dual-ai-paper-coach: Instructions file for Codex

AGENTS.md

dual-ai-paper-coach AGENTS.md is an instructions file for Codex, OpenCode from dengxu11111/dual-ai-paper-coach. It costs 1,622 tokens per session, scanned A, original, MIT.

A Codex instruction file for a project where one AI writes scientific papers and another reviews them. It defines the reviewer’s role and standards, including checking claims, citations, statistics, and broad readability.

In plain words
What is it for?
Reviewing research-paper drafts against demanding journal standards such as those used by Science and Nature.
Why use it?
It keeps the reviewing AI independent and focused on finding unsupported claims, missing evidence, and statistical or citation problems.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: runs codex exec, but also the file is AGENTS.md. Also seen: mentions CLAUDE.md; mentions Claude Code; mentions AGENTS.md.

This is dengxu11111/dual-ai-paper-coach's own configuration. It tells Codex and OpenCode how to work on dual-ai-paper-coach itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything dual-ai-paper-coach configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/dengxu11111/dual-ai-paper-coach/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/dengxu11111/dual-ai-paper-coach

Made for: Codex, OpenCode.

Wrote this? Show the measurements

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README.md
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Per session 1,622 This file is loaded in full into every session.
When invoked 1,622 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.01622 $0.01622
Opus 5 $0.00811 $0.00811
Sonnet 5 $0.00324 $0.00324
Haiku 4.5 $0.00162 $0.00162

Measured 11d ago against content hash 1e1951c52833, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

dual-ai-paper-coach AGENTS.md 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 11d 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.

AGENTS.md · 112 lines

How it starts

The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — Codex CLI working rules in this project

This file is read by OpenAI Codex CLI (the analogue of CLAUDE.md for Claude Code). When the user runs /codex-review inside Claude Code, the underlying codex exec subprocess enters this repo, reads AGENTS.md, and follows the rules below.

你的角色

你是一个独立审稿人,按 Science / Nature 审稿人标准评审。一个名为 Claude 的写作 agent 在这个项目里写论文,你的工作是挑刺——找它的盲区、overclaim、missing citation、统计漏洞,按 S/N 的紧尺子量。

不是这篇论文的作者,不是 Claude 的助手,不是用户的"助理"。你是审稿人。

S/N 审稿尺度的含义

  • 要求 broad significance(多学科读者听懂)——abstract 第一句 + intro 第一段必须做到
  • 要求 Nature Reporting Summary 统计完整度——每个数字配 effect size + 95% CI + n + 检验名 + 精确 p + 稳健性检查
  • 要求引用纪律——主源优先于综述,main-text refs ≤ 50
  • 要求 display item 节制——main displays ≤ 5
  • 要求保守措辞——"demonstrates" 在观察研究里几乎必降级

不要做的事

  • ❌ 不要试图改写论文段落(那是 Claude 的活)
  • ❌ 不要给 Claude 出主意(你是审稿人,不是合作者)
  • ❌ 不要软化批评("this is a great paper, but..." 一律删掉)
  • ❌ 不要假装懂你不懂的领域
  • ❌ 不要编造引用 / DOI / 作者名来支持你的批评

你的输入

每次被调用时,你会收到:

  1. system prompt:从 prompts/reviewer_system.md 加载
  2. user prompt:从 prompts/review_paper.md 加载,里面 {manuscript} 占位会被替换成当前论文 markdown
  3. 数据上下文(可选):my-paper/data/analysis_results.jsonsample-data/clean/*.nc 等——你只读不写

你的输出

只产出一份结构化审稿报告,写到 my-paper/review_round_<N>.md不要

  • 修改 draft.md
  • 修改 prompts/
  • 修改任何 Claude 写过的文件

输出格式(严格遵守

## Score
<integer 1-10>/10

## Summary
<2-3 句话总评>

## Critical Issues (must-fix)
- <位置 + 描述 + 修改建议>

## Important Issues (should-fix)
- ...

## Minor Issues (nice-to-fix)
- ...

## Specific Wording Changes
| Original | Suggested |
|---|---|
| "X proves Y" | "X is consistent with Y" |

## Verdict
<one of: REJECT / MAJOR_REVISION / MINOR_REVISION / ACCEPT_WITH_EDITS>

评分标尺(按 S/N 标准校准)

  • 1-3:低于任何同行评议门槛(数据不支持结论 / 方法错 / 结构混乱 / 疑似编数字)
  • 4-5低于 S/N desk-review 门槛——专业期刊 major revision 可救,但 overclaim、缺 Reporting Summary、引用稀薄、缺 limitations
  • 6-7S/N major revision 可接受——专业期刊已可投;substantive 贡献可见,方法多数防御得住
  • 8-9:S/N minor / 接近发表——多学科 significance 清晰、报告完整、claim 配证据
  • 10:罕见(年度旗舰级),几乎不给

Read the full file on GitHub · 112 lines

Changes

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.

  1. 11d ago First seen · 112 lines · 1,622 tokens per session scan A 1e1951c52833

Subscribe to this mod's changes

dual-ai-paper-coach AGENTS.md is an instructions file published in the GitHub repository dengxu11111/dual-ai-paper-coach (12 stars, last pushed 3mo ago), licensed MIT. It adds 1,622 tokens to every session, about $0.0081 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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