nature-reader
Create source-grounded Chinese-English paper readers with aligned text, figures, tables, and equations. Use for 全文翻译、中英文对照、论文精读 or source-linked questions about a paper; respect a requested excerpt or question without generating a full reader.
This skill includes scripts your agent can run. See which files
Overview
Full-Paper Markdown Reader — Router
Routing protocol
First distinguish creating a reader from answering a question or translating an excerpt. For a source-linked question, read references/grounding-rules.md and inspect only the relevant source material; reuse existing source-map IDs when available. Do not regenerate the reader or require a full source map before answering. For an explicit excerpt request, apply extraction, translation, and grounding rules to that excerpt. The full-artifact workflow below applies when the user requests a reader or full-paper translation.
For a new task, load the core and matching resources below. Reuse already loaded guidance on follow-ups; load more only when the task needs it.
1. Load the manifest and the core layer
Read manifest.yaml. It declares the source_format axis, the allowed values, and the file paths each value maps to.
Also read every file listed under always_load. These hold the core principles, the reading workflow, and the output contract that apply to every reading job, plus the shared Terminology Ledger used to build the recurring-term table.
2. Detect the source format
Decide the source_format value using the manifest's detect: hint and the user's input:
pdf-text— selectable-text PDF. Default.scanned-pdf— image-only or OCR-required PDF.html— publisher or preprint HTML page.doi-arxiv— a bare DOI or arXiv link that must be resolved first.pasted-text— pasted prose or notes with no retrievable original layout.
State the detected value in one short line to the user before processing, so they can correct you cheaply. A source may map to more than one value (for example a DOI that resolves to a PDF); load the resolution fragment first, then the fragment for the resolved artifact.
3. Load the matching fragment(s)
Read the file mapped for the detected source_format. Do not read every fragment in static/. Load only what step 2 selected.
4. Build the reader using the loaded material
Apply the loaded fragments in this priority order:
- Core principles (
core/principles.md) — bilingual reader by default, translate for meaning, never degrade to a summary, copyright caution. - Source-format fragment — how to extract text, figures, and tables for this input.
- Reading workflow (
core/workflow.md) — the six-step source-map-first process. - Output contract (
core/output-contract.md) — required files and the pre-response verification checklist.
Build the Terminology Ledger as you translate (../nature-shared/core/terminology-ledger.md); it becomes the paper.md recurring-term table and the source_map.json glossary.
If constraints prevent full processing, still create a draft reader and label missing pages, figures, or low-confidence crops in translation_notes.md. Do not switch to summary mode.
5. Reach for references only when needed
The files under references/ are deep references, not defaults. Open them on demand per the references.on_demand table in the manifest:
- detailed figure/table cropping and placement →
references/figure-extraction.md. - exact field schema for
paper.md/source_map.json→references/output-spec.md. - equations, mathematical expressions, chemical formulae, or image-only formulae →
references/equation-handling.md. - answering follow-up questions with source citations →
references/grounding-rules.md.
Files 20 files
- scripts/validate_reader_math.pyScript
- SKILL.mdMarkdown
- agents/openai.yamlFile
- evals/evals.jsonFile
- manifest.yamlFile
- README_EN.mdMarkdown
- README.mdMarkdown
- references/article-anatomy.mdMarkdown
- references/equation-handling.mdMarkdown
- references/figure-extraction.mdMarkdown
- references/grounding-rules.mdMarkdown
- references/output-spec.mdMarkdown
Install it by hand
Deska runs this for you, for every agent you pick. To run it yourself in a terminal:
npx skills add yuan1z0825/nature-skills --skill nature-readerMore from yuan1z0825
- nature-figureyuan1z0825Create, revise, audit, and export manuscript scientific figures in Python or R. Use for 论文配图、科研绘图、多面板图 and submission-ready plots, or explicitly requested AI-generated graphical abstracts and mechanism schematics. Not for interactive dashboards, data cleaning, or statistics-only analysis.16.6K installs on skills.sh0
- nature-polishingyuan1z0825Polish, translate, or tighten existing academic prose while preserving facts, terminology, and evidence boundaries. Use for 论文润色、学术翻译、正文精简, or manuscript LaTeX layout fixes. Use nature-writing when the main task is drafting new sections or rebuilding the manuscript argument.15.4K installs on skills.sh0
- nature-paper2pptyuan1z0825Create or improve a Chinese academic PPTX from a scientific paper or research reading notes, with source figures and speaker notes. Use for 论文做PPT、文献汇报、组会PPT and paper-based conference or defense presentations.14.1K installs on skills.sh0
- nature-citationyuan1z0825Find and verify Nature/CNS-family literature supporting manuscript claims, with claim-to-source mapping and reference-manager export. Use for Nature系列引用、CNS支撑文献、分段补引用 when this journal scope is requested; use broader literature search for unrestricted sources.13.8K installs on skills.sh0
- nature-datayuan1z0825Draft or audit manuscript Data/Code Availability statements, dataset access routes, repository plans, and FAIR metadata. Use for 数据可用性声明、数据共享、数据仓库选择 and dataset citations; not general data cleaning or statistical analysis.13.6K installs on skills.sh0