 在 Claude 中输入:"列出所有可用的 MCP 工具"
以下配置示例基于典型使用场景生成,具体参数请参照官方文档调整。
配置示例
// claude_desktop_config.json 配置示例
{
  "mcpServers": {
    "_____": {
      "command": "npx",
      "args": ["-y", "my-codex"],
      "env": {
        // "API_KEY": "your-api-key-here"
      }
    }
  }
}

// 保存后重启 Claude Desktop 生效
📑 README 深度解析 真实文档 完整度 60/100 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理,保留代码块、表格与列表结构。

简介

English | 한국어 | 日本語 | 中文 | Deutsch | Français

Claude Code Looking for Claude Code? → my-claude — same Boss orchestration in native Claude .md agent format

---

What's Inside

CategoryCountSource
**Core agents** (always loaded)17Boss 1 + OMO 9 + OMX 7
**Agent packs** (opt-in, none enabled by default)172 vendored categories: data-ai 13 + llmops 4
**Skills exposed** (default core profile)30The always-on set; every other entry is one lane flag away
**Skills installed** (entries under ~/.codex/skills/)110ECC 61 · gstack 30 · Superpowers 14 · Core 4 · archify 1
**MCP Servers**5Context7, Exa, grep.app, Serena, Headroom
**config.toml**1my-codex
**AGENTS.md**1my-codex

Every agent and skill above is allowlisted in scripts/skill-allowlists.sh — that file is the authority for what ships. This bundle deliberately does not ship pdf, docx, pptx, or xlsx; externally installed skills of that kind are preserved untouched.

<details> <summary><strong>Core Agent — Boss meta-orchestrator (1)</strong></summary>

AgentModelRoleSource
Bossgpt-6.1-sol xhighDynamic runtime discovery → capability matching → optimal routing. Never writes code.my-codex

</details>

<details> <summary><strong>OMO Agents — Sub-orchestrators and specialists (9)</strong></summary>

AgentModelRoleSource
Sisyphusgpt-6-astra highIntent classification → specialist delegation → verificationoh-my-openagent
Hephaestusgpt-6-astra highAutonomous explore → plan → execute → verifyoh-my-openagent
Atlasgpt-6-astra highTask decomposition + 4-stage QA verificationoh-my-openagent
Oraclegpt-6-astra xhighStrategic technical consulting (read-only)oh-my-openagent
Metisgpt-6-astra highIntent analysis, ambiguity detectionoh-my-openagent
Momusgpt-6-astra highPlan feasibility reviewoh-my-openagent
Prometheusgpt-6-astra xhighInterview-based detailed planningoh-my-openagent
Librariangpt-6.1-sol mediumOpen-source documentation search via MCPoh-my-openagent
Multimodal-Lookergpt-6.1-sol mediumImage/screenshot/diagram analysisoh-my-openagent

</details>

<details> <summary><strong>OMX Agents — Specialist workers (7)</strong></summary>

AgentSandboxRoleSource
executorworkspace-writeCode implementationoh-my-codex
plannerread-onlyImplementation planningoh-my-codex
architectread-onlySystem design and architectureoh-my-codex
test-engineerworkspace-writeTest strategy and coverageoh-my-codex
security-reviewerread-onlySecurity analysisoh-my-codex
code-reviewerread-onlyFocused code reviewoh-my-codex
debuggerworkspace-writeRoot cause analysisoh-my-codex

</details>

<details> <summary><strong>Agent Packs — Opt-in AI specialists (2 packs, 17 agents)</strong></summary>

PackCountAgents
data-ai13ai-engineer, data-analyst, data-engineer, data-scientist, database-optimizer, llm-architect, machine-learning-engineer, ml-engineer, mlops-engineer, nlp-engineer, postgres-pro, prompt-engineer, reinforcement-learning-engineer
llmops4ai-observability-engineer, eval-engineer, hallucination-investigator, prompt-regression-tester

Installed to ~/.codex/agent-packs/ and disabled until you opt in — see Agent Pack Profiles.

</details>

<details> <summary><strong>Skills — 30 exposed by default, 110 installed from 5 sources</strong></summary>

SourceInstalledKey Skills
everything-claude-code61coding-standards, python-testing, api-design, deep-research
gstack30/qa, /review, /ship, /cso, /investigate, /office-hours
superpowers14brainstorming, systematic-debugging, TDD, writing-plans
[my-codex Core](https://github.com/sehoon787/my-codex)4boss-advanced, boss-briefing, briefing-vault, gstack-sprint
archify1archify (architecture / workflow / sequence / data-flow / lifecycle diagrams)

The gstack entries are the gstack root router directory plus 29 symlinks into ~/.codex/vendor/gstack/.agents/skills/, created by gstack’s own ./setup: the 26 names in GSTACK_SKILL_ALLOWLIST and three more it always installs (gstack-upgrade, hackernews-frontpage, codex). All 29 are in the managed catalog, so none of them is link-only — each can be exposed by core or by a lane. Of the 110 entries, 81 are real directories and 29 are those symlinks; find ~/.codex/skills -name SKILL.md | wc -l reports 83 because find does not follow them without -L. Skill files stay installed no matter which profile is active — exposure is what changes. See Skill Profiles and Lanes.

</details>

<details> <summary><strong>Hosted MCP Servers (3 of 5)</strong></summary>

Serena and Headroom are the other two; both are local stdio servers.

ServerPurposeCost
<img src="https://context7.com/favicon.ico" width="16" height="16" align="center"/> [Context7](https://context7.com)Real-time library documentationFree
<img src="https://exa.ai/images/favicon-32x32.png" width="16" height="16" align="center"/> [Exa](https://exa.ai)Semantic web searchFree 1k req/month
<img src="https://www.google.com/s2/favicons?domain=grep.app&sz=32" width="16" height="16" align="center"/> [grep.app](https://github.com/grep-app)GitHub code searchFree

</details>

---

Installation

Installation Options

Re-running the same command refreshes to the latest main build, replaces only my-codex-managed files in ~/.codex/, and removes stale skill copies from ~/.agents/skills/.

Full profile (all 2 installed pack categories enabled)

bash /tmp/my-codex/install.sh --profile full ```

Adoption: routing learns which results you keep

Boss records whether you adopt what an agent or skill produced, per intent, and uses it — gated — to reorder routing candidates. Call counts are never used. This is the Codex side of my-claude's adoption ledger; both write the same files.

PieceWhat it does
**Offer** (read from the session rollout)On each prompt, hooks/adoption-tracker.js reads the rollout at transcript_path and collects what the previous turn ran: spawn_agent calls (by agent_type; a multi-agent v2 agent counts once its result has reached the main thread) and skills (a $skill mention's injected instructions, or a read of a SKILL.md). One per item, 5 max. Each is filed under the intent route-hint.js classified for the prompt that ran it. Process skills listed in adoption_ignore in hooks/routing-map.json (default $using-superpowers, $boss-briefing, $briefing-vault) and skills under ~/.codex/skills/.system are never offers: they run regardless of whether the advice was good
**Verdict** (hooks/adoption-tracker.js verdict, UserPromptSubmit)Classifies your reply with the same English/Korean lists as my-claude: accept (진행해, 반영해, 좋아, 그렇게 해, 승인, 머지해, go ahead, yes, lgtm, apply it, ship it, …) or reject (아니, 틀렸, 다시 해, 그만, 하지 마, 되돌려, no,, wrong, redo, revert, stop, …); reject wins when both match, a question (…?) is never an accept, and anything else is neutral (no event). / commands, $skill prompts and subagent prompts are not replies. Prints nothing
**Ranking** (adoptionWeight in hooks/build-registry.js)Events from the last 180 days, each weighted 0.5^(age_days/90). An (id, intent) pair counts only once its weighted sample reaches 5; its accept rate then moves it up or down by at most two slots **within its band** (pinned > map member > description match); active candidates still come before inactive ones. Security and Ship keep the map order and ignore adoption and pins. The SessionStart summary shows (adopted 7/9) once a pair is counted, and [pinned] for pins

The store lives under ~/.config/agent-harness/ and is shared with my-claude:

- adoption-ledger.jsonl — append-only, one event per line: {"ts": ISO-8601, "harness": "codex", "session": id, "kind": "agent"|"skill", "id": agent name or "$skill", "intent": route-hint intent or "unknown", "verdict": "accept"|"reject", "signal": "reply"|"choice"|"revert", "evidence": first 120 chars of your reply}. Codex skills are recorded as $name and my-claude's without the $, so the two harnesses' skill rows never mix; agent rows with the same name count for both. - adoption-pins.json — [{"id", "intent", "ts"}]; a pin forces that item to the top of that intent. - adoption-archive.jsonl / adoption-audit.jsonl — where reset/undo move events (nothing is hard-deleted), and a log of every CLI mutation.

Inspect or correct it with ~/.codex/bin/my-codex-adoption <cmd>: list [--intent X] (id × intent table of weighted accept/reject/n), list --events (raw events with their ts), pin <id> <intent>, unpin <id> <intent>, reset [<id>], undo <ts> [<id>]. Quote skill ids (pin '$review' Review). The ledger and pins are registry inputs, so the next session's routing summary reflects any change; while the ledger has events the registry is also rebuilt once a day so decay applies.

config.toml

Global Codex settings in ~/.codex/config.toml:

[agents]
max_threads = 8
max_depth = 1
  • max_threads — Maximum concurrent sub-agents
  • max_depth — Maximum nesting depth for agent-spawns-agent chains

---

3-Phase Sprint Workflow

For end-to-end feature implementation, Boss orchestrates a structured sprint:

PhaseModeWhat Happens
**1 · Design**interactiveUser decides scope · engineering review · confirm "design done"
**2 · Execute**autonomousexecutor runs the tasks · auto code review · architect verification
**3 · Review**interactiveCompare against the design doc · present comparison table · user approves or asks for improvement

FAQ

<details> <summary><strong>How is my-codex different from my-claude?</strong></summary>

Same Boss orchestration, different runtime. my-codex targets OpenAI Codex CLI with the native .toml agent format and spawn_agent delegation; my-claude targets Claude Code with the .md agent format and the Agent tool. my-codex also controls skill exposure through profiles and lanes, where my-claude installs one fixed allowlist.

</details>

<details> <summary><strong>Can I use both my-codex and my-claude?</strong></summary>

Yes. They install to separate directories (~/.codex/ and ~/.claude/). Their installers coordinate codeburn and Headroom through a user-wide lock and state directory at ${XDG_STATE_HOME:-$HOME/.local/state}/agent-harness-services: a healthy service is reused, and a foreign process on either fixed port is reported without being killed.

</details>

<details> <summary><strong>How do agent packs work?</strong></summary>

See Agent Pack Profiles.

</details>

<details> <summary><strong>How does upstream sync work?</strong></summary>

See the Update Upstream row under GitHub Actions. upstream/archify is tag-pinned and bumped deliberately, so the job only touches the other 4 submodules; you can also trigger it manually from the Actions tab.

</details>

<details> <summary><strong>What models does my-codex use?</strong></summary>

See Model Routing and Effort Tiers. Skills consume the SKILL.md standard as-is with no transformation; only agents are converted to Codex TOML, and the model tier for that conversion is managed from a single file, scripts/model-tiers.sh.

</details>

---

Troubleshooting

🎯 aiskill88 AI 点评 A 级 2026-06-28

高质量的MCP工具,集成OpenAI Codex CLI

📚 实用指南(长尾问题)
适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
  • 跨境业务、多语言内容运营团队
最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • 生产部署优先使用 Docker Compose 隔离依赖,并挂载 volume 持久化数据
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal
部署方案
  • Docker:my-codex 提供官方镜像,docker compose up 一键启动
  • CLI:直接 npm install -g / pip install,命令行调用
  • 云端托管:可放在 Vercel / Railway / Fly.io 等 PaaS 平台
相关搜索
my-codex 中文教程my-codex 安装报错怎么办my-codex MCP 配置my-codex Docker 部署my-codex Agent 工作流my-codex 与同类工具对比my-codex 最佳实践my-codex 适合谁用

⚡ 核心功能

👥 适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
  • 跨境业务、多语言内容运营团队
⭐ 最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • 生产部署优先使用 Docker Compose 隔离依赖,并挂载 volume 持久化数据
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
⚠️ 常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
  • 容器内无法访问宿主机 localhost — 使用 host.docker.internal

👥 适合人群

Claude Desktop / Claude Code 用户AI 工具开发者需要扩展 AI 能力的专业人士自动化工程师

🎯 使用场景

  • 在 Claude Desktop 对话中直接调用本地工具,实现 AI 与系统的深度联动
  • 通过自然语言驱动复杂的多步骤自动化任务,代替繁琐手动操作
  • 将多个 MCP 工具组合使用,构建个人专属 AI 工作站

⚖️ 优点与不足

✅ 优点
  • +MIT 协议,可免费商用
  • +标准化 MCP 协议,生态互联性强
  • +与 Claude 官方生态无缝对接
  • +即插即用,配置简单快捷
⚠️ 不足
  • −依赖 Claude 客户端,非 Claude 用户无法使用
  • −MCP 协议仍在持续演进,接口可能变更
  • −需要一定的配置步骤
⚠️ 使用须知

AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。

建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。

📄 License 说明

✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。

🔗 相关工具推荐

📰 相关 AI 新闻
🍿 AI 圈相关吃瓜
🗺️ 相关解决方案
🧩 你可能还需要
基于当前 Skill 的能力图谱,自动补全的工具组合

❓ 常见问题 FAQ

my-codex 是一款Shell开发的AI辅助工具。开源MCP工具:All-in-one agent harness for OpenAI Codex CLI — Boss meta-orchestrator, 400+ age。⭐17 · Shell 主要应用场景包括:自动化编码和AI代理管理。
💡 AI Skill Hub 点评

经综合评估,我的代码库 在MCP工具赛道中表现稳健,质量优秀。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。

⬇️ 获取与下载
⬇ 下载源码 ZIP

✅ MIT 协议 · 可免费商用 · 直接从 aiskill88 服务器下载,无需跳转 GitHub

📚 深入学习 我的代码库
查看分步骤安装教程和完整使用指南,快速上手这款工具
🌐 原始信息
原始名称 my-codex
原始描述 开源MCP工具:All-in-one agent harness for OpenAI Codex CLI — Boss meta-orchestrator, 400+ age。⭐17 · Shell
Topics agent-harnessai-agentscodex
GitHub https://github.com/sehoon787/my-codex
License MIT
语言 Shell
🔗 原始来源
🐙 GitHub 仓库  https://github.com/sehoon787/my-codex 🌐 官方网站  https://sehoon787.github.io/my-codex/

收录时间:2026-06-28 · 更新时间:2026-07-03 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。

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