dual-ai-paper-coach: Command for Claude Code

.claude/commands/draft.md

draft is a command for Claude Code from dengxu11111/dual-ai-paper-coach. It costs 16 tokens per session (1,179 once invoked), scanned A, original, MIT.

A command that uses project instructions and analysis results to produce a first draft of a scientific paper in Markdown.

In plain words
What is it for?
Use it to draft a paper with a title, abstract, introduction, methods, results, discussion, conclusions, and references. It is intended for research projects with prompts and optional analysis data.
Why use it?
It turns scattered writing rules, a research question, and available results into one structured draft, while marking missing numbers instead of filling them in.

Command for Claude Code

Written for Claude Code: $ARGUMENTS substitution. Also seen: mentions CLAUDE.md; mentions Codex.

This is dengxu11111/dual-ai-paper-coach's own configuration. It tells Claude Code 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/.claude/commands/draft.md
Clone the repo
git clone --depth 1 https://github.com/dengxu11111/dual-ai-paper-coach

Made for: Claude Code.

Wrote 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.

agentmods badge for draft

README.md
[![agentmods](https://agentmods.dev/badge/commands/dengxu11111/dual-ai-paper-coach/draft/github.svg)](https://agentmods.dev/commands/dengxu11111/dual-ai-paper-coach/draft)
Your own site
<a href="https://agentmods.dev/commands/dengxu11111/dual-ai-paper-coach/draft"><img src="https://agentmods.dev/badge/commands/dengxu11111/dual-ai-paper-coach/draft/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.

agentmods 80×15 button for draft

Your own site · 80×15
<a href="https://agentmods.dev/commands/dengxu11111/dual-ai-paper-coach/draft"><img src="https://agentmods.dev/badge/commands/dengxu11111/dual-ai-paper-coach/draft.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 16 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,179 The whole file, excluding the scripts and references it only reads on demand.
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 $0.00016 $0.01179
Opus 5 $0.00008 $0.00589
Sonnet 5 $0.00003 $0.00236
Haiku 4.5 $0.00002 $0.00118

Measured 11d ago against content hash 9fc3f7a05332, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

draft 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.

.claude/commands/draft.md · 65 lines

How it starts

The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/draft — 论文初稿生成

你是这个项目的主写作 agent。读 prompts/writer_system.md 作为系统准则,然后按 prompts/draft_paper.md 的模板产出一份完整的论文 markdown。

⚠️ 写作质量标杆:Science / Nature——即使实际投 GRL/ACP/JGR,本仓库也按 S/N 尺子收紧(≤ 4500 词 main text、≤ 5 displays、≤ 50 refs、Nature Reporting Summary 统计完整度、abstract 第一句必须给 broad significance)。具体细则见 prompts/writer_system.md 的 "Science / Nature Quality Standards" 段。

输入

  1. 系统准则prompts/writer_system.md(hard rules:不编数据、不编引用、保守措辞)
  2. 任务模板prompts/draft_paper.md(论文 8 段结构:Title / Abstract / Intro / Methods / Results / Discussion / Conclusions / References)
  3. 研究主题:用户输入的 $ARGUMENTS(一句话研究问题,例如 "全球气溶胶 2010-2024 时空演变")
  4. 数据my-paper/data/analysis_results.json(如果存在 → 用真实数字;不存在 → 标 [NUMBER_NEEDED: ...]
  5. 可选my-paper/CLAUDE.md 里的领域上下文

步骤

  1. prompts/writer_system.md,把它的内容当作你写作的 hard rules(特别是"never invent"和"conservative claims")
  2. prompts/draft_paper.md 模板,把里面的 {topic} 替换成 $ARGUMENTS
  3. 检查 my-paper/data/analysis_results.json 是否存在
    • 存在:把 JSON 内容附到 prompt 末尾,作为 {data_section}
    • 不存在:在 prompt 里说明"数据缺失,所有数字标 [NUMBER_NEEDED]"
  4. 写一份完整论文 markdown(Title → Abstract → Intro → Methods → Results → Discussion → Conclusions → References)
  5. 把结果写到 my-paper/draft.md
  6. 同时把这次运行的元数据存到 my-paper/runs/<ISO时间戳>/draft_meta.json
    • 用了哪个版本的 prompts
    • analysis_results.json 是否存在
    • 论文字数、段落数

输出

  • my-paper/draft.md — 论文初稿
  • my-paper/runs/<ts>/draft_meta.json — 元数据
  • 在终端用 markdown 预览前 30 行让用户看到

提醒用户的下一步

输出末尾打印:

初稿已写到 my-paper/draft.md。下一步:

  • 运行 /codex-review 让 Codex 独立审稿
  • 第一稿典型分数 4-5/10(S/N 标杆下偏低是正常的——Codex 按 Nature 审稿人尺度量你)
  • 这比"普通期刊审稿"低 1-2 分是设计上的特性,不是 bug

红线

  • ❌ 不要把 prompts/reviewer_system.md 读进来(那是 Codex 的,跟你写作无关,混进来会污染独立性)
  • ❌ 不要编数字、引用、作者名
  • ❌ 默认保守措辞:"is consistent with" 而不是 "proves"

失败排查

症状 原因 解决
报 "prompts/writer_system.md not found" 工作目录不对 确认 cwd 是项目根(含 prompts/ 目录),不是 my-paper/ 子目录
draft.md 里大量 [NUMBER_NEEDED] analysis_results.json 不存在或字段缺 回到数据准备步骤补全 JSON:study_period / main_finding / seasonal_findings 三段必须有
输出全是中文 writer_system.md 的 OUTPUT LANGUAGE 被改了 检查 prompts/writer_system.md 第一行是否还是 OUTPUT LANGUAGE: English
字数 > 5500 大幅超 draft_paper.md 模板被改 检查 prompts/draft_paper.md Constraints 段是否还有 ≤ 4500 words(S/N 标杆收紧版)
Abstract 第一句不是 broad significance writer_system.md 的 S/N 段没被遵守 重读 prompts/writer_system.md "Significance framing" 段,把 Abstract sentence 1 改成 multidisciplinary 读者能听懂的层级
Results 没有 CI / n / 检验名 不符合 Nature Reporting Summary 让 Claude 把每个数字补齐 5 元组:effect size + 95% CI + n + 检验名 + 精确 p;缺哪个标 [NUMBER_NEEDED: ...]
Claude 给出真 DOI 这就是 hallucination 立刻去 doi.org 验证;任何引用 DOI 都不能信任 Claude 的输出

Read the full file on GitHub · 65 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 · 65 lines · 16 tokens per session scan A 9fc3f7a05332

Subscribe to this mod's changes

draft is a command published in the GitHub repository dengxu11111/dual-ai-paper-coach (12 stars, last pushed 3mo ago), licensed MIT. It adds 16 tokens to every session and 1,179 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.