nature-paper2ppt

Yuan1z0825/nature-skills · Agent Skill

Genera automaticamente presentazioni in stile Nature da articoli scientifici, PDF o note. Struttura i contenuti, seleziona le figure e scrive testo delle slide e note del relatore in cinese.

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AutoreYuan1z0825Community
RepositoryYuan1z0825/nature-skills ↗
Cartella dello skillnature-paper2ppt
Stelle39.583
File inclusi2
LicenzaApache-2.0
Aggiornato2 agosto 2026
LinkSorgente dello skill ↗ · Repository ↗

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Installazione

Uno skill è una cartella con un file SKILL.md. Scegli il tuo agente ed esegui i comandi in un terminale (macOS, Linux o Git Bash su Windows) dalla radice del progetto.

Incolla il prompt in Claude, Codex o un altro assistente: controllerà lo skill e lo installerà.

Install the “nature-paper2ppt” skill from mozgi.io (https://mozgi.io/skills/nature-paper2ppt/) for me.

1. Download the archive: https://mozgi.io/skills/nature-paper2ppt/download
   If it fails, take the skill folder from the source: https://github.com/Yuan1z0825/nature-skills/tree/main/skills/nature-paper2ppt
2. Read SKILL.md and the other files. Check the skill is safe: no hidden commands, no sending data elsewhere, no scripts doing something other than described. If anything looks suspicious, stop and tell me.
3. Put the “nature-paper2ppt” folder into your agent's skills folder: .claude/skills/ for Claude Code, .agents/skills/ for Codex, .cursor/skills/ for Cursor, .gemini/skills/ for Gemini CLI. Inside the project if we are in one, otherwise in the home folder (~/.claude/skills/ etc.).
4. Make sure <skills folder>/nature-paper2ppt/SKILL.md exists.
5. Briefly tell me what this skill does and offer to try it on my task.

If you cannot do a step yourself, tell me what to click or type.

File dello skill

2 file Scarica ZIP

Leggi SKILL.md e i file correlati prima di installare. Clicca un file per l'anteprima.

Contenuto di SKILL.md

Testo originale dell'autore · sola lettura

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:

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.

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 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.

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, and nature-reader so shared content lives in nature-shared/.

Domande frequenti

Che cosa fa lo skill nature-paper2ppt?

Genera automaticamente presentazioni in stile Nature da articoli scientifici, PDF o note. Struttura i contenuti, seleziona le figure e scrive testo delle slide e note del relatore in cinese.

Come si installa nature-paper2ppt?

Apri il blocco “Installazione” in questa pagina e scegli il tuo agente — Claude Code, Codex, Cursor o Gemini CLI — per i comandi pronti che copiano la cartella dello skill nel posto giusto. Oppure copia il prompt dalla scheda “Con l'assistente” e lascia che sia l'assistente a controllare e installare lo skill.

Con quali modelli AI funziona nature-paper2ppt?

Gli skill sono semplici istruzioni in Markdown (formato Agent Skills), quindi nature-paper2ppt funziona con qualsiasi agente che legge SKILL.md: Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode e altri. Il modello carica le istruzioni solo quando il compito corrisponde alla descrizione dello skill.

nature-paper2ppt è gratuito?

Lo skill è pubblicato apertamente da Yuan1z0825 con licenza Apache-2.0. Lo skill in sé non costa nulla: paghi solo il tuo fornitore AI per i token usati.

È sicuro installare gli skill?

Uno skill è un insieme di istruzioni che il tuo agente seguirà: leggi SKILL.md e i file correlati prima di installarlo. Gli script nella cartella dello skill vengono eseguiti solo se li esegue l'agente: controlla cosa fa e non concedere permessi superflui.

In cosa differisce uno skill da un server MCP o da un plugin?

Un server MCP o un plugin danno al modello nuovi strumenti: codice che viene eseguito e restituisce un risultato. Uno skill fornisce conoscenza e metodo: come affrontare il compito, quali passi seguire e com'è un buon risultato. Non servono server né chiavi API.

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