Agent skill

Nature Reader

by jing1312 in jing1312/nature-figure-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.

MITAuto-check passedResearch & Science

Install Nature Reader

skills CLI
$ npx skills add jing1312/nature-figure-skill --skill nature-reader -a claude-code

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

GitHub CLI
$ gh skill install jing1312/nature-figure-skill nature-reader --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/jing1312/nature-figure-skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nature-reader .claude/skills/nature-reader && 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
nature-reader
GitHub stars
168
Token cost
~2.8k tokens
SKILL.md length
1,469 words
Files
5 (incl. references)
Skills in repo
4
Repo updated
First seen
Licence
MIT

At a glance

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.

  • Works in 6 steps: Identify the source and paper type → Build a full-document source map before… → Translate conservatively → …
  • The user asks to translate
  • SKILL.md covers When to use, Non-negotiable defaults, Core principle and Workflow, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Nature Reader is an agent skill from jing1312/nature-figure-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 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.

Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `README.md`, `evals/evals.json` and `references/grounding-rules.md`).

It sits in Research & Science, covering Translation, Academic paper search and Source-grounded notebooks. It works with arXiv. The repository describes itself as: 追求每一张图的完美,支持直接修改图片,让每一张图都经得起审稿人的追问. The licence is MIT.

When your agent uses it

  • The user asks to translate
  • Make 中英文对照/原文对照/全文翻译解读
  • Extract figures
  • Tables into the right positions

Example prompts

  • “/nature-reader”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Identify the source and paper type
  2. Build a full-document source map before translating
  3. Translate conservatively
  4. Extract and place figures and tables near the relevant discussion
  5. Generate the Markdown file
  6. Answer follow-up questions with source grounding

What it can do on your machine

Read from SKILL.md and the folder at commit af77802. 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

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    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

Nature Reader loads about 2.8k tokens when it runs, and up to ~4.2k if it reads all its reference files. Until then it costs about 134 tokens; SKILL.md has 1,469 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~134
When it runs · the whole SKILL.md, loaded when a task matches
~2.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.2k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from jing1312/nature-figure-skill at commit af77802, republished under its MIT licence (© jing1312). 1,469 words, ~2,750 tokens.

Download SKILL.mdSave it as .claude/skills/nature-reader/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
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:

markdown
<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 and tables near the relevant discussion

Do not try to recreate the PDF pixel-for-pixel. Preserve semantic proximity instead.

Default placement rule:

  • crop each figure/table into assets/ and show it near its first substantive mention in the body text
  • keep the caption attached to the figure/table
  • show both original caption and Chinese caption translation
  • if the caption contains critical details, keep caption and figure together
  • if a table is central to the claim, keep it near the paragraph that interprets it
  • if a figure/table appears before the body discussion in PDF layout, still place it where it best supports the reading flow and add Placed near: p.X SYYY
  • if a later section mentions the same figure/table again, link back to the already inserted figure/table block instead of duplicating it

If the paper has a complex multi-column layout, prefer a clean reading layout over exact visual mimicry.

Show full SKILL.md (568 more words)Show less
4b. Crop figures and tables tightly

When extracting a figure or table image:

  • crop only the figure or table content area, not the whole page
  • use the smallest rectangle that fully contains the visual object
  • exclude page headers, footers, surrounding prose, and unrelated margins
  • keep the caption separate unless the caption is part of the requested visual crop
  • if the crop box is uncertain, mark it as approximate instead of enlarging it

Precision matters more than convenience here. A slightly smaller but correct crop is better than a wider crop that includes unrelated page content.

Figure/table blocks in paper.md should use this shape:

markdown
<a id="F001"></a>
### Fig. 1. [short translated title]

**Placed near:** p.3 S012
**Source:** p.4 C001

![Fig. 1](assets/fig1.png)

**Original caption:** [caption text]

**中文图注:** [caption translation]

**Reading note:** [brief explanation of what to inspect in the figure]
5. Generate the Markdown file

Default output is a single full-paper paper.md file.

The Markdown must include:

  • metadata header
  • a short page/section index
  • page-level or section-level divisions for long papers
  • paragraph-level original/Chinese pairs for all extractable substantive text
  • figure and table blocks placed near the relevant discussion
  • source anchors on every substantive text, figure, caption, and table block
  • a terminology table for recurring technical terms
  • a short 阅读提示 / critical reading notes section only after the bilingual body, not as a replacement for it
  • short uncertainty notes only when extraction is weak

Do not add an interactive Q&A panel or follow-up widget in the Markdown deliverable. If the user later asks a question, answer it in chat using the source map rather than embedding a conversational panel in the artifact.

If a browser preview is explicitly requested, a companion reader.html can be generated as a secondary artifact, but the Markdown file remains the primary output.

6. Answer follow-up questions with source grounding

When the user asks a question after the file is created:

  • identify the most relevant source blocks first
  • answer from the paper, not from memory
  • cite the exact block IDs and page numbers
  • if the answer depends on a figure or table, cite that too
  • if the paper does not support the claim, say so plainly

Every substantive answer should include a source pointer such as:

  • p.4 S012-S013
  • Fig. 2 caption
  • Table 1

If the answer is a synthesis across several blocks, list all supporting locations.

Output contract

Prefer these outputs:

  • paper.md for the full-paper Markdown artifact
  • source_map.json for stable source anchors
  • translation_notes.md for terminology, uncertainty, and layout notes
  • assets/ for extracted figures or cropped snippets when needed
  • reader.html only when the user explicitly wants a browser preview

Do not hide missing information. If the source is incomplete, label the output as draft mode.

Before final response, verify:

  • paper.md contains **Original:** and **中文:** block pairs
  • every image/table link used in paper.md exists under assets/
  • every figure/table in assets/ has a corresponding Markdown block and source pointer
  • source_map.json parses as JSON and includes source block IDs
  • translation_notes.md records skipped, uncertain, or draft-mode content

Tooling guidance

If the input is a PDF, load the pdf skill first for extraction and OCR guidance. If the user asks for a richer browser view, use web-artifacts-builder or frontend-design only as a preview layer on top of the Markdown workflow. If the user wants citation-level grounding to original text, keep the source map explicit and do not lose the page or block IDs.

Quality bar

Good output feels like a paper reader, not a machine translation dump.

It should let a reader:

  • read the paper in two languages
  • see where a claim came from
  • inspect the nearby figure or table
  • move through a complete Markdown file without losing source traceability

© jing1312, 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 (references) in skills/nature-reader of jing1312/nature-figure-skill.

  • SKILL.md
  • README.md
  • evals/evals.json
  • references/grounding-rules.md
  • references/output-spec.md

Open the folder on GitHubat commit af77802

Compare with similar skills

Nature Reader next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Nature Reader compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Nature Reader this skilljing1312/nature-figure-skill168—~2.8kAutomated safety check: PassMIT
Nature Readeraiskillstore/marketplace4301 repos~1.1kAutomated safety check: PassNone
Paper LensYSQ-boop/paper-lens101—~1.3kAutomated safety check: PassApache-2.0
Paper ReadingEdwardxlai/easyread871—~567Automated safety check: PassMIT
Arxiv TranslatorLeey21/arxiv-translator496—~627Automated safety check: PassMIT
Paper Interpretationdigoal/blog8.6k—~1.5kAutomated safety check: PassGPL-2.0

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Works with

Questions about Nature Reader

What does Nature Reader do?

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. Nature Reader is an agent skill from jing1312/nature-figure-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.

When should I use Nature Reader?

Nature Reader fits situations like: the user asks to translate; make 中英文对照/原文对照/全文翻译解读; extract figures; tables into the right positions.

How do I install Nature Reader in Claude Code?

Run `npx skills add jing1312/nature-figure-skill --skill nature-reader -a claude-code`. Or copy the skill folder (skills/nature-reader in jing1312/nature-figure-skill) into .claude/skills/nature-reader in your project. Claude Code loads it when a task matches its description.

How do I install Nature Reader in Codex?

Run `npx skills add jing1312/nature-figure-skill --skill nature-reader -a codex`. Or copy the skill folder (skills/nature-reader in jing1312/nature-figure-skill) into .agents/skills/nature-reader in your project. Codex loads it when a task matches its description.

Can I use Nature Reader 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 jing1312/nature-figure-skill --skill nature-reader -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nature-reader, .gemini/skills/nature-reader, .github/skills/nature-reader and .opencode/skills/nature-reader in your project.

What does Nature Reader need to run?

SKILL.md names no scripts, command-line tools or credentials: Nature Reader is instructions for the agent only.

Does Nature Reader 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 Nature Reader 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. Review the folder before installing.

What licence does Nature Reader use?

Nature Reader 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 Nature Reader use?

About 2.8k tokens (SKILL.md is roughly 11k 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 1.4k tokens, read only when the agent opens those files.

What are the alternatives to Nature Reader?

Skills that share tags, products or a category with Nature Reader: Nature Reader (aiskillstore/marketplace, 430 stars), Paper Lens (YSQ-boop/paper-lens, 101 stars), Paper Reading (Edwardxlai/easyread, 871 stars) and Arxiv Translator (Leey21/arxiv-translator, 496 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nature Reader?

jing1312 (a GitHub user) maintains it in jing1312/nature-figure-skill, which has 168 GitHub stars. The repository holds 4 skills in this directory. The repository was last updated on September 8, 2026.

Source: jing1312/nature-figure-skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.