paper-slide-deck

# Paper Slide Deck Generator Transform academic papers and content into professional slide deck images with automatic figure extraction. ## Usage ```bash /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 ``` ## Setup (one-time) The TypeScript scripts (`merge-to-*`, `detect-figures`, `extract-figure`, `apply-template`) need Node dependencies. Install them once: ```bash cd ${SKILL_DIR}/scripts && npm install ``` This installs `canvas`, `pdfjs-dist`, `pptxgenjs`, and `pdf-lib` (a `package-lock.json` pins versions). If a script exits with `missing Node dependency "<name>"`, run the command above. The Python generator (`generate-slides.py`) auto-installs `google-genai` on first run. **Also install PyMuPDF** (`pip install pymupdf`) — it is the reliable fallback for extracting figures from pages that embed bitmaps (X-rays, CAM heatmaps, photographs), where the `pdfjs` + `canvas` path in `extract-figure.ts` fails with `Error: Image or Canvas expected`. For medical-imaging papers this is the common case, not the exception, so treat PyMuPDF as required, not optional. ### Image generation & no-API-key path Image generation needs either a `GOOGLE_API_KEY`/`GEMINI_API_KEY` (Gemini API) or the Gemini Web skill. **If no key and no web option is available, the skill still works in a degraded mode** — do not abort: 1. Run with `--outline-only` to produce the outline + prompts (no images). 2. For a source PDF, extract real figures/tables with `detect-figures.ts` + `extract-figure.ts` + `apply-template.ts` (no API key needed — pure rendering). 3. Merge whatever slides exist (`extract`-sourced pages) into PPTX/PDF, and hand the `prompts/` back to the user to generate images later when a key is available. ## Script Directory **Important**: All scripts are located in the `scripts/` subdirectory of this skill. **Agent Execution Instructions**: 1. Determine this SKILL.md file's directory path as `SKILL_DIR` 2. Script path = `${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 | ## 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: ``` slide-deck/{topic-slug}/ ├── source-{slug}.{ext} # Source files (text, images; source-paper.pdf for papers) ├── figures.json # Figure-detection results (academic PDFs) ├── outline.md # Final outline (with IMAGE_SOURCE blocks) ├── outline-{style}.md # Style variant outlines ├── extracted/ # Raw PDF page extractions (pre-template) ├── figures/ # Extracted figure crops (pre-template) ├── prompts/ │ └── {NN}-slide-{slug}.md, ... ├── {NN}-slide-{slug}.png # Final slide images in the deck ROOT — both AI-generated │ # and extracted-figure slides; ext may be .jpg ├── {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: ```bash 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. **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. 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 | Always (3 options + custom) | | 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**: ```bash python3 ${SKILL_DIR}/scripts/generate-slides.py <slide-deck-dir> ``` 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 exponential backoff (3 attempts total) - 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 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: ```bash npx -y bun ${SKILL_DIR}/scripts/extract-figure.ts \ --pdf source-paper.pdf \ --page <page-number> \ --output figures/figure-<N>.png ``` **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: ```bash 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 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: ```python 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. 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 is the single biggest risk of this skill. Do not ship unchecked. (This is this skill's instance of the repository's shared citation-integrity core: numbers and claims must match the source, and failures surface as visible flags — never silent substitutes.) **For every generated slide** (especially any with equations, tables, key numbers, or non-Latin text), use `Read` to open the PNG and visually check: 1. **Spelling / wording** — headline and body text match the outline, no invented or mangled words. 2. **Math & symbols** — equations, subscripts, Greek letters, operators are correct (or absent). Assume the model got them wrong until you confirm otherwise. 3. **Numbers & units** — any figure that carries data matches the source exactly. **If garbling is found:** - Regenerate that slide with a corrected/simplified prompt (spell risky terms phonetically, reduce text density, move exact numbers to a caption). **Max 2 retries.** - If it still fails after 2 retries, **flag the slide `[CHECK]`** in the Step 8 summary and recommend one of: