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Codex

OrganizationPopular
skills-directory
codex

Use when the user asks to run Codex CLI (codex exec, codex resume) or references OpenAI Codex for code analysis, refactoring, or automated editing

Overview

Publisherskills-directory
Repositoryskill-codex
Skill namecodex
Stars
1.4K
Forks
112
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by skills-directory 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.
  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/skills-directory/skill-codex.git /tmp/skill-codex
mkdir -p .claude/skills
cp -r /tmp/skill-codex/plugins/skill-codex/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 Skill Guide

Running a Task

  1. For a new session (resumes inherit the prior model/effort — see step 5), ask the user (via AskUserQuestion) which model AND which reasoning effort to use, in a single prompt with two questions. When the user expresses no preference, default to gpt-6-astra at high.
    • Model — default gpt-6-astra:
      • GPT-6: gpt-6-astra (frontier / most capable — default)
      • GPT-5.6: gpt-5.6-sol (reliable agentic workhorse), gpt-5.6-terra (balanced, everyday), gpt-5.6-luna (fast & affordable)
      • Legacy (kept for compatibility): gpt-5.5, gpt-5.4, gpt-5.4-mini, gpt-5.3-codex-spark, gpt-5.3-codex
    • Reasoning effort — default high: low, medium, high, xhigh, max, ultra.
      • max/ultra require a GPT-6 or GPT-5.6 model; ultra is only on astra/sol/terra (luna caps at max); legacy models cap at xhigh.
      • ultra = maximum reasoning with automatic task delegation (slowest and most expensive — reserve for the hardest jobs).
      • If the chosen effort exceeds the chosen model's maximum, fall back to that model's highest supported effort and tell the user.
  2. Select the sandbox mode required for the task; default to --sandbox read-only unless edits or network access are necessary.
  3. Assemble the command with the appropriate options:
    • -m, --model <MODEL>
    • --config model_reasoning_effort="<low|medium|high|xhigh|max|ultra>" (max/ultra only on GPT-6 / GPT-5.6 models; ultra only on astra/sol/terra — see step 1)
    • --sandbox <read-only|workspace-write|danger-full-access>
    • --full-auto
    • -C, --cd <DIR>
    • --skip-git-repo-check
    • "your prompt here" (as final positional argument)
  4. Always use --skip-git-repo-check.
  5. When continuing a previous session, use codex exec --skip-git-repo-check resume --last via stdin. When resuming don't use any configuration flags unless explicitly requested by the user e.g. if he species the model or the reasoning effort when requesting to resume a session. Resume syntax: echo "your prompt here" | codex exec --skip-git-repo-check resume --last 2>/dev/null. All flags have to be inserted between exec and resume.
  6. IMPORTANT: By default, append 2>/dev/null to all codex exec commands to suppress thinking tokens (stderr). Only show stderr if the user explicitly requests to see thinking tokens or if debugging is needed.
  7. IMPORTANT (stdin): codex exec always reads stdin and concatenates it with the positional prompt -- even when the prompt is fully supplied as a positional argument. If stdin is not closed, codex blocks forever. When invoking from a harness (background tasks, hooks, scripts where stdin is not a TTY but also not closed), explicitly redirect stdin: append </dev/null to the command, e.g. codex exec ... "prompt" </dev/null 2>/dev/null. Symptom of getting this wrong: zero bytes of stdout, zero CPU accumulated, process appears hung indefinitely.
  8. Run the command, capture stdout/stderr (filtered as appropriate), and summarize the outcome for the user.
  9. After Codex completes, inform the user: "You can resume this Codex session at any time by saying 'codex resume' or asking me to continue with additional analysis or changes."

Quick Reference

Use caseSandbox modeKey flags
Read-only review or analysisread-only--sandbox read-only 2>/dev/null
Apply local editsworkspace-write--sandbox workspace-write --full-auto 2>/dev/null
Permit network or broad accessdanger-full-access--sandbox danger-full-access --full-auto 2>/dev/null
Resume recent sessionInherited from originalecho "prompt" | codex exec --skip-git-repo-check resume --last 2>/dev/null (no flags allowed)
Run from another directoryMatch task needs-C <DIR> plus other flags 2>/dev/null

Execution timeouts

Codex produces no intermediate output — it writes the result only at completion. If the process is killed before finishing, the output file is silently empty (no error).

Preferred approach: run synchronously — eliminates timeout risk entirely and the conversation waits for the result anyway.

If running in background, set the execution timeout based on reasoning effort:

Reasoning effortTimeout
low150s
medium300s
high600s
xhigh1200s
max1800s
ultra1800s

Following Up

  • After every codex command, immediately use AskUserQuestion to confirm next steps, collect clarifications, or decide whether to resume with codex exec resume --last.
  • When resuming, pipe the new prompt via stdin: echo "new prompt" | codex exec resume --last 2>/dev/null. The resumed session automatically uses the same model, reasoning effort, and sandbox mode from the original session.
  • Restate the chosen model, reasoning effort, and sandbox mode when proposing follow-up actions.

Critical Evaluation of Codex Output

Codex is powered by OpenAI models with their own knowledge cutoffs and limitations. Treat Codex as a colleague, not an authority.

Guidelines

  • Trust your own knowledge when confident. If Codex claims something you know is incorrect, push back directly.
  • Research disagreements using WebSearch or documentation before accepting Codex's claims. Share findings with Codex via resume if needed.
  • Remember knowledge cutoffs - Codex may not know about recent releases, APIs, or changes that occurred after its training data.
  • Don't defer blindly - Codex can be wrong. Evaluate its suggestions critically, especially regarding:
    • Model names and capabilities
    • Recent library versions or API changes
    • Best practices that may have evolved

When Codex is Wrong

  1. State your disagreement clearly to the user
  2. Provide evidence (your own knowledge, web search, docs)
  3. Optionally resume the Codex session to discuss the disagreement. Identify yourself as Claude so Codex knows it's a peer AI discussion. Use your actual model name (e.g., the model you are currently running as) instead of a hardcoded name:
    bash
    echo "This is Claude (<your current model name>) following up. I disagree with [X] because [evidence]. What's your take on this?" | codex exec --skip-git-repo-check resume --last 2>/dev/null
  4. Frame disagreements as discussions, not corrections - either AI could be wrong
  5. Let the user decide how to proceed if there's genuine ambiguity

Error Handling

  • Stop and report failures whenever codex --version or a codex exec command exits non-zero; request direction before retrying.
  • Before you use high-impact flags (--full-auto, --sandbox danger-full-access, --skip-git-repo-check) ask the user for permission using AskUserQuestion unless it was already given.
  • When output includes warnings or partial results, summarize them and ask how to adjust using AskUserQuestion.

Frequently asked questions

What does the Codex AI skill do?

Use when the user asks to run Codex CLI (codex exec, codex resume) or references OpenAI Codex for code analysis, refactoring, or automated editing

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/skills-directory/skill-codex/tree/main/plugins/skill-codex/skills/codex. TypingMind reads its SKILL.md 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 skills-directory 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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