Skip to content
Automation
Skill

/nature-reader

Build full-paper Chinese-English side-by-side, figure/table-aware, source-grounded Markdown readers for journal or conference papers from PDF, DOI, arXiv, publisher HTML, or pasted text. Use whenever the user asks to translate or read a paper, make 中英文对照/原文对照/全文翻译解读, extract

From plugin
lihongwei-cn
5200 skills1 agent
Install
$ npx -y skills add LiHongwei-cn/lihongwei-cn --skill nature-reader --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/nature-reader

Context preview

The summary Claude sees to decide when to auto-load this skill.

Build full-paper Chinese-English side-by-side, figure/table-aware, source-grounded Markdown readers for journal or conference papers from PDF, DOI, arXiv, publisher HTML, or pasted text. Use whenever the user asks to translate or read a paper, make 中英文对照/原文对照/全文翻译解读, extract

SKILL.md

nature-reader.SKILL.md
name: nature-reader
description: Build full-paper Chinese-English side-by-side, figure/table-aware, source-grounded Markdown readers for journal or conference papers from PDF, DOI, arXiv, publisher HTML, or pasted text. Use whenever the user asks to translate or read a paper, make 中英文对照/原文对照/全文翻译解读, extract figures or tables into the right positions, preserve figure/table placement near relevant prose, or keep exact source anchors for every block. This skill must not degrade into a summary-only output unless the user explicitly asks for a summary.

Full-Paper Markdown Reader

Use this skill to turn a research paper into a complete Markdown reading artifact.

The default output should read like a bilingual paper companion, not a summary dump:

  • keep the extractable prose, paragraph structure, and section flow
  • show original text and Chinese translation together at block level
  • extract figures and tables as assets and place them at the first substantive mention or interpretation point
  • keep captions attached to figures/tables with English caption text and Chinese caption translation
  • preserve stable page and block anchors for traceability
  • write a complete `paper.md` by default, plus `source_map.json`, `translation_notes.md`, and `assets/`

This skill is for papers, preprints, and conference proceedings across disciplines. It is not limited to Nature-family journals.

When to use

Use this skill when the user wants any of the following:

  • translate an entire paper into a complete Markdown document
  • make a paper easier to read without losing the original wording
  • generate a full-paper reading file with original/translation alignment
  • keep figures or tables visually close to the claims they support
  • preserve exact source locations for every substantive block
  • build a source-grounded markdown artifact rather than a slide deck or short summary

If the user only wants a summary, use a summarization skill instead. If the user only wants citation search, use a citation skill instead.

Non-negotiable defaults

When the user asks for paper translation, reading, `nature-reader`, `中英文对照`, `原文对照`, `全文翻译`, or `翻译解读`, produce a paragraph-level bilingual reader by default.

Do not replace the reader with:

  • a Chinese-only summary
  • a paper review without original/translation alignment
  • figure captions without figure/table crops
  • a list of key points detached from source locations
  • only the abstract, introduction, or selected highlights

If constraints prevent full processing, still create a draft reader and clearly label missing pages, missing figures/tables, untranslated blocks, or low-confidence OCR/crops in `translation_notes.md`.

Core principle

Translate for meaning, not for style. Preserve the paper's structure, evidence, hedging, terminology, equations, units, and citation markers. Keep the output in prose paragraphs unless the source itself is tabular or list-like. Do not collapse the paper into keyword bullets or slide-style notes.

The reading file should help a reader move between:

  • original text
  • translated text
  • source location
  • figure or table evidence

Each substantive source block should have a stable anchor and a visible bilingual pair:

<a id="S001"></a>
**Source:** p.1 S001

**Original:** [source paragraph]

**中文:** [faithful Chinese translation]

For copyrighted publisher PDFs, keep chat responses short and point to the local artifact. In local `paper.md`, include the bilingual reader only for the user-provided source file or clearly lawful open-access content; avoid reproducing large copyrighted text directly in chat.

Workflow

1. Identify the source and paper type

Determine whether the source is:

  • selectable-text PDF
  • scanned PDF
  • publisher HTML
  • DOI or arXiv link
  • pasted text or notes

Then identify the paper type at a high level:

  • discovery or mechanism paper
  • methods or algorithm paper
  • resource or dataset paper
  • conference paper
  • review or perspective

This helps decide how tightly to couple text, figures, and captions.

2. Build a full-document source map before translating

If the user provides a full paper, process the entire document. Do not stop at the abstract, introduction, or a few representative pages unless the user explicitly asks for a preview.

Create stable IDs for source blocks:

  • `S001`, `S002`, ... for body text
  • `C001`, `C002`, ... for captions
  • `F001`, `F002`, ... for figures
  • `T001`, `T002`, ... for tables

For each block, capture:

  • page number
  • block type
  • original text
  • translation
  • reading-order index
  • nearby figure or table references
  • first substantive figure/table mention when applicable
  • confidence level when extraction is uncertain

Keep the source map stable so later questions can point back to the same IDs. For long papers, add a page index so the reader can jump across the whole document without losing location.

3. Translate conservatively

Translate every extractable substantive block with these rules:

  • preserve technical terms unless a standard Chinese equivalent is clearly better
  • keep gene names, protein names, formulas, model names, and symbols intact
  • keep citations, superscripts, subscripts, and numeric values unchanged
  • do not collapse methods details into vague prose
  • keep paragraph order and section order unless the user asks for restructuring
  • mark uncertain text instead of guessing when OCR or layout extraction is weak
  • keep the source's paragraph form; do not convert dense prose into bullet-point keywords
  • do not silently skip Methods, limitations, data availability, code availability, competing interests, or extended captions
  • if the paper is too long for one pass, write `paper.md` incrementally by page/section and mark pending blocks rather than switching to summary mode

If a sentence contains multiple claims, keep the translation readable but do not split away the original evidence chain.

4. Extract and place figures a

Read more
Ships withlihongwei-cn

MUNDO - THE EMPEROR. Complete AI orchestration system with 1208 skills, 25 capability modules, self-evolving, collective consciousness. GitHub Actions 24/7 automation.

Get the whole plugin
Stats
5
Stars
1
Forks
Maintained
Maintenance
Python
Language
MIT
License
1mo ago
Last commit
4mo ago
Created

Repo: LiHongwei-cn/lihongwei-cn

Other skills on lihongwei-cn.

cheat-on-content
Skill

cheat-on-content

给所有想把"感觉"变成可校准预测的内容创作者。**方法论通用**——打分 → 盲预测 → T+3d 复盘 → 进化 rubric 的循环适用任何能被量化(播放 / 阅读 / 收听 / 点击)的内容。**rubric 是循环的内容,不是循环本身**——当前内置一份观点视频 rubric(参考博主 25+…

cheat-bump
Skill

cheat-bump

提议并执行 rubric 或 bucket 升级。两种模式:**完整 rubric bump**(最高风险动作,5 步强制 + 跨模型审核)和 **--bucket-only 轻量重校**(只换 bucket 边界,不动 rubric 公式)。**Phase 2 强制走 cheat-score-blind…

cheat-init
Skill

cheat-init

cheat-on-content 的首次 onboarding 与脚手架创建器。统一流程——所有用户都走相同 5 阶段闭环,唯一区别是"发过视频的人"会在 init 时多一步:抓取已有视频建立历史 context(用于后续 cheat-seed 给更贴合的选题、更准的…

cheat-migrate
Skill

cheat-migrate

把老用户的 .cheat-state.json 升级到当前 schema_version。读 migrations/registry.md 算迁移链,按顺序应用每一步迁移文件。幂等:跑两次结果一样。失败停在中间版本不前进。触发词:"迁移"/"升级 state"/"migrate"/"我的 state…

cheat-persona
Skill

cheat-persona

从复盘评论数据派生 / 刷新账号的受众画像,写入 audience.md。这是和 rubric 平行的第二个派生物——rubric 答"怎么打分",persona 答"谁在看"。cheat-seed 选题 / 写稿时读它。**audience.md 含实绩信号,cheat-score-blind…