Codex plugin for Claude Code brings Codex into Claude Code so users can request code reviews or delegate tasks from that existing workflow. It is for Claude Code users with a ChatGPT subscription or OpenAI API key who want commands for reviews, handoffs, background jobs, and session management. The catalogue entries are the plugin's commands, skills, hooks, and agent.
Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add openai/codex-plugin-cc/plugin install codexWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/openai/codex-plugin-cc/result)<a href="https://agentmods.dev/commands/openai/codex-plugin-cc/result"><img src="https://agentmods.dev/badge/commands/openai/codex-plugin-cc/result/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/openai/codex-plugin-cc/result"><img src="https://agentmods.dev/badge/commands/openai/codex-plugin-cc/result.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00014 | $0.00155 |
| Opus 5.5 | $0.00006 | $0.00062 |
| Sonnet 5.5 | $0.00003 | $0.00031 |
| Haiku 4.5 | $0.00001 | $0.00015 |
Grade A, and why
result scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
!node "${CLAUDE_PLUGIN_ROOT}/scripts/codex-companion.mjs" result "$ARGUMENTS"
Present the full command output to the user. Do not summarize or condense it. Preserve all details including:
- Job ID and status
- The complete result payload, including verdict, summary, findings, details, artifacts, and next steps
- File paths and line numbers exactly as reported
- Any error messages or parse errors
- Follow-up commands such as
/codex:status <id>and/codex:review
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 16 lines · 14 tokens per session scan A 2d996b04f51c
result is a command published in the GitHub repository openai/codex-plugin-cc (34,274 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 14 tokens to every session and 155 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.