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/nature-paper2ppt

Build a complete Nature-style Chinese PPTX presentation from a scientific paper, preprint, PDF, article text, figure legends, or reading notes. Use for journal club, group meeting, thesis seminar, paper sharing, conference or defense decks, and Chinese requests such as

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nature-skills
41k20 skills
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$ npx -y skills add Yuan1z0825/nature-skills --skill nature-paper2ppt --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/nature-paper2ppt

Context preview

The summary Claude sees to decide when to auto-load this skill.

Build a complete Nature-style Chinese PPTX presentation from a scientific paper, preprint, PDF, article text, figure legends, or reading notes. Use for journal club, group meeting, thesis seminar, paper sharing, conference or defense decks, and Chinese requests such as

SKILL.md

nature-paper2ppt.SKILL.md
name: 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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