--- name: Paper Illustration slug: paper-illustration category: Writing description: Paper Illustration generates publication-quality figures for academic papers, including architecture diagrams and method illustrations. Use it when you need a paper visual or when prompted to generate diagrams or AI drawings. github: "https://github.com/OpenLAIR/dr-claw/tree/main/skills/aris-paper-illustration" language: JavaScript stars: 1047 forks: 116 install: "npx degit https://github.com/OpenLAIR/dr-claw/tree/main/skills/aris-paper-illustration ~/.claude/skills/aris-paper-illustration" installs_to: ~/.claude/skills/aris-paper-illustration source_path: skills/aris-paper-illustration/SKILL.md collection_size: 25 category_size: 1012 collection_url: "https://dirskills.com/collections/OpenLAIR/dr-claw" added: 2026-08-21T05:14:03.680Z last_synced: 2026-08-21T05:14:03.680Z canonical_url: "https://dirskills.com/skills/paper-illustration" --- # Paper Illustration Paper Illustration generates publication-quality figures for academic papers, including architecture diagrams and method illustrations. Use it when you need a paper visual or when prompted to generate diagrams or AI drawings. **Install:** ```bash npx degit https://github.com/OpenLAIR/dr-claw/tree/main/skills/aris-paper-illustration ~/.claude/skills/aris-paper-illustration ``` ## README # Paper Illustration: Multi-Stage Claude-Supervised Figure Generation Generate publication-quality illustrations using a **multi-stage workflow** with **Claude as the STRICT supervisor/reviewer**. ## Core Design Philosophy ``` ┌──────────────────────────────────────────────────────────────────────────┐ │ MULTI-STAGE ITERATIVE WORKFLOW │ ├──────────────────────────────────────────────────────────────────────────┤ │ │ │ User Request │ │ │ │ │ ▼ │ │ ┌─────────────┐ │ │ │ Claude │ ◄─── Step 1: Parse request, create initial prompt │ │ │ (Planner) │ │ │ └──────┬──────┘ │ │ │ │ │ ▼ │ │ ┌─────────────┐ │ │ │ Gemini │ ◄─── Step 2: Optimize layout description │ │ │ (gemini-3-pro)│ - Refine component positioning │ │ │ Layout │ - Optimize spacing and grouping │ │ └──────┬──────┘ │ │ │ │ │ ▼ │ │ ┌─────────────┐ │ │ │ Gemini │ ◄─── Step 3: CVPR/NeurIPS style verification │ │ │ (gemini-3-pro)│ - Check color palette compliance │ │ │ Style │ - Verify arrow and font standards │ │ └──────┬──────┘ │ │ │ │ │ ▼ │ │ ┌─────────────┐ │ │ │ Paperbanana │ ◄─── Step 4: Render final image │ │ │ (gemini-3- │ - High-quality image generation │ │ │ pro-image) │ - Internal codename: Nano Banana Pro │ │ └──────┬──────┘ │ │ │ │ │ ▼ │ │ ┌─────────────┐ │ │ │ Claude │ ◄─── Step 5: STRICT visual review + SCORE (1-10) │ │ │ (Reviewer) │ - Verify EVERY arrow direction │ │ │ STRICT! │ - Verify EVERY block content │ │ └──────┬──────┘ - Verify aesthetics & visual appeal │ │ │ │ │ ▼ │ │ Score ≥ 9? ──YES──► Accept & Output │ │ │ │ │ NO │ │ │ │ │ ▼ │ │ Generate SPECIFIC improvement feedback ──► Loop back to Step 2 │ │ │ └──────────────────────────────────────────────────────────────────────────┘ ``` ## Constants - **IMAGE_MODEL = `gemini-3-pro-image-preview`** — Paperbanana (Nano Banana Pro) for image rendering - **REASONING_MODEL = `gemini-3-pro-preview`** — Gemini for layout optimization and style checking - **MAX_ITERATIONS = 5** — Maximum refinement rounds - **TARGET_SCORE = 9** — Minimum acceptable score (1-10) — RAISED FOR QUALITY - **OUTPUT_DIR = `figures/ai_generated/`** — Output directory - **API_KEY_ENV = `GEMINI_API_KEY`** — Environment variable ## CVPR/ICLR/NeurIPS Top-Tier Conference Style Guide **What "CVPR Style" Actually Means:** ### Visual Standards - **Clean white background** — No decorative patterns or gradients (unless subtle) - **Sans-serif fonts** — Arial, Helvetica, or Computer Modern; minimum 14pt - **Subtle color palette** — Not rainbow colors; use 3-5 coordinated colors - **Print-friendly** — Must be readable in grayscale (many reviewers print papers) - **Professional borders** — Thin (2-3px), solid colors, not flashy ### Layout Standards - **Horizontal flow** — Left-to-right is the standard for pipelines - **Clear grouping** — Use subtle background boxes to group related modules - **Consistent sizing** — Similar components should have similar sizes - **Balanced whitespace** — Not cramped, not sparse ### Arrow Standards (MOST CRITICAL) - **Thick strokes** — 4-6px minimum (thin arrows disappear when printed) - **Clear arrowheads** — Large, filled triangular heads - **Dark colors** — Black or dark gray (#333333); avoid colored arrows - **Labeled** — Every arrow should indicate what data flows through it - **No crossings** — Reorganize layout to avoid arrow crossings - **CORRECT DIRECTION** — Arrows must point to the RIGHT target! ### Visual Appeal (科研风格 - Professional Academic Style) **目标：既不保守也不花哨，找到平衡点** #### ✅ 应该有的视觉元素： - **Subtle gradient fills** — 淡雅的渐变填充（同色系从浅到深），不是炫彩 - **Rounded corners** — 圆角矩形（6-10px radius），现代感但不夸张 - **Clear visual hierarchy** — 通过大小、颜色深浅区分层次 - **Consistent color coding** — 统一的配色方案（3-4种主色） - **Internal structure** — 大模块内部显示子组件（如Encoder内部的layer结构） - **Professional typography** — 清晰的标签，适当的字号层次 #### ✅ 配色建议（学术专业）： - **Inputs**: 柔和的绿色系 (#10B981 / #34D399) - **Encoders**: 专业的蓝色系 (#2563EB / #3B82F6) - **Fusion**: 优雅的紫色系 (#7C3AED / #8B5CF6) - **Outputs**: 温暖的橙色系 (#EA580C / #F97316) - **Arrows**: 黑色或深灰 (#333333 / #1F2937) - **Background**: 纯白 (#FFFFFF)，不要花纹 #### ❌ 要避免的过度装饰： - ❌ Rainbow color schemes (彩虹配色) - ❌ Heavy drop shadows (重阴影效果) - ❌ 3D effects / perspective (3D透视) - ❌ Excessive gradients (夸张的多色渐变) - ❌ Clip art / cartoon icons (卡通图标) - ❌ Decorative patterns in background (背景花纹) - ❌ Glowing effects (发光效果) - ❌ Too many small icons (过多小图标) #### ✓ 理想的视觉效果： - 一眼看上去**专业、清晰** - 有**适度的视觉吸引力**，但不抢眼 - 符合**CVPR/NeurIPS论文**的审美标准 - **打印友好**（灰度模式下也能清晰辨认） - 像**精心设计**的学术图表，而不是PPT模板 ### What to AVOID (CRITICAL) - ❌ Rainbow color schemes (too many colors) - ❌ Thin, hairline arrows (arrows must be THICK) - ❌ Unlabeled connections - ❌ Plain boring rectangles (add some visual interest) - ❌ **Over-decorated with shadows/glows/icons** (too flashy) - ❌ Small text that's unreadable when printed - ❌ **WRONG arrow directions** — This is UNACCEPTABLE! ## Scope | Figure Type | Quality | Examples | |-------------|---------|----------| | **Architecture diagrams** | Excellent | Model architecture, pipeline, encoder-decoder | | **Method illustrations** | Excellent | Conceptual diagrams, algorithm flowcharts | | **Conceptual figures** | Good | Comparison diagrams, taxonomy trees | **Not for:** Statistical plots (use `/aris-paper-figure`), photo-realistic images ## Workflow: MUST EXECUTE ALL STEPS ### Step 0: Pre-flight Check ```bash # Check API key if [ -z "$GEMINI_API_KEY" ]; then echo "ERROR: GEMINI_API_KEY not set" echo "Get your key from: https://aistudio.google.com/app/apikey" echo "Set it: export GEMINI_API_KEY='your-key'" exit 1 fi # Create output directory mkdir -p figures/ai_generated ``` ### Step 1: Claude Plans the Figure (YOU ARE HERE) **CRITICAL: Claude must first analyze the user's request and create a detailed prompt.** Parse the input: **$ARGUMENTS** Claude's task: 1. Understand what figure the user wants 2. Identify all components, connections, data flow 3. Create a **detailed, structured prompt** for Gemini 4. Include style requirements AND visual appeal requirements **Prompt Template for Claude to generate:** ``` Create a PROFESSIONAL, VISUALLY APPEALING publication-quality academic diagram following CVPR/ICLR/NeurIPS standards. ## Visual Style: 科研风格 (Academic Professional Style) ### 目标：平衡 — 既不保守也不花哨 #### DO (应该有): - **Subtle gradients** — 同色系淡雅渐变（如 #2563EB → #3B82F6），不是多色炫彩 - **Rounded corners** — 圆角矩形（6-10px），现代感 - **Clear visual hierarchy** — 通过大小、深浅区分层次 - **Internal structure** — 大模块内显示子组件结构 - **Consistent color coding** — 统一的3-4色方案 - **Professional polish** — 精致但不夸张 #### DON'T (不要有): - ❌ Rainbow/multi-color gradients (彩虹渐变) - ❌ Heavy drop shadows (重阴影) - ❌ 3D effects / perspective (3D效果) - ❌ Glowing effects (发光效果) - ❌ Excessive decorative icons (过多装饰图标) - ❌ Plain boring rectangles (完全平淡的方块) #### 理想效果： 像顶会论文中精心设计的架构图 — 专业、清晰、有适度的视觉吸引力 ## Figure Type [Architecture Diagram / Pipeline / Comparison / etc.] ## Components to Include (BE SPECIFIC ABOUT CONTENT) 1. [Component 1]: - Label: "[exact text]" - Sub-label: "[smaller text below]" - Position: [left/center/right, top/middle/bottom] - Style: [border color, fill, internal structure] 2. [Component 2]: ... ## Layout - Direction: [left-to-right / top-to-bottom] - Spacing: [tight / normal / loose] - Grouping: [how components should be grouped] ## Connections (BE EXPLICIT ABOUT DIRECTION) EXACT arrow specifications: 1. [Component A] → [Component B]: Arrow goes FROM A TO B, label it "[data type]" 2. [Component C] → [Component D]: Arrow goes FROM C TO D, label it "[data type]" ... VERIFY: Each arrow must point to the CORRECT target! ## Style Requirements (CVPR/ICLR/NeurIPS Standard) ### Visual Style - Color palette: Professional academic colors - Inputs: Green (#10B981) - Encoders: Blue (#2563EB) - Fusion modules: Purple (#7C3AED) - Outputs: Orange (#EA580C) - Font: Sans-serif (Arial/Helvetica), minimum 14pt, bold for labels - Background: Clean white, no patterns - Blocks: Rounded rectangles (8-12px radius), subtle gradient fill, colored border (2-3px) - Subtle shadows for depth effect - Print-friendly (must work in grayscale) ### CRITICAL: Arrow & Data Flow Requirements 1. **ALL arrows must be VERY THICK** - minimum 5-6px stroke width 2. **ALL arrows must have CLEAR arrowheads** - large, visible triangular heads 3. **ALL arrows must be BLACK or DARK GRAY** - not colored 4. **Label EVERY arrow** with what data flows through it 5. **VERIFY arrow direction** - each arrow MUST point to the correct target 6. **No ambiguous connections** - every arrow should have a clear source and destination ### Logic Clarity Requirements 1. **Data flow must be immediately obvious** - viewer should understand the pipeline in 5 seconds 2. **No crossing arrows** - reorganize layout to avoid arrow crossings 3. **Consistent direction** - maintain left-to-right or top-to-bottom flow throughout 4. **Group related components** - use subtle background boxes or spacing to group modules 5. **Clear hierarchy** - main components larger, sub-components smaller ## Additional Requirements [Any specific requirements from user] ``` ### Step 2: Gemini Layout Optimization (gemini-3-pro) **Claude sends the initial prompt to Gemini (gemini-3-pro) for layout optimization.** ```bash #!/bin/bash # Step 2: Optimize layout using Gemini gemini-3-pro # This step refines component positioning and spacing set -e OUTPUT_DIR="figures/ai_generated" mkdir -p "$OUTPUT_DIR" API_KEY="${GEMINI_API_KEY}" URL="https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-preview:generateContent?key=$API_KEY" # The initial prompt from Claude INITIAL_PROMPT='[Claude fills in the detailed prompt here]' # Layout optimization request LAYOUT_REQUEST="You are an expert in academic figure layout design for CVPR/NeurIPS papers. Analyze this figure request and provide an OPTIMIZED LAYOUT DESCRIPTION: $INITIAL_PROMPT Provide: 1. **Optimized Component Positions**: Exact positions (left/center/right, top/middle/bottom) for each component 2. **Spacing Recommendations**: Specific spacing between components 3. **Grouping Strategy**: Which components should be visually grouped together 4. **Arrow Routing**: Optimal paths for arrows to avoid crossings 5. **Visual Hierarchy**: Size recommendations for main vs sub-components Output a DETAILED layout specification that will be used for rendering." # Build JSON payload python3 << PYTHON import json payload = { "contents": [{"parts": [{"text": '''$LAYOUT_REQUEST'''}]}] } with open("/tmp/gemini_layout_request.json", "w") as f: json.dump(payload, f, indent=2) print("Layout request created") PYTHON # Call Gemini gemini-3-pro-preview for layout optimization (DIRECT connection, no proxy) RESPONSE=$(curl -s --max-time 90 \ -X POST "$URL" \ -H 'Content-Type: application/json' \ -d @/tmp/gemini_layout_request.json) # Extract layout description LAYOUT_DESCRIPTION=$(echo "$RESPONSE" | python3 -c " import sys, json data = json.load(sys.stdin) try: print(data['candidates'][0]['content']['parts'][0]['text']) except: print('Error extracting layout') ") echo "=== Layout Optimization Complete ===" echo "$LAYOUT_DESCRIPTION" echo "$LAYOUT_DESCRIPTION" > "$OUTPUT_DIR/layout_description.txt" ``` ### Step 3: Gemini Style Verification (gemini-3-pro) **Claude sends the optimized layout to Gemini for CVPR/NeurIPS style verification.** ```bash #!/bin/bash # Step 3: Verify and enhance style compliance using Gemini gemini-3-pro API_KEY="${GEMINI_API_KEY}" URL="https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-preview:generateContent?key=$API_KEY" # Read layout from previous step LAYOUT=$(cat figures/ai_generated/layout_description.txt) # Style verification request STYLE_REQUEST="You are a CVPR/NeurIPS paper figure reviewer specializing in visual standards. Review and ENHANCE this figure specification for top-tier conference compliance: $LAYOUT Ensure compliance with: 1. **Color Palette**: Use professional academic colors (green for inputs, blue for encoders, purple for fusion, orange for outputs) 2. **Arrow Standards**: Thick (5-6px), black/dark gray, clear arrowheads, all labeled 3. **Font Standards**: Sans-serif, minimum 14pt, readable in print 4. **Visual Appeal (科研风格)**: - ✅ Subtle same-color gradients, rounded corners (6-10px), internal structure visible - ❌ NO heavy shadows, NO glowing effects, NO rainbow gradients Output an ENHANCED figure specification with explicit style instructions for rendering." # Build JSON payload python3 << PYTHON import json payload = { "contents": [{"parts": [{"text": '''$STYLE_REQUEST'''}]}] } with open("/tmp/gemini_style_request.json", "w") as f: json.dump(payload, f, indent=2) print("Style request created") PYTHON # Call Gemini gemini-3-pro-preview for style verification (DIRECT connection, no proxy) RESPONSE=$(curl -s --max-time 90 \ -X POST "$URL" \ -H 'Content-Type: application/json' \ -d @/tmp/gemini_style_request.json) # Extract style-enhanced specification STYLE_SPEC=$(echo "$RESPONSE" | python3 -c " import sys, json data = json.load(sys.stdin) try: print(data['candidates'][0]['content']['parts'][0]['text']) except: print('Error extracting style spec') ") echo "=== Style Verification Complete ===" echo "$STYLE_SPEC" echo "$STYLE_SPEC" > "figures/ai_generated/style_spec.txt" ``` ### Step 4: Paperbanana Image Rendering (gemini-3-pro-image-preview) **Claude sends the optimized, style-verified specification to Paperbanana for rendering.** ```bash #!/bin/bash # Step 4: Render image using Paperbanana (gemini-3-pro-image-preview) # Internal codename: Nano Banana Pro # Use DIRECT connection (no proxy) - proxy causes SSL errors set -e OUTPUT_DIR="figures/ai_generated" mkdir -p "$OUTPUT_DIR" API_KEY="${GEMINI_API_KEY}" URL="https://generativelanguage.googleapis.com/v1beta/models/gemini-3-pro-image-preview:generateContent?key=$API_KEY" # Read the style-enhanced specification from previous step STYLE_SPEC=$(cat figures/ai_generated/style_spec.txt) # Add rendering instructions RENDER_PROMPT="Render a publication-quality academic diagram based on this specification: $STYLE_SPEC RENDERING REQUIREMENTS: - Output a clean, professional diagram suitable for CVPR/NeurIPS submission - Use vector-quality rendering with sharp edges and clear text - Ensure all elements are properly aligned and spaced - The diagram should be immediately understandable at a glance" # Build JSON payload using Python for proper escaping python3 << PYTHON import json payload = { "contents": [{"parts": [{"text": '''$RENDER_PROMPT'''}]}], "generationConfig": {"responseModalities": ["TEXT", "IMAGE"]} } with open("/tmp/gemini_request.json", "w") as f: json.dump(payload, f, indent=2) print("JSON payload created") PYTHON # Call Paperbanana API WITHOUT proxy (direct connection works better) RESPONSE=$(curl -s --max-time 180 \ -X POST "$URL" \ -H 'Content-Type: application/json' \ -d @/tmp/gemini_request.json) # Check for error if echo "$RESPONSE" | grep -q '"error"'; then echo "API Error:" echo "$RESPONSE" | python3 -m json.tool 2>/dev/null || echo "$RESPONSE" exit 1 fi # Extract and save image echo "$RESPONSE" | python3 << 'PYTHON' import sys, json, base64 from pathlib import Path output_dir = Path("figures/ai_generated") data = json.load(sys.stdin) try: parts = data['candidates'][0]['content']['parts'] iteration = 1 # Claude increments this each iteration for part in parts: if 'text' in part: print(f"\n[Paperbanana]: {part['text'][:200]}...") elif 'inlineData' in part: img_data = base64.b64decode(part['inlineData']['data']) img_path = output_dir / f"figure_v{iteration}.png" with open(img_path, "wb") as f: f.write(img_data) print(f"\n✅ Image saved: {img_path}") print(f" Size: {len(img_data)/1024:.1f} KB") except Exception as e: print(f"Parse error: {e}") print(f"Raw response: {str(data)[:500]}") PYTHON ``` ### Step 5: Claude STRICT Visual Review & Scoring (MANDATORY) **Claude MUST read the generated image and perform a STRICT review:** 1. **Visual Analysis**: What does the image show in detail? 2. **Strengths**: What's good about it? 3. **STRICT Verification**: Check EVERY item below 4. **Score**: Rate 1-10 (10 = perfect) — BE STRICT! **STRICT Review Template:** ```markdown ## Claude's STRICT Review of Figure v{N} ### What I See [Describe the generated image in DETAIL - every block, every arrow] ### Strengths - [Strength 1] - [Strength 2] ### ═══════════════════════════════════════════════════════════════ ### STRICT VERIFICATION CHECKLIST (ALL must pass for score ≥ 9) ### ═══════════════════════════════════════════════════════════════ #### A. Arrow Correctness Verification (CRITICAL - any failure = score ≤ 6) Check EACH arrow: - [ ] Arrow 1: [Source] → [Target] — Does it point to the CORRECT target? - [ ] Arrow 2: [Source] → [Target] — Does it point to the CORRECT target? - [ ] Arrow 3: [Source] → [Target] — Does it point to the CORRECT target? - [ ] Arrow 4: [Source] → [Target] — Does it point to the CORRECT target? - [ ] Arrow 5: [Source] → [Target] — Does it point to the CORRECT target? - [ ] Arrow 6: [Source] → [Target] — Does it point to the CORRECT target? #### B. Block Content Verification (any failure = score ≤ 7) Check EACH block: - [ ] Block 1 "[Name]": Has correct label? Has sub-label? Content correct? - [ ] Block 2 "[Name]": Has correct label? Has sub-label? Content correct? - [ ] Block 3 "[Name]": Has correct label? Has sub-label? Content correct? - [ ] Block 4 "[Name]": Has correct label? Has s