Agent skill

Qinyan Nature Review

by LeonChaoX in LeonChaoX/qinyan-academic-skills

面向 Nature、Nature Communications 及高影响力期刊的可追溯投稿前评审技能。用于模拟同行评审、检查原创性与广泛意义、压力测试技术严谨性、核验主张—证据链、评估可重复性与表达清晰度,并生成带严重级别、证据指针和解决标准的审稿报告及交叉综合。触发场景包括 Nature review、模拟审稿、投稿前预审、peer review、reviewer…

MITAuto-check passedResearch & Science

Install Qinyan Nature Review

skills CLI
$ npx skills add LeonChaoX/qinyan-academic-skills --skill qinyan-nature-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeonChaoX/qinyan-academic-skills qinyan-nature-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeonChaoX/qinyan-academic-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'skills/沁言学术skills/qinyan-nature-review' .claude/skills/qinyan-nature-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
qinyan-nature-review
GitHub stars
944
Token cost
~546 tokens
SKILL.md length
105 words
Files
5 (incl. scripts, references)
Skills in repo
31
Repo updated
First seen
Licence
MIT

At a glance

面向 Nature、Nature Communications 及高影响力期刊的可追溯投稿前评审技能。用于模拟同行评审、检查原创性与广泛意义、压力测试技术严谨性、核验主张—证据链、评估可重复性与表达清晰度,并生成带严重级别、证据指针和解决标准的审稿报告及交叉综合。触发场景包括 Nature review、模拟审稿、投稿前预审、peer review、reviewer…

  • Works in 5 steps: Review setup:输入范围、评审边界、稿件核心命题和可见证据。 → Lens A — Conceptual… → Lens B — Technical… → …
  • Research & Science work in your project
  • SKILL.md covers 默认评审包, 评审原则, 执行流程 and 严重级别, plus 3 more sections
  • Runs Python scripts from its folder; calls python

What it does

Qinyan Nature Review is an agent skill from LeonChaoX/qinyan-academic-skills. 面向 Nature、Nature Communications 及高影响力期刊的可追溯投稿前评审技能。用于模拟同行评审、检查原创性与广泛意义、压力测试技术严谨性、核验主张—证据链、评估可重复性与表达清晰度,并生成带严重级别、证据指针和解决标准的审稿报告及交叉综合。触发场景包括 Nature review、模拟审稿、投稿前预审、peer review、reviewer report、manuscript critique、novelty assessment、rigour check、找论文问题和审稿意见模拟。

Its SKILL.md is about 550 tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/report-contract.md` and `references/review-framework.md`).

It sits in Research & Science. The repository describes itself as: A curated, multilingual library of 182 installable AI agent skills for end-to-end academic research—spanning literature discovery, scientific writing, grant development… The licence is MIT.

When your agent uses it

  • Research & Science work in your project

Example prompts

  • “/qinyan-nature-review”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Review setup:输入范围、评审边界、稿件核心命题和可见证据。
  2. Lens A — Conceptual significance:问题重要性、原创性、广泛读者价值。
  3. Lens B — Technical integrity:设计、方法、统计、对照、可重复性。
  4. Lens C — Evidence and communication:主张—证据一致性、图文一致性、可读性与透明度。
  5. Cross-review synthesis:共识、分歧、优先修复顺序和未能评估事项。

What it can do on your machine

Read from SKILL.md and the folder at commit df5a498. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Qinyan Nature Review loads about 546 tokens when it runs, and up to ~1.8k if it reads all its reference files. Until then it costs about 70 tokens; SKILL.md has 105 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~70
When it runs · the whole SKILL.md, loaded when a task matches
~546
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from LeonChaoX/qinyan-academic-skills at commit df5a498, republished under its MIT licence (© LeonChaoX). 105 words, ~546 tokens.

Download SKILL.mdSave it as .claude/skills/qinyan-nature-review/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
qinyan-nature-review
description
面向 Nature、Nature Communications 及高影响力期刊的可追溯投稿前评审技能。用于模拟同行评审、检查原创性与广泛意义、压力测试技术严谨性、核验主张—证据链、评估可重复性与表达清晰度,并生成带严重级别、证据指针和解决标准的审稿报告及交叉综合。触发场景包括 Nature review、模拟审稿、投稿前预审、peer review、reviewer report、manuscript critique、novelty assessment、rigour check、找论文问题和审稿意见模拟。

沁言 Nature 投稿前预审

以审稿人的证据标准审查稿件,不扮演编辑、不预测录用,也不替作者编写回复信。

默认评审包

除非用户指定其他格式,生成:

  1. Review setup:输入范围、评审边界、稿件核心命题和可见证据。
  2. Lens A — Conceptual significance:问题重要性、原创性、广泛读者价值。
  3. Lens B — Technical integrity:设计、方法、统计、对照、可重复性。
  4. Lens C — Evidence and communication:主张—证据一致性、图文一致性、可读性与透明度。
  5. Cross-review synthesis:共识、分歧、优先修复顺序和未能评估事项。

这些是评审视角,不是虚构的审稿人身份、机构或专业履历。

评审原则

  • 只依据用户提供的稿件、图表、数据和已核验来源。
  • 对每条实质性问题分配稳定 Issue key 和唯一 Concern ID。
  • 为问题绑定 Claim pointer 与 Evidence pointer;缺失时写 NOT_LOCATABLE。
  • 把“缺少材料无法判断”与“材料显示存在缺陷”分开。
  • 只有同一 Issue key 被至少两个评审视角独立提出,才能称为共识。
  • 给出可验证的 Resolution test,不只说“需要更多实验”。
  • 不以写作风格问题掩盖科学问题,也不把偏好包装成硬性要求。

执行流程

  1. 界定输入。 说明收到全文还是部分章节,以及缺失材料对结论的影响。
  2. 抽取共享事实库。 记录核心命题、关键证据、目标读者、方法设计和作者承认的限制。
  3. 建立问题台账。 按评审维度登记证据位置、严重级别、适用性和解决标准。
  4. 执行三种视角。 共享事实,但分别强调概念、技术、证据与表达;避免人为制造分歧。
  5. 生成交叉综合。 合并相同问题,保留不同权重,按 P0/P1/P2 排序。
  6. 运行一致性校验。 把报告保存为 Markdown,执行 python scripts/review_consistency.py <report.md>。
  7. 交付边界。 明确哪些判断不能从当前材料得出,避免给出虚假编辑决定。

评审维度与严重级别读取 references/review-framework.md。报告字段与综合规则读取 references/report-contract.md。

严重级别

  • P0:核心命题无法由现有设计或证据建立;通常需要改变主张或补充关键验证。
  • P1:重要缺陷会显著削弱可信度、可重复性或解释,但存在明确修复路径。
  • P2:局部清晰度、报告完整性或呈现问题,不改变主要结论。

严重级别表示对论证的影响,不等同于接收、修改或拒稿建议。

默认问题格式

text
Concern ID: A-M1
Issue key: evidence-causality-01
Severity: P0
Axis: claim–evidence alignment
Claim pointer: Results, paragraph 3
Evidence pointer: Fig. 2b–d
Concern: ...
Why it matters: ...
Resolution test: ...

红线

  • 不虚构审稿人身份、稿件行号、图件内容、实验、文献或编辑政策。
  • 不把期刊适配度陈述为确定事实。
  • 不把领域偏好写成普遍方法学要求。
  • 不把同一问题换词重复以制造“多人共识”。
  • 不替作者隐去不利结果或合理限制。
  • 用户要求回复审稿意见时,先完成问题解析,再交由适合的回复/写作流程。

资料路由

任务读取
原创性、意义、严谨性、统计、复现与表达检查references/review-framework.md
问题字段、三视角结构、共识规则与最终 QAreferences/report-contract.md

© LeonChaoX, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts, references) in skills/沁言学术skills/qinyan-nature-review of LeonChaoX/qinyan-academic-skills.

  • SKILL.md
  • agents/openai.yaml
  • references/report-contract.md
  • references/review-framework.md
  • scripts/review_consistency.py

Open the folder on GitHubat commit df5a498

Compare with similar skills

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Nature Paper CardYuan1z0825/nature-skills47k2 repos~2.1kAutomated safety check: PassApache-2.0
Content Research Writerweapp-tailwindcss/weapp-tailwindcss1.9k25 repos~3.5kAutomated safety check: PassMIT
Last30daysmvanhorn/last30days-skill64k—~7.9kAutomated safety check: NotesMIT

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Questions about Qinyan Nature Review

What does Qinyan Nature Review do?

面向 Nature、Nature Communications 及高影响力期刊的可追溯投稿前评审技能。用于模拟同行评审、检查原创性与广泛意义、压力测试技术严谨性、核验主张—证据链、评估可重复性与表达清晰度,并生成带严重级别、证据指针和解决标准的审稿报告及交叉综合。触发场景包括 Nature review、模拟审稿、投稿前预审、peer review、reviewer…. Qinyan Nature Review is an agent skill from LeonChaoX/qinyan-academic-skills.

When should I use Qinyan Nature Review?

Qinyan Nature Review fits situations like: research & Science work in your project.

How do I install Qinyan Nature Review in Claude Code?

Run `npx skills add LeonChaoX/qinyan-academic-skills --skill qinyan-nature-review -a claude-code`. Or copy the skill folder (skills/沁言学术skills/qinyan-nature-review in LeonChaoX/qinyan-academic-skills) into .claude/skills/qinyan-nature-review in your project. Claude Code loads it when a task matches its description.

How do I install Qinyan Nature Review in Codex?

Run `npx skills add LeonChaoX/qinyan-academic-skills --skill qinyan-nature-review -a codex`. Or copy the skill folder (skills/沁言学术skills/qinyan-nature-review in LeonChaoX/qinyan-academic-skills) into .agents/skills/qinyan-nature-review in your project. Codex loads it when a task matches its description.

Can I use Qinyan Nature Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeonChaoX/qinyan-academic-skills --skill qinyan-nature-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/qinyan-nature-review, .gemini/skills/qinyan-nature-review, .github/skills/qinyan-nature-review and .opencode/skills/qinyan-nature-review in your project.

What does Qinyan Nature Review need to run?

Going by SKILL.md and its folder, Qinyan Nature Review needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Qinyan Nature Review access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Qinyan Nature Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Qinyan Nature Review use?

Qinyan Nature Review is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Qinyan Nature Review use?

About 546 tokens (SKILL.md is roughly 2.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.3k tokens, read only when the agent opens those files.

What are the alternatives to Qinyan Nature Review?

Skills that share tags, products or a category with Qinyan Nature Review: Hypothesis Generation (spacering-net/codeg, 3.9k stars), GitHub Deep Research (bytedance/deer-flow, 84k stars), Nature Paper Card (Yuan1z0825/nature-skills, 47k stars) and Content Research Writer (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Qinyan Nature Review?

LeonChaoX (a GitHub user) maintains it in LeonChaoX/qinyan-academic-skills, which has 944 GitHub stars. The repository holds 31 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeonChaoX/qinyan-academic-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.