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nature-paper2ppt

Turn scientific papers or research notes into Chinese academic PowerPoint presentations with source figures and speaker notes. Organize slides around the paper type, keep technical terms consistent, and revise existing decks with quality checks.

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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.

See how to install

Good for

  • Preparing a paper presentation for a lab meeting
  • Creating paper-based conference or defense slides
  • Revising selected slides in an existing academic deck
  • Keeping technical terms consistent across slides and notes

Files included (19)

  • SKILL.md4.2 KB
  • agents/openai.yaml254 B
  • manifest.yaml2.8 KB
  • README_EN.md2.1 KB
  • README.md1.8 KB
  • references/design-and-layout.md14.4 KB
  • references/figure-assets.md4.6 KB
  • references/self-review.md6.8 KB
  • scripts/audit_pptx_quality.py12.8 KB
  • static/core/output-and-quality.md5.2 KB
  • static/core/principles.md3.0 KB
  • static/core/toolchain.md2.5 KB
  • static/core/workflow.md6.2 KB
  • static/fragments/paper_type/clinical.md1.2 KB
  • static/fragments/paper_type/discovery.md1.3 KB
  • static/fragments/paper_type/materials.md1.2 KB
  • static/fragments/paper_type/methods.md1.2 KB
  • static/fragments/paper_type/resource.md1.1 KB
  • static/fragments/paper_type/review.md1.0 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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. 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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