yuan1z0825/nature-skills

nature-paper2ppt

Create or improve a Chinese academic PPTX from a scientific paper or research reading notes, with source figures and speaker notes.

소스 보기

사용 방법

  1. 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
  2. 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
  3. 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/yuan1z0825/nature-skills --skill nature-paper2ppt

시작 전 확인

  • 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
  • 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

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.

관련 작업

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