Borrowing it
Nothing to install: this file belongs to dengxu11111/dual-ai-paper-coach. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/dengxu11111/dual-ai-paper-coach/main/.claude/commands/codex-review.mdgit clone --depth 1 https://github.com/dengxu11111/dual-ai-paper-coachWrote 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/dengxu11111/dual-ai-paper-coach/codex-review)<a href="https://agentmods.dev/commands/dengxu11111/dual-ai-paper-coach/codex-review"><img src="https://agentmods.dev/badge/commands/dengxu11111/dual-ai-paper-coach/codex-review/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/dengxu11111/dual-ai-paper-coach/codex-review"><img src="https://agentmods.dev/badge/commands/dengxu11111/dual-ai-paper-coach/codex-review.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 | $0.00018 | $0.01247 |
| Opus 5 | $0.00009 | $0.00624 |
| Sonnet 5 | $0.00004 | $0.00249 |
| Haiku 4.5 | $0.00002 | $0.00125 |
Grade A, and why
codex-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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/codex-review — Codex 独立审稿
重要:这是双 AI 流程的核心独立性边界。你(Claude)不审这份稿子。你只是触发 Codex 子进程,然后读 Codex 写出的报告。
工作流
用户跑 /codex-review
↓
你(Claude)通过 Bash 调用 codex exec
↓
Codex CLI 启动子进程(独立环境)
↓
Codex 读 AGENTS.md + prompts/reviewer_system.md + my-paper/draft.md + my-paper/data/analysis_results.json(如果存在)
↓
Codex 输出 review 到 my-paper/review_round_N.md
↓
你读这份 review 文件,给用户预览前 30 行
步骤
步骤 1:确定轮次号
读 my-paper/ 下已经存在的 review_round_*.md 文件,找到最大 N,新一轮为 N+1。
步骤 2:构造 Codex 调用
用 Bash 工具跑:
codex exec --skip-git-repo-check --sandbox workspace-write \
-c 'model_reasoning_effort="xhigh"' \
"Read AGENTS.md, prompts/reviewer_system.md, prompts/review_paper.md, my-paper/draft.md, and if present my-paper/data/analysis_results.json. The {manuscript} placeholder in review_paper.md should be replaced with the contents of draft.md. If analysis_results.json is present, verify every quantitative manuscript claim against it and flag any mismatch or unverifiable number as a Critical Issue. If it is absent, state that numerical verification was unavailable. Produce a structured peer review per the format in reviewer_system.md. Write your output to my-paper/review_round_<N>.md (replace <N> with the round number ${ROUND}). Do not modify any other files."
把 ${ROUND} 替换成步骤 1 算出的轮次号。
关键参数:
--skip-git-repo-check:避免 Codex 卡在 git repo 检查--sandbox workspace-write:必须——默认沙盒只读,Codex 写不进review_round_N.md-c 'model_reasoning_effort="xhigh"':审稿要用 xhigh,默认 medium 不够严- 不传
--model,让 Codex 用它的默认模型 - 显式让 Codex 读
my-paper/data/analysis_results.json(如存在):否则它只能审文字,不能兑现"数字必须能对上数据"这条红线
步骤 3:等 Codex 完成
Codex 子进程通常运行 1-4 分钟(视论文长度、网络和账号状态);慢时可能到 5-10 分钟。需要快速对比可参考预跑好的 examples/global-aerosol-2010-2024/review_round_1.md。
步骤 4:读结果并预览
Codex 跑完后:
- 检查
my-paper/review_round_<N>.md是否存在 - 不存在 → 报错"Codex did not produce review file. Check
codex --helpand Codex login status." - 存在 → 读它,提取
## Score那一行的分数 - 用 markdown 预览给用户看
- 同时把这次运行的元数据写到
my-paper/runs/<ts>/codex_review_meta.json:- 轮次号
- Codex 版本(通过
codex --version) - 论文字数
- Score
- Verdict
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.
- 12d ago First seen · 103 lines · 18 tokens per session scan A fd65c6487813
codex-review is a command published in the GitHub repository dengxu11111/dual-ai-paper-coach (12 stars, last pushed 3mo ago), licensed MIT. It adds 18 tokens to every session and 1,247 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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