Agent skill · research science · yuan1z0825
nature-paper-card
Build a source-grounded deep-reading Paper Card for one scientific paper, preprint, PDF, DOI, arXiv page, publisher article, or pasted paper text. Use when the user asks for a Paper Card, deep-reading literature card, single-paper deep analysis, module-by-module analysis, experiment-to-claim evidence chain, conclusion-boundary audit, critical analysis, knowledge connections, or candidate research ideas. Produce the fixed Sections 01-16 covering bibliographic position, research question, background route, pain point, core insight, method and module logic, essential formulas, experiment-to-claim evidence, conclusion boundaries, author-stated limitations, critical analysis, learned knowledge, knowledge connections, and testable research ideas. Do not use for full-paper bilingual translation, formal peer-review reports, batch literature monitoring, academic-English collection, comprehension quizzes, or public-article writing.
Why this skill is useful
Adds a comprehensive script for generating evidence-grounded research cards from scientific papers, which includes specific workflows and validation processes not commonly found in public documentation.
What it needs
Requires python installed locally. About 5k tokens when loaded. Last updated 2026-08-07. 33,818 stars on the source repository.
What this skill does
Nature Paper Card - Router Use this skill to turn one paper into an evidence-grounded research card, not a translated abstract, generic summary, reviewer report, or publication article. The skill uses: a static core under static/core/ for principles, workflow, and the fixed output contract; one paper-type fragment under static/fragments/papertype/; on-demand references for evidence labels, the exact card schema, and research-idea checks. Routing protocol Follow these steps every time. 1. Load the manifest and core layer Read manifest.yaml, then read every file under alwaysload. Do not generate the card from this router alone. 2. Establish the source boundary Identify which material is available: full paper with figures and tables; paper text without reliable layout; abstract or metadata only; an existing nature-reader artifact with stable source IDs. Prefer an existing nature-reader artifact when supplied. Do not repeat full bilingual translation or figure extraction. If only partial material is available, create a visibly partial card and mark every unsupported section Not assessable from supplied material. For a PDF or nature-reader source-map JSON, the bundled script is mandatory. 1. Resolve SKILLDIR as the directory containing this loaded SKILL.md. 2. Verify SKILLDIR/scripts/preparepaper.py exists. 3. Run exactly the bundled script by its resolved path: Add --render-dir WORKDIR/rendered-pages when visual page review is needed. Inspect the script exit code and the bundle validation block before drafting. For source-map input, also inspect locatorsummary and unlocatedblocks. Only records under pages have verified positive PDF page locators. Missing or invalid page locators remain in unlocatedblocks with an explicit status and must be cited structurally, never as page 1. Never write inline Python, a temporary extraction script, or a replacement script during a Paper Card run. Never patch the bundled scripts during a normal Paper Card run. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills yuan1z0825/nature-paper-card