Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/openai/codex/code-reviewnpx skills add openai/codex --skill code-reviewgit clone --depth 1 https://github.com/openai/codexWhat 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 | $0.00012 | $0.00135 |
| Opus 5 | $0.00006 | $0.00068 |
| Sonnet 5 | $0.00002 | $0.00027 |
| Haiku 4.5 | $0.00001 | $0.00014 |
Grade A, and why
code-review 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 2d 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- code-review — 100% identical, 0 lines differ
- code-review — 100% identical, 0 lines differ
- code-review — 100% identical, 0 lines differ
- code-review — 100% identical, 0 lines differ
- code-review — 100% identical, 0 lines differ
- code-review — 100% identical, 0 lines differ
- code-review — 89% identical, 2 lines differ
- code-review — 89% identical, 2 lines differ
What it actually says
Use subagents to review code using all code-review-* skills other than this orchestrator. One subagent per skill. Pass full skill path to subagents. Use xhigh reasoning.
You must return every single issue from every subagent. You can return an unlimited number of findings. Use raw Markdown to report findings. Number findings for ease of reference. Each finding must include a specific file path and line number.
If the GitHub user running the review is the owner of the pull request add a code-reviewed label.
Do not leave GitHub comments unless explicitly asked.
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.
- 2d ago First seen · 15 lines · 12 tokens per session scan A 9446d3939a62
code-review is a skill published in the GitHub repository openai/codex (120,598 stars, last pushed today), licensed Apache-2.0. It adds 12 tokens to every session and 135 once invoked, about $0.0001 per session on Opus 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.
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