Use when the user wants visually striking, shareable slide-deck IMAGES from any content — an article, blog post, topic, or paper — where look-and-feel matters more than editable precision (风格化幻灯/小红书配图/公众号配图/视觉化海报), optimized for reading and social sharing rather than live presentation. Offers 17 T2I

Paper Slide Deck Generator Transform any content — an article, a topic, or a paper — into stylized, shareable slide images; for PDF papers, figures are detected and extracted from the source. Generated slides are AI images that garble math and numbers, so every slide recorded as generated ( generated.json , written by generate slides.py or proofread.ts record ) must carry a proofread disposition before the merge scripts accept the deck (Step 6.5). Usage Setup (one time) The TypeScript scripts ( merge to , detect figures , extract figure , apply template ) need Node dependencies. Install them once: This installs canvas , pdfjs dist , pptxgenjs , and pdf lib (a package lock.json pins versions). If a script exits with missing Node dependency "<name " , run the command above. The Python generator ( generate slides.py ) auto installs google genai on first run. Also install PyMuPDF ( pip install pymupdf ) — it is the reliable fallback for extracting figures from pages that embed bitmaps (X rays, CAM heatmaps, photographs), where the pdfjs + canvas path in extract figure.ts fails with Error: Image or Canvas expected . For medical imaging papers this is the common case, not the exception, so treat PyMuPDF as required, not optional. Also install Pillow ( pip install Pillow ) — when Gemini returns image/webp , every slide goes through a Pillow conversion to PNG; without it each image errors with "converting it needs Pillow (pip install Pillow)" until installed. Treat it as required alongside PyMuPDF. Image generation & no API key path Image generation needs either a GOOGLE API KEY / GEMINI API KEY (Gemini API) or the Gemini Web skill. If no key and no web option is available, the skill still works in a degraded mode — do not abort: 1. Run with outline only to produce the outline + prompts (no images). 2. For a source PDF, extract real figures/tables with detect figures.ts + extract figure.ts + apply template.ts (no API key needed — pure rendering). 3. Merge whatever slides exist ( extract sourced pages) into PPTX/PDF, and hand the prompts/ back to the user to generate images later when a key is available. 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 : Script Purpose scripts/generate slides.py Generate AI slides via Gemini API (Python) scripts/merge to pptx.ts Merge slides into PowerPoint scripts/merge to pdf.ts Merge slides into PDF scripts/detect figures.ts Auto detect figures/tables in PDF (heuristic; verify pages) scripts/extract figure.ts Render a full PDF page to PNG (optional crop ; PyMuPDF fallback) scripts/apply template.ts Apply figure container template scripts/proofread.ts Record a proofread disposition ( mark ); the merge scripts' Step 6.5 gate Options Option Description style <name Visual style — gallery + auto selection rules in [references/style selection.md](references/style selection.md) audience <type Target audience: beginners, intermediate, experts, executives, general lang <code Output language (en, zh, ja, etc.) slides <number Target slide count outline only Generate outline only, skip image generation Style & layout selection : read [references/style selection.md](references/style selection.md) when picking a style (explicit or auto selected from content signals — including the academic signal caution about garbled math/numbers). Read [references/layout gallery.md](references/layout gallery.md) only when composing the outline's // LAYOUT hints ( Layout: <name per slide). 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: 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: This outputs a JSON file with all detected figures/tables, their page numbers, and captions. Caption detection is heuristic — verify, especially the first page teaser. The line anchored Figure N matcher reliably finds captions that sit on their own line (single column layouts), but misses figures whose caption is interleaved with body text on a two column first page — which is often the paper's most important architecture/overview figure. After running detect figures, cross check the source's Figure 1 explicitly: if the paper's text references a Figure N that is absent from figures.json , add it manually via an // IMAGE SOURCE block and extract it with the PyMuPDF fallback. Do not assume figures.json is complete. Step 2: Generate Outline Variants 1. If the user explicitly passed style <name , use that style directly and skip the variant question below (asking again after an explicit choice is noise). Otherwise, 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) 4. Save as outline {style}.md for each variant Step 3: User Confirmation Single AskUserQuestion with all applicable options: Question When to Ask Style variant Only when style was NOT passed (3 options + custom); an explicit style is the answer Language Only if source ≠ user language 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: Option Label Description 1 Gemini API (Recommended) Official Google API via Python. Requires GOOGLE API KEY env var. 2 Gemini Web (Browser based) ⚠️ Uses reverse engineered web API. No API key needed but may break. If no API key is available, do not still recommend Option 1 — offer the degraded no key path from the Setup section ( outline only + extraction + prompts handoff). Option 1: Gemini API (Python) 1. Verify API key : Check GOOGLE API KEY or GEMINI API KEY environment variable 2. Run generation script : The model id is stated once here: gemini 3 pro image (Nano Banana Pro, GA; the older preview id is deprecated). Override with model <id only if needed. Script behavior (details in the script docstring): Auto installs google genai ; errors out (non zero) if no prompt files are found Retries failed generations with backoff (3 attempts total, 10s/15s linear) Skips already generated slides ( 10KB, any image extension) Writes each slide to the deck root — the same place extracted figure slides land, so one merge step picks up both Saves with the real image extension, converting webp responses to PNG (the merge scripts only accept png/jpg/jpeg) Option 2: Gemini Web Skill The Web path does not write generated.json itself — record each slide it produces with proofread.ts record <slide deck dir <slide file model gemini web , or the merge gate cannot tell those slides are AI generated (it warns when prompts/ exists without a ledger). Read [references/gemini web.md](references/gemini web.md) for the consent check, per slide invocation, and proxy setup. It requires the baoyu danger gemini web skill and explicit user consent to the reverse engineered API disclaimer. 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: Note : extract figure.ts renders the entire page to a high resolution PNG — it does not auto detect or crop a single figure's bounding box. On a two column page you will get both columns. To isolate one figure, either pass crop "x,y,width,height" (pixels in the rendered/scaled page) or open the PNG, confirm it visually, and crop manually before applying the template. Run template application script: Report: "Extracted: Figure N → slide NN" 4. For slides with Source: generate (or no IMAGE SOURCE): Proceed to Step 6 for AI generation 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: 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. Failures retry automatically (API path: 3 attempts with backoff; see Step 5) Step 6.5: Proofread Generated Images (Content Integrity) Text to image bakes text into pixels and will garble spelling, math symbols, and numbers — this

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