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
Install / Use
npx skills add skills-directory/skill-codex --skill codexInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
AutomationSupported Platforms
Tags
Our assessment of codex
codex scores 85/100 on our quality scale, 1659th of 2,897 Automation skills we index.
Its SKILL.md is 7.0 KB long, well organised into 9 sections and no code examples: a thorough specification that gives an agent plenty to work with.
With 1,448 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 19 days ago, so codex is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.
Automated pattern scan on 2026-10-03. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
codex compared with similar skills
All 4 of these similar skills score higher than codex; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| codex (this skill)by skills-directory | 85 | 1.4k | 19d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 89.0k | 17d ago | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 85.3k | 2d ago | MCP Server |
| rufloby ruvnet | 100 | 73.7k | today | MCP Server |
| algorithmic-artby anthropics | 100 | 177.9k | 10d ago | SKILL.md |
Frequently asked questions
- How do I install codex?
- Run
npx skills add skills-directory/skill-codex --skill codex. The install tabs above show the steps for each supported agent. - Which AI agents does codex work with?
- It is written for OpenAI Codex, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is codex safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is codex still maintained?
- The repository was last updated 19 days ago, so codex is actively maintained.
Skill content
View source on GitHubname: codex description: Use when the user asks to run Codex CLI (codex exec, codex resume) or references OpenAI Codex for code analysis, refactoring, or automated editing
Codex Skill Guide
Running a Task
- 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 togpt-6-astraathigh.- 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
- GPT-6:
- Reasoning effort — default
high:low,medium,high,xhigh,max,ultra.max/ultrarequire a GPT-6 or GPT-5.6 model;ultrais only onastra/sol/terra(lunacaps atmax); legacy models cap atxhigh.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.
- Model — default
- Select the sandbox mode required for the task; default to
--sandbox read-onlyunless edits or network access are necessary. - 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)
- Always use --skip-git-repo-check.
- When continuing a previous session, use
codex exec --skip-git-repo-check resume --lastvia 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. - IMPORTANT: By default, append
2>/dev/nullto allcodex execcommands to suppress thinking tokens (stderr). Only show stderr if the user explicitly requests to see thinking tokens or if debugging is needed. - IMPORTANT (stdin):
codex execalways 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/nullto 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. - Run the command, capture stdout/stderr (filtered as appropriate), and summarize the outcome for the user.
- 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 case | Sandbox mode | Key flags |
| --- | --- | --- |
| Read-only review or analysis | read-only | --sandbox read-only 2>/dev/null |
| Apply local edits | workspace-write | --sandbox workspace-write --full-auto 2>/dev/null |
| Permit network or broad access | danger-full-access | --sandbox danger-full-access --full-auto 2>/dev/null |
| Resume recent session | Inherited from original | echo "prompt" \| codex exec --skip-git-repo-check resume --last 2>/dev/null (no flags allowed) |
| Run from another directory | Match 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 effort | Timeout |
|---|---|
| low | 150s |
| medium | 300s |
| high | 600s |
| xhigh | 1200s |
| max | 1800s |
| ultra | 1800s |
Following Up
- After every
codexcommand, immediately useAskUserQuestionto confirm next steps, collect clarifications, or decide whether to resume withcodex 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
- State your disagreement clearly to the user
- Provide evidence (your own knowledge, web search, docs)
- 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:
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 - Frame disagreements as discussions, not corrections - either AI could be wrong
- Let the user decide how to proceed if there's genuine ambiguity
Error Handling
- Stop and report failures whenever
codex --versionor acodex execcommand 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.
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
