--- name: nature-paper2ppt description: Build a complete Nature-style Chinese PPTX presentation from a scientific paper, preprint, PDF, article text, figure legends, or reading notes. Use for journal club, group meeting, thesis seminar, paper sharing, conference or defense decks, and Chinese requests such as 论文做PPT、论文汇报、组会PPT、文献汇报、学术汇报、做幻灯片、读书报告PPT. It classifies paper type, builds an evidence-led story, selects key figures, writes Chinese slide content and speaker notes, creates the actual .pptx, and runs corrective QA for complete figure crops, stable alignment, text overflow, and de-templated Chinese academic expression. Also trigger when improving weak paper-to-PPT output with cropped figures, loose alignment, obvious AI-style wording, or heavy manual rework. ---

# 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](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/`.

--- name: nature-paper2ppt description: Build a complete Nature-style Chinese PPTX presentation from a scientific paper, preprint, PDF, article text, figure legends, or reading notes. Use for journal club, group meeting, thesis seminar, paper sharing, conference or defense decks, and Chinese requests such as 论文做PPT、论文汇报、组会PPT、文献汇报、学术汇报、做幻灯片、读书报告PPT. It classifies paper type, builds an evidence-led story, selects key figures, writes Chinese slide content and speaker notes, creates the actual .pptx, and runs corrective QA for complete figure crops, stable alignment, text overflow, and de-templated Chinese academic expression. Also trigger when improving weak paper-to-PPT output with cropped figures, loose alignment, obvious AI-style wording, or heavy manual rework. ---

# 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](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/`.

--- name: nature-paper2ppt description: Build a complete Nature-style Chinese PPTX presentation from a scientific paper, preprint, PDF, article text, figure legends, or reading notes. Use for journal club, group meeting, thesis seminar, paper sharing, conference or defense decks, and Chinese requests such as 论文做PPT、论文汇报、组会PPT、文献汇报、学术汇报、做幻灯片、读书报告PPT. It classifies paper type, builds an evidence-led story, selects key figures, writes Chinese slide content and speaker notes, creates the actual .pptx, and runs corrective QA for complete figure crops, stable alignment, text overflow, and de-templated Chinese academic expression. Also trigger when improving weak paper-to-PPT output with cropped figures, loose alignment, obvious AI-style wording, or heavy manual rework. ---

# 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](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/`.

--- name: nature-paper2ppt description: Build a complete Nature-style Chinese PPTX presentation from a scientific paper, preprint, PDF, article text, figure legends, or reading notes. Use for journal club, group meeting, thesis seminar, paper sharing, conference or defense decks, and Chinese requests such as 论文做PPT、论文汇报、组会PPT、文献汇报、学术汇报、做幻灯片、读书报告PPT. It classifies paper type, builds an evidence-led story, selects key figures, writes Chinese slide content and speaker notes, creates the actual .pptx, and runs corrective QA for complete figure crops, stable alignment, text overflow, and de-templated Chinese academic expression. Also trigger when improving weak paper-to-PPT output with cropped figures, loose alignment, obvious AI-style wording, or heavy manual rework. ---

# 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](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/`.