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Paper Slide Deck Generator

Transform academic papers and content into professional slide deck images with automatic figure extraction.

Usage

/paper-slide-deck path/to/paper.pdf
/paper-slide-deck path/to/paper.pdf --style academic-paper
/paper-slide-deck path/to/content.md --style sketch-notes
/paper-slide-deck path/to/content.md --audience executives
/paper-slide-deck path/to/content.md --lang zh
/paper-slide-deck path/to/content.md --slides 10
/paper-slide-deck path/to/content.md --outline-only
/paper-slide-deck  # Then paste content

Script Directory

Important : All scripts are located in the scripts/ subdirectory of this skill.

Agent Execution Instructions :

1. Determine this SKILL.md file's directory path as SKILL_DIR
2. Script path = ${SKILL_DIR}/scripts/<script-name>.ts
3. Replace all ${SKILL_DIR} in this document with the actual path

Script Reference :

Options

Style Gallery

Auto Style Selection

Layout Gallery

Optional layout hints for individual slides. Specify in outline's // LAYOUT section.

Slide-Specific Layouts

Infographic-Derived Layouts

Academic-Specific Layouts

Usage : Add Layout: <name> in slide's // LAYOUT section to guide visual composition.

Design Philosophy

This deck is designed for reading and sharing , not live presentation:

• Each slide must be self-explanatory without verbal commentary
• Structure content for logical flow when scrolling
• Include all necessary context within each slide
• Optimize for social media sharing and offline reading

File Management

Output Directory

Each session creates an independent directory named by content slug:

slide-deck/{topic-slug}/
├── source-{slug}.{ext}    # Source files (text, images, etc.)
├── outline.md
├── outline-{style}.md     # Style variant outlines
├── prompts/
│   └── 01-slide-cover.md, 02-slide-{slug}.md, ...
├── 01-slide-cover.png, 02-slide-{slug}.png, ...
├── {topic-slug}.pptx
└── {topic-slug}.pdf

Slug Generation :

1. Extract main topic from content (2-4 words, kebab-case)
2. Example: "Introduction to Machine Learning" → intro-machine-learning

Conflict Resolution

If slide-deck/{topic-slug}/ already exists:

• Append timestamp: {topic-slug}-YYYYMMDD-HHMMSS
• Example: intro-ml exists → intro-ml-20260118-143052

Source Files

Copy all sources with naming source-{slug}.{ext}:

• source-article.md (main text content)
• source-diagram.png (image from conversation)
• source-data.xlsx (additional file)

Multiple sources supported: text, images, files from conversation.

Workflow

Step 1: Analyze Content

1. Save source content (if pasted, save as source.md)

2. Follow references/analysis-framework.md for deep content analysis

3. Determine style (use --style or auto-select from signals)

4. Detect languages (source vs. user preference)

5. Plan slide count (--slides or dynamic)

6. For academic papers (PDF with figures) : Run automatic figure detection:

   npx -y bun ${SKILL_DIR}/scripts/detect-figures.ts --pdf source-paper.pdf --output figures.json

This outputs a JSON file with all detected figures/tables, their page numbers, and captions.

Step 2: Generate Outline Variants

1. Generate 3 style variant outlines based on content analysis
2. Follow references/outline-template.md for structure
3. Auto-populate IMAGE_SOURCE for academic papers:
   • Read figures.json from Step 1
   • Map figures to slides using rules in references/analysis-framework.md Section 8
   • Automatically add // IMAGE_SOURCE blocks to appropriate slides:
     • Architecture/pipeline figures → Methods slides (Source: extract)
     • Results tables → Quantitative results slides (Source: extract)
     • Comparison images → Qualitative results slides (Source: extract)
     • Conceptual/simple diagrams → Leave for AI generation (Source: generate or omit)

Step 3: User Confirmation

Single AskUserQuestion with all applicable options:

After selection:

• Copy selected outline-{style}.md to outline.md
• Regenerate in different language if requested
• User may edit outline.md for fine-tuning

If --outline-only, stop here.

Step 4: Generate Prompts

1. Read references/base-prompt.md
2. Combine with style instructions from outline
3. Add slide-specific content
4. If Layout: specified in outline, include layout guidance in prompt:
   • Reference layout characteristics for image composition
   • Example: Layout: hub-spoke → "Central concept in middle with related items radiating outward"
5. Save to prompts/ directory

Step 5: Image Generation Method Selection

Before generating images , ask user to choose generation method:

Use AskUserQuestion with options:

Based on selection:

Option 1: Gemini API (Python)

1. Verify API key : Check GOOGLE_API_KEY or GEMINI_API_KEY environment variable

2. Run generation script :

   python ${SKILL_DIR}/scripts/generate-slides.py <slide-deck-dir> --model gemini-3-pro-image-preview

Script Features :

• Auto-installs google-genai package if missing
• Retry logic with exponential backoff (3 retries)
• Skips already-generated slides (> 10KB)
• Supports custom model via --model flag
• Outputs to slides/ subdirectory

Troubleshooting :

• If server disconnection errors occur, script auto-retries
• For persistent failures, re-run the script (it skips completed slides)
• Check API quota if many failures occur

Option 2: Gemini Web Skill

1. Consent Check : Read consent file at:

   • Windows: $APPDATA/baoyu-skills/gemini-web/consent.json
   • macOS: ~/Library/Application Support/baoyu-skills/gemini-web/consent.json
   • Linux: ~/.local/share/baoyu-skills/gemini-web/consent.json

2. If no consent or version mismatch , display disclaimer and ask:

   ⚠️ DISCLAIMER: This uses a reverse-engineered Gemini Web API (NOT official). Risks: May break anytime, no support, possible account risk.

3. For each slide , run:

   npx -y bun ${GEMINI_WEB_SKILL_DIR}/scripts/main.ts
   --promptfiles prompts/01-slide-cover.md
   --image 01-slide-cover.png
   --sessionId slides-{topic-slug}-{timestamp}

Where GEMINI_WEB_SKILL_DIR = path to baoyu-danger-gemini-web skill directory.

1. Proxy support : If user is in restricted network, prepend:

   HTTP_PROXY=http://127.0.0.1:7890 HTTPS_PROXY=http://127.0.0.1:7890

Step 5.5: Process IMAGE_SOURCE (Automatic Figure Extraction)

For academic presentations, IMAGE_SOURCE metadata was auto-populated in Step 2 based on figure detection from Step 1.

Automatic Execution :

1. Parse outline to identify slides with Source: extract

2. Create figures directory : mkdir -p figures

3. For each extract slide , automatically:

   • Read the Figure number, Page, and Caption from metadata

   • Run figure extraction script:

          npx -y bun ${SKILL_DIR}/scripts/extract-figure.ts \
     

     --pdf source-paper.pdf
     --page <page-number>
     --output figures/figure-<N>.png

   • Run template application script:

          npx -y bun ${SKILL_DIR}/scripts/apply-template.ts \
     

     --figure figures/figure-<N>.png
     --title "<slide-headline>"
     --caption "Figure <N>: <caption-text>"
     --output <NN>-slide-<slug>.png

   • Report: "Extracted: Figure N → slide NN"

4. For slides withSource: generate (or no IMAGE_SOURCE):

Note : Source PDF must be saved as source-paper.pdf in output directory.

Troubleshooting :

• If figure detection missed a figure: manually add // IMAGE_SOURCE block to outline
• If wrong figure mapped: edit the Figure: and Page: values in outline
• If extraction fails: check PDF page number (1-indexed)

PyMuPDF Fallback for Page Extraction : If extract-figure.ts fails with "Image or Canvas expected" error (common with complex PDFs), use PyMuPDF:

import fitz
doc = fitz.open("source-paper.pdf")
page = doc[page_num - 1]  # 0-indexed
mat = fitz.Matrix(3, 3)  # 3x scale for high resolution
pix = page.get_pixmap(matrix=mat)
pix.save(f"extracted/page-{page_num}.png")

Then apply template using apply-template.ts.

Step 6: Generate Images

1. Use selected method from Step 5
2. Skip slides already processed in Step 5.5 (those with Source: extract)
3. Generate session ID: slides-{topic-slug}-{timestamp}
4. Generate each remaining slide with same session ID
5. Report progress: "Generated X/N"
6. Auto-retry once on generation failure

Step 7: Merge to PPTX and PDF

npx -y bun ${SKILL_DIR}/scripts/merge-to-pptx.ts <slide-deck-dir>
npx -y bun ${SKILL_DIR}/scripts/merge-to-pdf.ts <slide-deck-dir>

Step 8: Output Summary

Slide Deck Complete!

Topic: [topic]
Style: [style name]
Location: [directory path]
Slides: N total

- 01-slide-cover.png ✓ Cover
- 02-slide-intro.png ✓ Content
- ...
- {NN}-slide-back-cover.png ✓ Back Cover

Outline: outline.md
PPTX: {topic-slug}.pptx
PDF: {topic-slug}.pdf

Slide Modification

See references/modification-guide.md for:

• Edit single slide workflow
• Add new slide (with renumbering)
• Delete slide (with renumbering)
• File naming conventions

Image Generation Dependencies

Gemini API (Option 1 - Recommended)

Requires:

• GOOGLE_API_KEY or GEMINI_API_KEY environment variable
• Python 3.8+ with pip
• google-genai package (auto-installed by script)

Model: gemini-3-pro-image-preview (default)

Gemini Web Skill (Option 2)

Requires:

• baoyu-danger-gemini-web skill installed at .claude/skills/baoyu-danger-gemini-web
• Google Chrome browser with logged-in Google account
• User consent for reverse-engineered API disclaimer

PDF Figure Extraction

Requires:

• Primary : pdfjs-dist npm package (use legacy build for Node.js)
• Fallback : pymupdf Python package (more reliable for complex PDFs)
• canvas npm package for apply-template.ts

References

Notes

Image Generation

• Nano Banana Pro API : Recommended. Stable, reliable, requires API key
• Gemini Web : No API key needed, but uses reverse-engineered API with account risk
• Generation time: 10-30 seconds per slide
• Auto-retry once on generation failure
• Maintain style consistency via session ID

Content Guidelines

• Use stylized alternatives for sensitive public figures
• Both methods use the same underlying Gemini model for image generation

Extension Support

Custom styles and configurations via EXTEND.md.

Check paths (priority order):

1. .paper-skills/paper-slide-deck/EXTEND.md (project)
2. ~/.paper-skills/paper-slide-deck/EXTEND.md (user)

If found, load before Step 1. Extension content overrides defaults.

Weekly Installs

82

Repository

luwill/research-skills

GitHub Stars

308

First Seen

Jan 25, 2026

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Installed on

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