nature-paper2ppt
- Repo stars 16,057
- Author updated Live
- Author repo nature-skills
- Domain
- Design
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 92 / 100 · audit passed
- Author / version / license
- @Yuan1z0825 · v2.0.0 · no license declared
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- Python
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: nature-paper2ppt
description: Build a complete but efficient Nature-style Chinese PPTX presentation from a scientific paper, p…
category: design
runtime: Python
---
# nature-paper2ppt output preview
## PART A: Task fit
- Use case: 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..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Routing protocol / 1. Load the manifest and the core layer / 2. Classify the paper type” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “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.”.
- **02** When the source has headings, the agent prioritizes “Routing protocol / 1. Load the manifest and the core layer / 2. Classify the paper type” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Start with a small task and check whether the result follows “Routing protocol / 1. Load the manifest and the core layer / 2. Classify the paper type”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: nature-paper2ppt
description: Build a complete but efficient Nature-style Chinese PPTX presentation from a scientific paper, p…
category: design
source: Yuan1z0825/nature-skills
---
# nature-paper2ppt
## When to use
- Build a complete but efficient Nature-style Chinese PPTX presentation from a scientific paper, preprint, PDF, article…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Routing protocol / 1. Load the manifest and the core layer / 2. Classify the paper type” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies.The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "nature-paper2ppt" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Routing protocol / 1. Load the manifest and the core layer / 2. Classify the paper type
rules -> SKILL.md triggers / order / output contract
runtime -> Python | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}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. 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:
- Core principles (
core/principles.md) — the argument is the spine; lean operating mode; accepted inputs; Chinese-by-default language rule. - Toolchain policy and fast path (
core/toolchain.md) — cross-platform Python-first stack, default fast path. - Paper-type arc (the loaded
paper_typefragment) — narrative order and slide structure for this paper. - Workflow (
core/workflow.md) — run the 9 steps end to end. - 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, andnature-readerso shared content lives in_shared/.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review