/nature-paper2ppt

name: nature-paper2ppt
description: Build a complete but efficient Nature-style Chinese PPTX presentation from a scientific paper, preprint, PDF, article text, abstract, figure legends, or reading notes. Use this skill whenever the user asks to make slides/PPT/PPTX for journal club, group meeting, paper sharing, thesis seminar, lab meeting, department report, or academic presentation from a research paper, not only medical papers. It identifies the paper type and argument, selects only the figures needed for the story, writes Chinese slide content and speaker notes, creates the actual .pptx deck, and performs lightweight verification with cross-platform Python tooling by default.

Purpose

Transform a scientific paper or paper-derived notes into a complete Chinese, figure-integrated PPTX presentation package with a Nature-style reporting logic.

The skill must not stop at an outline or script. The expected end product is a real `.pptx` deck. Keep supporting files minimal unless the user asks for more traceability.

Use this skill for papers across scientific fields, including:

• life sciences and medicine
• chemistry and materials science
• environmental and earth sciences
• physics and engineering
• computational biology, AI, and methods papers
• interdisciplinary Nature-family style research
• reviews, perspectives, resources, datasets, and benchmark papers

Core Principle

Use the paper's scientific argument as the presentation spine.

The default slide logic should help the audience answer, in order: 1. Why does this problem matter? 2. What gap or bottleneck does the paper address? 3. What did the authors do? 4. What is the key evidence? 5. Why should we trust the result? 6. What is new, reusable, or broadly meaningful? 7. Where are the boundaries and open questions?

This is more important than copying the paper section order.

Lean Operating Mode

Default to the lowest-overhead workflow that still produces a usable PPTX.

Do:

• read only the source material needed to understand the paper's argument,
• extract only figures/tables that will actually appear in the deck,
• create the PPTX as the primary deliverable,
• run lightweight structural checks on the PPTX package,
• write a short QA report.

Avoid by default:

• exhaustive extraction of every figure, page, image, table, or supplement,
• full OCR unless normal text extraction fails or the PDF is scanned,
• saving full raw extracted paper text unless it is needed for debugging or reuse,
• installing new dependencies when an existing tool can complete the task,
• launching GUI apps or desktop automation just to render previews,
• generating long markdown scripts when the user only needs a deck,
• rendering every slide when no reliable headless renderer is available.

Toolchain Policy

Use a cross-platform Python-first stack unless the user explicitly asks for something else:

• PyMuPDF for metadata, text extraction, page rendering, and page-level crops,
• Pillow for figure crops, contact sheets, and lightweight preview images,
• python-pptx for slide authoring and PPTX-safe editing,
• zipfile plus a reopen pass through python-pptx for package validation.

This stack must work on macOS, Linux, and Windows. Use `pathlib` paths, project-local output directories, and Office-safe fonts or theme fonts. Do not hardcode OS font paths or platform-specific file locations. If Python packages are missing, create a local virtual environment and install the minimum packages only when policy permits; do not install broad document suites just to finish a normal deck.

Treat LibreOffice/soffice as optional, only when it is already available and a real rendered preview is worth the cost. Avoid Keynote, PowerPoint desktop automation, AppleScript, Preview, Finder, `open`, and any OS-specific font or path dependency in helper scripts. If a preview can be made from extracted slide objects or assets, prefer that over re-rendering the whole deck.

Ask or document the tradeoff before doing expensive extras such as full supplementary-material processing, high-resolution recreation of many figures, full slide-by-slide rendered QA, or very long decks.

Accepted Inputs

The skill may receive:

• a full paper PDF
• supplementary figures or tables
• Word or markdown converted paper text
• abstract + results + figure legends
• structured reading notes
• manually pasted article content
• an `input/source.md` file
• a user-provided PPTX template

Default output language is simplified Chinese unless the user requests otherwise. Preserve important technical terms, abbreviations, gene/protein names, model names, dataset names, equations, and statistical terms in English when needed.

Default Fast Path

For a normal selectable-text paper PDF, run the shortest complete path: 1. Extract metadata, abstract, headings, figure legends, and table captions with PyMuPDF. 2. Identify the paper type, argument, and candidate figures before rendering high-resolution pages. 3. Render low-resolution contact sheets only when figure locations are unclear. 4. Render high-resolution images only for selected figure/table pages and crop only assets that will appear in the deck. 5. Build the PPTX directly with python-pptx, using native tables/charts when values are explicit and figure crops when the original visual carries the evidence. 6. Verify by reopening the PPTX and inspecting package structure; render slide previews only if a reliable cross-platform headless renderer is already available.

OCR, full supplementary extraction, all-page high-resolution rendering, all-slide rendered QA, and long script files are opt-in or justified exceptions, not defaults.

Workflow

Step 1. Read and extract source material

Extract, when available:

• title, authors, journal/preprint server, year, DOI
• field and subfield
• paper type
• central problem and knowledge gap
• main claim or thesis
• study design, workflow, model, dataset, or experimental system
• key methods and controls
• m