`nature-paper2ppt` 技能SAFE

From the repository's own README, as read at the audited commit. Badges and raw HTML are left out.

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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`.

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

none-observed

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

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