| name | remote-sensing-nature-paper2ppt |
| description | Build a complete but efficient Nature-style Chinese PPTX presentation from a remote-sensing, Earth-observation, geospatial AI, or broader 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 remote-sensing research paper. 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. Also trigger on general academic-presentation requests even without the word "Nature", such as turning a paper into slides, building an academic talk deck, conference/defense presentations, and Chinese phrasings like 论文做PPT、遥感论文汇报、组会PPT、文献汇报、学术汇报、做幻灯片、讲paper、读书报告PPT. |
| version | 2.0.0 |
| author | Community contribution, refactored into static/dynamic layers |
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 Wuhan University white-background brand defaults, 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 — remote-sensing resource / dataset / atlas / product / benchmark papers (workflow-to-validation arc).application — remote-sensing application / regional assessment / monitoring studies (area-to-evidence 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.
- Wuhan University brand defaults (
core/whu-brand-defaults.md) — Microsoft YaHei for Chinese, Times New Roman for English/numerals, white background, and WHU logo placement.
- Toolchain policy and fast path (
core/toolchain.md) — cross-platform Python-first stack, default fast path.
- Paper-type arc (the loaded
paper_type fragment) — 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 sensor/platform names, mission/product names, datasets, metrics, regions, model names, 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
remote-sensing-nature-writing, remote-sensing-nature-polishing, and remote-sensing-nature-reader so shared content lives in _shared/.
