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nature-paper2ppt

Create 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.

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ToddModica/upstream-skills
Dernière activité de la source
14 septembre 2026 à 21:04
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SKILL.md
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nature-paper2ppt
description
Create 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.
# Paper-to-PPTX — Router ## Routing protocol For an edit to an existing deck, reuse its paper source, narrative, terminology, and assets. Change the requested slides and any affected cross-slide references; do not rerun paper intake or rebuild the deck's story unless the request requires it. Inspect changed slides and run the existing final PPTX audit before delivery. A requested outline or explanation alone does not require creating a deck. 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](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 (`../nature-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. When a deck is requested, the end product is a real `.pptx`, not only 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 PPTX checks, rendered-preview policy, and final verification → `references/self-review.md`. When a real PPTX has been generated, run `scripts/audit_pptx_quality.py` unless the file is unavailable. Treat high-severity findings as blockers, revise the deck, then re-run the audit and record the final result in `output/qa_report.md`.
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