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 an
复制下面这句话,粘贴给 Claude Code、Codex、Cursor 等 AI 编程工具,它会读取安装说明并在你确认后完成安装。
请阅读 https://ai.atlankj.com/install/asset/gh-nature-paper2ppt-f8f1902fbd89 ,按照其中的说明把「nature-paper2ppt」安装到你(当前 AI 工具)中。执行前先告诉我将运行的命令和写入的位置,等我确认。
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内容来自 GitHub 原始文件,由原作者维护。在 GitHub 查看
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.
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.
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.
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/.
Apply the loaded fragments in this priority order:
core/principles.md) — the argument is the spine; lean operating mode; accepted inputs; Chinese-by-default language rule.core/toolchain.md) — cross-platform Python-first stack, default fast path.paper_type fragment) — narrative order and slide structure for this paper.core/workflow.md) — run the 9 steps end to end.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.
The files under references/ are deep references, not defaults. Open them on demand per the references.on_demand table in the manifest:
references/design-and-layout.md.references/figure-assets.md.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.