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Codex

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codex

当用户明确要求直接操作 Codex CLI 作为后台代理(自选 sandbox、codex review、apply、resume 等原生命令,或用 git worktree 并行布置多个 worker)做代码分析、编辑、审查时使用。普通的“让 Codex 或 GPT-6 写代码、调研、review”派单走 agent-fleet(fleet code);图片生成与网站视觉素材走 imagegen。

Overview

Publisheryan-labs
Repositoryyan-skills
Skill namecodex
Stars
225
Forks
94
Bundled files
2
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by yan-labs on GitHub. Read the source before you install it.

Installation

Install the Codex AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/yan-labs/yan-skills/tree/main/codex
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/yan-labs/yan-skills.git /tmp/yan-skills
mkdir -p .claude/skills
cp -r /tmp/yan-skills/codex .claude/skills/codex
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Codex in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Codex on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Codex is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Codex 后台代理

用 Codex CLI 承接用户指定的代码分析、编辑、审查或并行代理任务。图片生成的当前流程见 imagegen/SKILL.md;旧版操作与实验记录保存在 references/image-experiments.md。

路由前提:按全局 CLAUDE.md §2,编码、修 bug、调研与 review 默认用 fleet code(gpt-6.1-sol,见 agent-fleet),不手写 codex exec。本 Skill 只在用户明确要求直接操作 Codex CLI(自选 sandbox、codex review/apply/resume 等原生子命令、并行 worker 的 worktree 布置)时使用;简单派单不要先读本文件。

Core Principle

Never block the main conversation on a codex exec call. Always launch via Bash with run_in_background: true. The only exception is a trivial codex --version health check.

Launching a Codex Sub-Agent

  1. Pick reasoning effort + sandbox from context — do not interrupt the user with AskUserQuestion unless they explicitly ask to be prompted. Model is gpt-6.1-sol (the default in ~/.codex/config.toml; pass -m only when the user names another model in the current request). Defaults:
    • Reasoning effort: medium; low for single-file edits with clear boundaries. Never escalate to high/xhigh on your own (global §2); only when the user asks for it explicitly
    • Sandbox: read-only unless the task clearly needs edits (workspace-write) or network (danger-full-access)
  2. Write the prompt to a temp file when it's non-trivial (multi-line, contains quotes, long context). Pipe it via stdin so quoting never breaks:
    bash
    cat /tmp/codex-prompt-<tag>.md | codex exec --skip-git-repo-check \
      --config model_reasoning_effort="medium" \
      --sandbox read-only \
      -C <workdir> 2>/dev/null
  3. Launch with run_in_background: true. Record the returned shell id and a short tag (e.g. codex-review, codex-refactor-auth) so you can reference it later.
  4. Report the launch to the user in one line — e.g. "Launched Codex sub-agent codex-review (medium effort, read-only) in background." Then continue with other work or wait for user input. Do NOT sit and poll.
  5. 默认加 2>/dev/null 压低 stderr 噪音;排查启动失败或调试 CLI 时保留错误输出。
  6. Always pass --skip-git-repo-check. Put all flags between exec and resume (if resuming).

Checking Results

  • When the background shell finishes, the harness notifies you. Read its output with BashOutput (or Read on the captured log file) — do not re-run the command.
  • If the user asks for status mid-run, read the current buffer once and summarize progress; don't busy-loop.
  • Summarize Codex's findings in the main thread in a few sentences. Link file:line references so the user can jump directly.
  • After completion, tell the user they can resume with: codex resume <tag> → you will run echo "<new prompt>" | codex exec --skip-git-repo-check resume --last 2>/dev/null (no other flags on resume; session inherits model/effort/sandbox).

Agent Teams (Parallel Codex Workers)

Codex sub-agents compose cleanly. To run an agent team:

  1. Split the task into independent slices (e.g. "review auth layer", "review billing layer", "draft migration", "write tests"). Dependent steps must stay sequential.
  2. For each slice, write a prompt file and launch a separate background Bash call in the same message (parallel tool calls). Give each a distinct tag and, if they write, a distinct -C workdir or separate git worktree to avoid edit collisions.
  3. Track the set: tag → shell id → one-line goal. Keep this list short in the user-facing update.
  4. As workers finish, fold their findings into a single synthesis. If two workers disagree, surface the disagreement explicitly instead of silently picking one.
  5. Edit collisions: never run two workspace-write Codex workers against the same files concurrently. Either serialize them, scope them to disjoint directories, or run each in its own git worktree.

Team composition guidance

  • Reviewer team: multiple read-only workers, each with a different lens (security, perf, API design). Cheap and fully parallel.
  • Builder + reviewer: one workspace-write worker implements, then a read-only worker reviews the diff. Sequential, not parallel.
  • Independent review: maker and checker must be different executors. Have another read-only gpt-6.1-sol worker (fleet code --review) check the diff against the brief (global §4.3). Do not pair a Codex worker with a Claude sub-agent as the reviewer.

Model Selection

Default model: gpt-6.1-sol, which ~/.codex/config.toml already sets. Only add an explicit -m flag when the user asks for a different model by name in the current request.

Reasoning effort: medium (standard default) · low (single-file, clear boundaries). high and above are never chosen automatically; use them only on the user's explicit request.

Cached input is 90% off for 24h — reuse the same prompt prefix across workers when possible.

Do not ration Codex calls and set no turn, context or time cap (global §3). The owner's plan does not need protecting from extra workers; optimize for the right answer and run to the acceptance condition. A limit the user states explicitly always wins.

Error Handling

  • 如果 codex --version 或启动失败,先运行 codex doctor 并读取错误,最多重试一次;仍失败(含 402、登录失效、模型被拒、产物为空或含裸 tool-call 控制 token)就停下并如实告知用户,由用户决定。不许静默改派 Claude subagent,Claude subagent 也不得再派 Claude subagent。
  • Sandbox flags need no permission prompt on this machine. The owner has granted standing authorization for --full-auto and --sandbox danger-full-access: it is their own single-user machine and they prefer agents to act rather than ask. Pick the sandbox the task needs and run. Still disclose it — the one-line launch report names the sandbox, so "no gate" never becomes "no visibility". Never use AskUserQuestion for a sandbox flag.
  • If a background worker exits non-zero, read its tail output, summarize the failure, retry at most once after diagnosing, and if it still fails stop and tell the user. Exit code 0 is not acceptance: check the diff and artifacts.

CLI surface worth knowing (verified against codex-cli 0.147.0, 2026-08-22)

The skill used to describe exec as if it were the whole CLI. It is not. Commands that change what you would reach for:

CommandWhat it doesWhen it beats exec
codex reviewNon-interactive code review of the repo (also codex exec review)A purpose-built reviewer — use it instead of hand-writing a "review this diff" prompt
codex applyApplies the agent's latest diff to the working tree via git applyLets a read-only worker propose changes you land separately — safer than workspace-write
codex doctorDiagnoses install, config, auth, runtime healthFirst move when a launch fails, before any retry
codex forkForks a past sessionExplore a variant without destroying the original thread
codex resume / archive / delete / unarchiveSession lifecycleLong-running work across days
codex mcp / mcp-serverManage MCP servers, or run Codex itself as oneCodex can be a tool for another agent
codex cloudBrowse Codex Cloud tasks, apply locally (experimental)Work started elsewhere
codex update · codex featuresSelf-update; inspect feature flagsCheck before assuming a capability is missing

Two exec flags the recipes above should use more:

  • -o <FILE> / --output-last-message <FILE> — writes the agent's final message to a file. Prefer this over scraping stdout: stdout carries progress chatter, and parsing it is exactly the kind of silently-wrong extraction this workspace has been bitten by.
  • --output-schema <FILE> — a JSON Schema constraining the final response shape. Use it whenever you need a structured result back, instead of asking for JSON in prose and hoping.

CLI Version

Check with codex --version. Default model gpt-6.1-sol is configured in ~/.codex/config.toml — do not override it unless the user explicitly requests a different model.

本 Skill 当前位于 yan-skills/codex/;安装位置和符号链接以实际工作树为准。

Anti-patterns

  • Running codex exec in the foreground and making the user wait.
  • Calling AskUserQuestion before every launch — decide from context.
  • Asking permission for a sandbox flag. Standing authorization exists on this machine; asking is friction, not safety. Disclose the sandbox in the launch line instead.
  • Spawning parallel workspace-write workers on overlapping paths.
  • Polling a background shell in a tight loop instead of waiting for the completion notification.
  • Forgetting 2>/dev/null and flooding the main thread with thinking tokens.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Codex AI skill do?

当用户明确要求直接操作 Codex CLI 作为后台代理(自选 sandbox、codex review、apply、resume 等原生命令,或用 git worktree 并行布置多个 worker)做代码分析、编辑、审查时使用。普通的“让 Codex 或 GPT-6 写代码、调研、review”派单走 agent-fleet(fleet code);图片生成与网站视觉素材走 imagegen。

Why use Codex on TypingMind?

Because you install it once and use it with any model. Codex is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Codex in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yan-labs/yan-skills/tree/main/codex. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Codex?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Codex?

As many as you like. As long as a model supports skills, you can use Codex with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Codex AI skill free?

Yes. It is published on GitHub by yan-labs under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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