Agent skill

Codex CLI

by scarletkc in scarletkc/agents

When handing work to the Codex CLI earns its cost, and how to size the run: second-model review, bounded implementation hand-offs, sandbox permissions, model and reasoning effort.

Apache-2.0Auto-check passedDevelopment

Install Codex CLI

skills CLI
$ npx skills add scarletkc/agents --skill codex-cli -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install scarletkc/agents codex-cli --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/scarletkc/agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/codex-cli .claude/skills/codex-cli && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
codex-cli
GitHub stars
224
Token cost
~2.8k tokens
SKILL.md length
1,640 words
Files
1
Skills in repo
11
Repo updated
First seen
Licence
Apache-2.0

At a glance

When handing work to the Codex CLI earns its cost, and how to size the run: second-model review, bounded implementation hand-offs, sandbox permissions, model and reasoning effort.

  • The user asks for Codex
  • SKILL.md covers When to reach for it, When to keep it, Choosing the model and Choosing the effort, plus 2 more sections
  • Calls codex and git
  • A change is complex

What it does

Codex CLI is an agent skill from scarletkc/agents. When handing work to the Codex CLI earns its cost, and how to size the run: second-model review, bounded implementation hand-offs, sandbox permissions, model and reasoning effort. Use when the user asks for Codex or codex exec, when a change is complex or high-stakes enough that an independent reviewer would change the outcome, or when a delegated run needs its model, effort, or permissions chosen. For agents other than Codex itself.

Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development. The repository describes itself as: Shared standards and reusable skills for Claude Code, Codex CLI, and other AI coding agents. The licence is Apache-2.0.

When your agent uses it

  • The user asks for Codex
  • A change is complex
  • High-stakes enough that an independent reviewer would change the outcome
  • A delegated run needs its model

Example prompts

  • “/codex-cli”

What it can do on your machine

Read from SKILL.md and the folder at commit eb55005. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • codex
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Codex CLI loads about 2.8k tokens when it runs. Until then it costs about 112 tokens; SKILL.md has 1,640 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~112
When it runs · the whole SKILL.md, loaded when a task matches
~2.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from scarletkc/agents at commit eb55005, republished under its Apache-2.0 licence (© scarletkc). 1,640 words, ~2,805 tokens.

Download SKILL.mdSave it as .claude/skills/codex-cli/SKILL.md (or your agent's skills folder).
name
codex-cli
description
When handing work to the Codex CLI earns its cost, and how to size the run: second-model review, bounded implementation hand-offs, sandbox permissions, model and reasoning effort. Use when the user asks for Codex or `codex exec`, when a change is complex or high-stakes enough that an independent reviewer would change the outcome, or when a delegated run needs its model, effort, or permissions chosen. For agents other than Codex itself.
license
Apache-2.0
metadata.author
scarletkc
metadata.source
https://github.com/scarletkc/agents
metadata.summary
Reach for the Codex CLI when a task is hard enough to earn it: second-model review, bounded hand-offs, sandbox permissions, and a model and effort matched to…

Codex CLI

Codex is a second agent on the same machine, with its own model behind it. That is the entire reason to reach for it: a model that did not write the code has no memory of intending it to work. Everything below follows from that one asymmetry — who wrote it, who reads it, and how much thinking each step is worth paying for.

This skill is for the supervising agent, not for Codex. If you are Codex, this does not apply — calling yourself buys nothing but a second opinion from the same mind. Claude Code is the intended caller.

It also assumes the machine is already set up: codex on PATH, the user logged in, and whatever MCP servers and tools they want available configured in ~/.codex/config.toml. If the binary is missing, auth has expired, or a run dies on permissions, report that plainly and stop — quietly falling back to doing it yourself hides the fact that the review the user asked for never happened.

Concrete flags belong to codex --help, which is authoritative and moves faster than this file. What follows is the judgment.

When to reach for it

Every invocation is a second model spending the user's money and your wall-clock time. It earns that when the problem is hard enough that another model changes the outcome — not as a reflex after every edit. Doing the work yourself and checking it with the project's own tests remains the normal path.

  • When the user asks for it. They have already made the call; don't re-litigate it. Match the model and effort to the task and go.
  • After writing something complex or expensive to get wrong. Your own review of your own diff is the weakest review available, because you are checking the code against the intent you already have in your head rather than against what it says. That weakness only matters when the defect would be costly — concurrency, migrations, security-adjacent paths, platform assumptions, anything on a compatibility surface. A routine edit that the suite already covers is not worth a review pass. Counter-example: an agent changed one side of a path comparison to a normalized form and left the other side platform-native; every test it wrote passed, because it wrote them against the same wrong mental model.
  • When a demanding change is bounded well enough to describe in a prompt. A hand-off is worth it when you can state the goal, the files, and the acceptance check in a paragraph. That paragraph is also the honest test of whether you understand the change — if you cannot write it, delegating it just moves the confusion downstream. What "bounded" means is scoped-change, and it binds Codex exactly as it binds you: pass the boundary along in the prompt, because Codex cannot infer where the user drew it.
  • When the work is long, mechanical, and verifiable. Wide renames, repetitive migrations, and mass edits with a green suite proving them buy throughput rather than insight, and they are cheap to check.
  • When you are stuck. After two failed attempts on the same defect, a third attempt from the same context tends to repeat the second. A fresh agent with the symptom and the reproduction, and none of your accumulated theory, is a better use of the next few minutes.

When to keep it

  • Ordinary work you can verify yourself. Most changes are this. Writing the prompt, waiting for the run, and reading the diff costs more than the edit, and a delegated pass over a small change mostly returns items you already knew. Absent a reason above, just do it.
  • Judgment about words. User-facing copy, documentation, naming, and release notes need the taste and the context of the session that has been talking to the user, and they survive delegation badly. See ux-writing.
  • Anything you cannot check afterwards. Delegating work you have no way to verify converts an unknown into a confident-sounding report, which is worse than the unknown. Establish the check first.
  • Decisions the user reserved. Choosing the approach, committing, pushing, opening or merging a PR — those stay where the user put them. A permissive sandbox makes it possible for a delegated run to do all of them, which is a reason to scope the prompt tightly, not a licence to let it decide.

Choosing the model

Pick from the task's difficulty, not from habit. Reaching for the strongest model every time wastes the user's money on renames; reaching for the cheapest on a subtle bug wastes the user's afternoon.

  • gpt-5.6-sol — the frontier model. Worth it for reviews that must not miss anything, root-cause hunts, concurrency and lock-ordering questions, cross-platform semantics, and any change whose failure mode is silent.
  • gpt-5.6-terra — balanced, and the sane default for ordinary feature work inside a boundary you have already defined.
  • gpt-5.6-luna — fast and cheap with a lower ceiling. Right when a test suite or a compiler, not the model, is what actually decides whether the result is correct.

Choosing the effort

Reasoning effort buys deliberation, not knowledge, and it multiplies both latency and cost. Scale it with how subtle the failure would be:

  • medium — mechanical work with an immediate, objective check.
  • high — real implementation work and routine reviews. The usual pick.
  • xhigh — subtle bugs, unfamiliar subsystems, anything one attempt has already failed at.
  • max — the hardest problems, when a wrong answer costs far more than the extra minutes. Deliberate, not habitual.

The pairing that matters most: review the code at least as high as you wrote it. A cheap review of an expensive change finds the typos and misses the reason you delegated it.

Running it

Drive the non-interactive surface — codex exec for work, codex exec review for review. Model and effort are per invocation: -m <model> and -c model_reasoning_effort=<level>, both overriding the user's config.toml defaults for that run only. Prefer codex exec review over the top-level codex review, which is equally non-interactive but takes the model through -c model="..." rather than -m. Review scope is --base <branch> for a branch, --uncommitted for the working tree, --commit <sha> for one commit. Everything else — --json, output files, resuming a session — is in codex --help.

Show full SKILL.md (620 more words)Show less
Give it the permissions the task needs

This is the step that most often turns a delegated run into a wasted one. codex exec is sandboxed, and its own default — before the user's config is applied — is read-only: the model reads the repository, plans the change, and every write is refused. Failures inside the sandbox are handed back to the model rather than raised to you, so what returns is a fluent description of a change that never reached disk.

  • Let the user's configuration apply, and reach for -s mainly to narrow. Their config.toml already encodes the permission level they are willing to run at, and on many machines it is the setting that actually works. -s read-only is a sound narrowing for a review or an investigation, since nothing should be written anyway. --ignore-user-config discards their settings wholesale and is rarely what you want.
  • Raising the mode is a request, not a guarantee. -s workspace-write asks for a writable workspace; whether it is granted depends on the platform's sandbox backend and on any .rules policy in effect, and on a host without a working backend the writes are refused anyway. So confirm with git status on the target tree rather than with the run's summary. When the answer is "nothing changed", the fix lives in the user's configuration or their host setup — say so, rather than rerunning the same command or escalating the flag yourself.
  • Widen the reach deliberately, not by default. --add-dir makes another directory writable, which matters when the work spans a worktree and its main checkout; -C sets the working root; --skip-git-repo-check allows running outside a repository. workspace-write does not imply network access — that is a separate setting (sandbox_workspace_write.network_access), so dependency installs inside it fail until it is enabled.
  • Treat full access as the user's call. -s danger-full-access and --dangerously-bypass-approvals-and-sandbox remove the boundary that keeps a delegated agent inside the task. Some users configure exactly that globally and are happy with it; inheriting their setting is different from escalating to it yourself on a task they scoped narrowly.
Write the prompt like a brief
  • Put the acceptance check in it. State the goal, the files in scope, the test or command that proves it, and the boundary it must not cross. Codex cannot see your conversation with the user, so anything the user said that constrains the change has to be restated.
  • Ask review prompts for specifics. "Review this" returns prose. Naming what you are unsure of — the migration path, the error handling, the platform assumption — returns findings you can act on.

Reading the result back

A Codex run is a proposal, not a merge. You asked for a second model precisely because a single model's confidence is not evidence, and that cuts both ways.

  • Read the diff, not the summary. The report describes what Codex meant to do. Only the diff says what it did, and the gap between the two is where the surprises live — the unrelated file it touched, the test it relaxed to make something pass, the fallback it added to keep an error from surfacing.
  • Run the suite yourself. "Tests pass" from the agent that changed the tests is a claim about the same run that produced them.
  • Findings are input, not a verdict. A review from a strong model still produces items that are wrong about this codebase or out of scope for this change. Judge each one, fix what is real, and say plainly which ones you dismissed and why — an unexplained dismissal reads as an oversight later.
  • Report the division of labour. When the user reads the result, they should know which parts another agent wrote and what you verified. That is what makes the supervision worth anything.

© scarletkc, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/codex-cli of scarletkc/agents.

Open the folder on GitHubat commit eb55005

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Categories

Questions about Codex CLI

What does Codex CLI do?

When handing work to the Codex CLI earns its cost, and how to size the run: second-model review, bounded implementation hand-offs, sandbox permissions, model and reasoning effort. Codex CLI is an agent skill from scarletkc/agents. When handing work to the Codex CLI earns its cost, and how to size the run: second-model review, bounded implementation hand-offs, sandbox permissions, model and reasoning effort.

When should I use Codex CLI?

Codex CLI fits situations like: the user asks for Codex; A change is complex; high-stakes enough that an independent reviewer would change the outcome; A delegated run needs its model.

How do I install Codex CLI in Claude Code?

Run `npx skills add scarletkc/agents --skill codex-cli -a claude-code`. Or copy the skill folder (skills/codex-cli in scarletkc/agents) into .claude/skills/codex-cli in your project. Claude Code loads it when a task matches its description.

How do I install Codex CLI in Codex?

Run `npx skills add scarletkc/agents --skill codex-cli -a codex`. Or copy the skill folder (skills/codex-cli in scarletkc/agents) into .agents/skills/codex-cli in your project. Codex loads it when a task matches its description.

Can I use Codex CLI in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add scarletkc/agents --skill codex-cli -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/codex-cli, .gemini/skills/codex-cli, .github/skills/codex-cli and .opencode/skills/codex-cli in your project.

What does Codex CLI need to run?

Going by SKILL.md and its folder, Codex CLI needs the command-line tools its instructions call (codex and git).

Does Codex CLI access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Codex CLI safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Codex CLI use?

Codex CLI is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Codex CLI use?

About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Codex CLI?

Skills that share tags, products or a category with Codex CLI: Finishing a Development Branch (obra/superpowers, 296k stars), Typescript Advanced Types (rolling-scopes/rsschool-app, 10k stars), PR Babysitter (openinterpreter/openinterpreter, 69k stars) and Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Codex CLI?

scarletkc (a GitHub user) maintains it in scarletkc/agents, which has 224 GitHub stars. The repository holds 11 skills in this directory. The repository was last updated on September 21, 2026.

Source: scarletkc/agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.