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To run Codex from Claude Code, use OpenAI’s Codex plugin for Claude Code. It connects to your local Codex installation for reviews and delegated tasks. If you mean reusing a skill written for Codex, that is a separate workflow: adapt its SKILL.md and supporting files for Claude Code’s skill directory and available tools. The phrase “Codex skill for Claude Code” also appears on community wrappers. Check the publisher and what the package actually invokes before choosing one. Neither the official plugin nor a portable skill needs AMS. AMS is an optional coordination layer for independent Claude Code and Codex tasks.

Choose the integration that matches your goal

Installing developer documentation tools does not itself delegate work to Codex. Copying a skill does not transfer authentication, tool connections or an active agent conversation.

Run Codex inside Claude Code with the official plugin

Prerequisites

Use a local Claude Code session with plugin support, a Git repository containing work to review, and Node.js 18.18 or later. Have Codex installed on the same machine and signed in with an existing account. The plugin uses the local Codex authentication and configuration; a new API key is unnecessary when your existing ChatGPT sign-in works. Codex runs still consume your account’s usage allowance. These requirements and commands come from the OpenAI plugin README. Check your organization’s plugin policy before installing. Keep repository dependency and install-script safeguards in place if setup offers to install a missing CLI.

Install and check readiness

Run these at the Claude Code prompt, one at a time:
Review the plugin’s details and choose the intended installation scope when Claude Code opens the install panel. Anthropic documents plugin installation and scope; local scope limits enablement to you in this repository. Setup should report whether the local Codex CLI is available and authenticated. If it needs sign-in, the plugin documents !codex login from Claude Code. Complete the existing account’s sign-in rather than placing credentials in a skill or prompt.

Review a real change

In the repository you want reviewed, inspect git status --short in a terminal first. Choose a small, existing uncommitted change whose purpose you understand. Then, in Claude Code:
The first command starts review; the next two inspect progress and retrieve the completed output. Wait for completion before expecting a final result. The review command’s source explicitly restricts it to review and includes staged, unstaged and untracked work in scope. Keep unrelated changes out of the checkout you choose for this exercise. Expected outcome: a completed Codex review of the selected repository state, with findings or a report of no findings. Review does not fix the code, and no findings is not proof that the change is correct. A clean working tree can legitimately leave nothing to review. For a branch comparison, use /codex:review --base your-base-ref, replacing the reference with the base you actually intend. Do not append custom focus instructions to normal review. The plugin provides /codex:adversarial-review for a steerable challenge review.

Know when you are authorizing edits or a handoff

/codex:rescue delegates a task that can change files. Define its allowed paths, desired result and checks before using it. The plugin works in the same repository checkout; installation does not create an isolated worktree for another writer. Use worktrees or exclusive file ownership when work overlaps. /codex:transfer creates a persistent Codex thread from the Claude Code conversation and prints a command to resume it. That is a conversation handoff, separate from copying a skill. See the upstream command documentation for transfer, cancellation and job selection.

The OpenAI Developers plugin is a different package

For OpenAI API guidance inside Claude Code, OpenAI separately documents:
Start a new Claude Code session afterward. Its Claude adaptation bundles developer skills and the OpenAI Docs MCP server, without the Codex-specific Platform connector. It is useful for researching implementation questions; it is not the /codex:review integration above. See OpenAI’s installation guide. If you only need documentation search, the public Docs MCP server provides read-only documentation access. It does not run OpenAI API requests on your behalf.

What about third-party Codex skills?

For example, skills-directory/skill-codex is a community integration whose README describes invoking codex exec and resuming sessions. It offers both a plugin and a standalone skill. It is distinct from OpenAI’s official Codex plugin, even though both use a local Codex installation. Before adopting a wrapper, read its actual SKILL.md and helpers. Check the executable it launches, model defaults, sandbox settings, approval behavior, and handling of logs and errors. Copying a community skill’s name does not reproduce its dependencies or make it an official integration. This guide does not require installing that wrapper.

Reuse a Codex skill in Claude Code

Both hosts document the open Agent Skills format: a directory containing SKILL.md, with optional scripts, references and assets. A small instruction-only skill is the easiest starting point. See OpenAI’s skill authoring documentation and Anthropic’s skill documentation. For local terminal workflows, use the appropriate discovery directory: Review the skill’s license and preserve its supporting files. Then adapt any host-specific behavior:
  • Replace unavailable tool names and connector assumptions with tools configured in the receiving host.
  • Check frontmatter semantics. Claude-specific fields such as context: fork, and dynamic shell injection, are not a portable execution contract.
  • Resolve bundled file paths correctly. A host-specific variable or absolute path on another computer is not a transferable dependency.
  • Recheck permissions. Instructions do not grant tool access or override the receiving host’s approval rules.
Portable instructions describe the procedure. Each host still controls discovery, invocation, execution and authorization.

Worked example: a portable handoff check

This example checks a short handoff note using file reading only. It invokes no Codex subprocess, installs no package and needs no new credentials. Use a disposable Git checkout, and check that neither skill directory below already exists before creating it. Create .agents/skills/handoff-check/SKILL.md with:
Create handoff-demo.txt at the project root:
Copy the complete skill folder to Claude Code’s project location. From the project root on macOS or Linux:
Start a local Claude Code session in that folder and invoke:
In Codex CLI or its IDE extension, invoke the corresponding skill:
Both should identify Unverified as missing and preserve Verification: Not run. Add Unverified: Runtime behavior has not been checked. to the note and invoke again: the expected missing-field result is now none. No result should imply that the example source file exists or that tests ran. This checks the portability of instructions, not communication between agents. For broader skill authoring and activation, continue with Claude Code skills or Codex skills.

Troubleshooting

Add AMS when independent tasks need shared context

Use AMS when separately running Claude Code and Codex tasks need to exchange findings, agree file ownership or leave a durable handoff. It does not replace either host’s skill loader or run the official plugin for you. Follow the cross-tool coordination guide for workspace activation, distinct task identities and a test–fix–retest workflow. A message stored in AMS does not by itself wake an idle agent or prove receipt. The receiving task must read it, or its host must supply a supported continuation mechanism; seek an acknowledgement before conflicting edits.
Vendor commands and skill locations were checked against the linked primary sources on 5 November 2026. The portable fixture and exact folder-copy commands were checked locally for matching skill files and expected missing fields. Plugin installation, authenticated Codex review, transfer and model-generated skill output were not exercised for this article. Follow the examples in your own authorized environment and inspect the actual results.