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。
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。
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。
# 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"
// 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
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Looking for Claude Code? → my-claude — same Boss orchestration in native Claude
.mdagent format
---
| Category | Count | Source |
|---|---|---|
| **Core agents** (always loaded) | 17 | Boss 1 + OMO 9 + OMX 7 |
| **Agent packs** (opt-in, none enabled by default) | 17 | 2 vendored categories: data-ai 13 + llmops 4 |
**Skills exposed** (default core profile) | 30 | The always-on set; every other entry is one lane flag away |
**Skills installed** (entries under ~/.codex/skills/) | 110 | ECC 61 · gstack 30 · Superpowers 14 · Core 4 · archify 1 |
| **MCP Servers** | 5 | Context7, Exa, grep.app, Serena, Headroom |
| **config.toml** | 1 | my-codex |
| **AGENTS.md** | 1 | my-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>
| Agent | Model | Role | Source |
|---|---|---|---|
| Boss | gpt-6.1-sol xhigh | Dynamic runtime discovery → capability matching → optimal routing. Never writes code. | my-codex |
</details>
<details> <summary><strong>OMO Agents — Sub-orchestrators and specialists (9)</strong></summary>
| Agent | Model | Role | Source |
|---|---|---|---|
| Sisyphus | gpt-6-astra high | Intent classification → specialist delegation → verification | oh-my-openagent |
| Hephaestus | gpt-6-astra high | Autonomous explore → plan → execute → verify | oh-my-openagent |
| Atlas | gpt-6-astra high | Task decomposition + 4-stage QA verification | oh-my-openagent |
| Oracle | gpt-6-astra xhigh | Strategic technical consulting (read-only) | oh-my-openagent |
| Metis | gpt-6-astra high | Intent analysis, ambiguity detection | oh-my-openagent |
| Momus | gpt-6-astra high | Plan feasibility review | oh-my-openagent |
| Prometheus | gpt-6-astra xhigh | Interview-based detailed planning | oh-my-openagent |
| Librarian | gpt-6.1-sol medium | Open-source documentation search via MCP | oh-my-openagent |
| Multimodal-Looker | gpt-6.1-sol medium | Image/screenshot/diagram analysis | oh-my-openagent |
</details>
<details> <summary><strong>OMX Agents — Specialist workers (7)</strong></summary>
| Agent | Sandbox | Role | Source |
|---|---|---|---|
| executor | workspace-write | Code implementation | oh-my-codex |
| planner | read-only | Implementation planning | oh-my-codex |
| architect | read-only | System design and architecture | oh-my-codex |
| test-engineer | workspace-write | Test strategy and coverage | oh-my-codex |
| security-reviewer | read-only | Security analysis | oh-my-codex |
| code-reviewer | read-only | Focused code review | oh-my-codex |
| debugger | workspace-write | Root cause analysis | oh-my-codex |
</details>
<details> <summary><strong>Agent Packs — Opt-in AI specialists (2 packs, 17 agents)</strong></summary>
| Pack | Count | Agents |
|---|---|---|
| data-ai | 13 | ai-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 |
| llmops | 4 | ai-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>
| Source | Installed | Key Skills |
|---|---|---|
| everything-claude-code | 61 | coding-standards, python-testing, api-design, deep-research |
| gstack | 30 | /qa, /review, /ship, /cso, /investigate, /office-hours |
| superpowers | 14 | brainstorming, systematic-debugging, TDD, writing-plans |
| [my-codex Core](https://github.com/sehoon787/my-codex) | 4 | boss-advanced, boss-briefing, briefing-vault, gstack-sprint |
| archify | 1 | archify (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.
| Server | Purpose | Cost |
|---|---|---|
| <img src="https://context7.com/favicon.ico" width="16" height="16" align="center"/> [Context7](https://context7.com) | Real-time library documentation | Free |
| <img src="https://exa.ai/images/favicon-32x32.png" width="16" height="16" align="center"/> [Exa](https://exa.ai) | Semantic web search | Free 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 search | Free |
</details>
---
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/.
bash /tmp/my-codex/install.sh --profile full ```
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.
| Piece | What 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.
Global Codex settings in ~/.codex/config.toml:
[agents]
max_threads = 8
max_depth = 1
max_threads — Maximum concurrent sub-agentsmax_depth — Maximum nesting depth for agent-spawns-agent chains---
For end-to-end feature implementation, Boss orchestrates a structured sprint:
| Phase | Mode | What Happens |
|---|---|---|
| **1 · Design** | interactive | User decides scope · engineering review · confirm "design done" |
| **2 · Execute** | autonomous | executor runs the tasks · auto code review · architect verification |
| **3 · Review** | interactive | Compare against the design doc · present comparison table · user approves or asks for improvement |
<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>
---
高质量的MCP工具,集成OpenAI Codex CLI
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。
✅ 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。
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。
| 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 |
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。
选择 Agent 类型,复制安装指令后粘贴到对应客户端