nature-paper2ppt
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 runs an explicit self-review/corrective revision loop focused on figure quality, text overflow prevention, and non-template visual design before delivery.
What this skill does
# Paper-to-PPTX — Router This skill is split into two layers: - A **static layer** under `static/` that holds versioned, reusable content fragments (core principles, toolchain policy, the 9-step workflow, output/quality rules, and per-paper-type presentation arcs). - A **dynamic layer** (this file plus `manifest.yaml`) that detects the paper type and loads only the fragments needed for the current job. Deep design, figure, and self-review material lives in on-demand references. Do not try to apply the deck-building logic from memory or from this router. Always load fragments from disk as described below. ## Routing protocol Follow these five steps every time the skill is invoked. ### 1. Load the manifest and the core layer Read [manifest.yaml](manifest.yaml). It declares the `paper_type` axis, the allowed values, and the file paths each value maps to. Also read every file listed under `always_load`. These hold the purpose and core principle, the lean operating mode and toolchain policy, the 9-step workflow spine, and the output/quality rules that apply to every deck, plus the shared Terminology Ledger used to keep technical terms consistent across slides. ### 2. Classify the paper type Decide the `paper_type` value using the manifest's `detect:` hint and the source: - `discovery` — discovery / mechanism papers (question-to-evidence arc). Default. - `methods` — methods / AI / tool / algorithm papers (problem-to-solution arc). - `resource` — resource / dataset / atlas / omics / benchmark papers (workflow-to-validation arc). - `clinical` — clinical / population / intervention studies (design-to-inference arc). - `materials` — materials / chemistry / physics / engineering papers (property-to-mechanism / design-to-performance arc). - `review` — reviews / perspectives / commentaries / meta-analyses (evidence-map arc). State the detected value in one short line to the user before designing slides, so they can correct you cheaply. ### 3. Load the matching fragment Read the file mapped for the detected `paper_type`. It gives the presentation arc and how to adapt the default slide structure for this type. Do **not** read every fragment in `static/`. ### 4. Build the deck using the loaded material Apply the loaded fragments in this priority order: 1. Core principles (`core/principles.md`) — the argument is the spine; lean operating mode; accepted inputs; Chinese-by-default language rule. 2. Toolchain policy and fast path (`core/toolchain.md`) — cross-platform Python-first stack, default fast path. 3. Paper-type arc (the loaded `paper_type` fragment) — narrative order and slide structure for this paper. 4. Workflow (`core/workflow.md`) — run the 9 steps end to end. 5. Output and quality rules (`core/output-and-quality.md`) — deliverables, quality gates, fallbacks. Build the Terminology Ledger (`../../_shared/core/terminology-ledger.md`) while reading the source, so model names, gene/protein names, datasets, metrics, and abbreviations stay identical across every slide and speaker note. The end product is a real `.pptx` deck, not an outline or script. Do not fabricate results, numbers, or figure details. ### 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: - composing/auditing slide layout, visual rhythm, typography, anti-template design, archetypes, on-slide text budget → `references/design-and-layout.md`. - selecting, extracting, cropping, and quality-checking figure/table assets → `references/figure-assets.md`. - running the self-review/corrective revision loop, severity grading, programmatic python-pptx checks, rendered-preview policy, and final verification → `references/self-review.md`. ## Why this split - The static layer is versioned and reviewable. Adding a new paper-type arc is one new fragment plus one manifest line. - The dynamic layer keeps each invocation cheap: only the arc for this paper enters context up front; heavy design and QA material loads only when that step runs. - The router itself is short on purpose. Update fragments, not this file, when adding scope. - This structure mirrors `nature-writing`, `nature-polishing`, and `nature-reader` so shared content lives in `_shared/`.
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