visual-architect
npx machina-cli add skill proyecto26/sherlock-ai-plugin/visual-architect --openclawPaper Visualizer Skill
Top-tier Scientific Visual Architect. Transforms text into geometric, structural visual instructions.
1. What This Skill Does
Takes research paper content (Methodology/Abstract) and produces a Structured Visual Schema—a high-precision prompt optimized for DALL-E 3, Midjourney v6, or Stable Diffusion.
2. Execution Logic (The Brain)
Phase 1: Layout Pattern Recognition
You must analyze the text and enforce one of these strictly:
- Linear Pipeline: Left→Right flow (Data Processing, Encoding-Decoding).
- Cyclic/Iterative: Center loop (Optimization, RL, Feedback Loops).
- Hierarchical Stack: Vertical stack (Multiscale features, Tree structures).
- Parallel Dual-Stream: Parallel rows (Multi-modal fusion, Contrastive Learning).
- Central Hub: Core connecting peripherals (Agent-Environment).
- Matrix Grid: Comparison studies or ablation components.
Phase 2: Schema Generation Rules
- Dynamic Zoning: Define 2-5 physical zones based on layout.
- Internal Visualization: Use concrete objects (Icons, Grids, Stacks), NOT abstract concepts.
- Explicit Connections: Describe physics of flow (e.g., "Curved arrow looping back").
3. Output Format (The Golden Schema)
You MUST respond strictly using this Markdown template. Use the examples in brackets [...] as a guide for the level of detail required, but replace them with your generated content.
---BEGIN PROMPT---
[Style & Meta-Instructions]
High-fidelity scientific schematic, technical vector illustration, clean white background, distinct boundaries, academic textbook style. High resolution 4k, strictly 2D flat design with subtle isometric elements.
**[TEXT RENDERING RULES]**
* **Typography**: Use bold, sans-serif font (e.g., Helvetica/Roboto style) for maximum legibility.
* **Hierarchy**: Prioritize correct spelling for MAIN HEADERS (Zone Titles). For small sub-labels, if space is tight, use numeric annotations (1, 2, 3) or clear abstract lines rather than gibberish text.
* **Contrast**: Text must be dark grey/black on light backgrounds. Avoid overlapping text on complex textures.
[LAYOUT CONFIGURATION]
* **Selected Layout**: [e.g., Cyclic Iterative Process with 3 Nodes]
* **Composition Logic**: [e.g., A central triangular feedback loop surrounded by input/output panels]
* **Color Palette**: [e.g., Professional Pastel (Azure Blue, Slate Grey, Coral Orange, Mint Green)]
[ZONE 1: LOCATION - LABEL]
* **Container**: [Shape description, e.g., Top-Left Rectangular Panel]
* **Visual Structure**: [Concrete objects, e.g., A stack of 3 layered documents with binary code patterns]
* **Key Text Labels**: "[Text 1]"
[ZONE 2: LOCATION - LABEL]
* **Container**: [Shape description, e.g., Central Circular Engine]
* **Visual Structure**: [Concrete objects, e.g., A clockwise loop connecting 3 internal modules: A (Gear), B (Graph), C (Filter)]
* **Key Text Labels**: "[Text 2]", "[Text 3]"
[ZONE 3: LOCATION - LABEL]
... (Add Zone 4 or 5 if necessary based on the selected layout)
[CONNECTIONS]
1. [Connection description, e.g., A curved dotted arrow looping from Zone 2 back to Zone 1 labeled "Feedback"]
2. [Connection description, e.g., A wide flow arrow branching from Zone 2 to Zone 3]
---END PROMPT---
4. Usage Guide for User
Input: Upload your paper PDF and say:
"Generate a visual schema for this paper's methodology section"
Pro Tips for Best Results:
- Text Correction: AI image generators often misspell complex scientific terms. Use the generated image as a base layer, then overlay correct text in PowerPoint/Canva/Illustrator.
- Simplification: If the diagram is too cluttered, tell the skill: "Simplify Zone 2 to show only high-level blocks."
Advanced Constraints:
- Add
--svgto request a Mermaid/SVG code block representation (Experimental). - Add
--style "poster"for simplified, bold layouts.
5. Technical Limitations
- SVG output is an approximation; prioritize the Text Schema for Image Generation models.
- Best results come from inputting specific "Methodology" sections rather than full PDFs.
Source
git clone https://github.com/proyecto26/sherlock-ai-plugin/blob/main/skills/visual-architect/SKILL.mdView on GitHub Overview
Transforms research paper content (Methodology/Abstract) into a Structured Visual Schema, delivering a high-precision prompt optimized for DALL-E 3, Midjourney v6, or Stable Diffusion. It analyzes the paper’s logic, selects a core layout pattern, and defines 2–5 concrete zones with objects to guide AI image generation.
How This Skill Works
The skill operates in two phases: Phase 1 Pattern Recognition enforces one of six layout patterns (Linear Pipeline, Cyclic/Iterative, Hierarchical Stack, Parallel Dual-Stream, Central Hub, Matrix Grid) based on the paper text. Phase 2 Schema Generation maps content into 2–5 zones with concrete objects and explicit connections (e.g., curved arrows), producing the final Golden Schema in a strict Markdown template for high-fidelity illustration.
When to Use It
- Creating a visual abstract for a paper's methodology to share in meetings or slides.
- Comparing architectures across multiple papers by aligning layout patterns for side-by-side visuals.
- Designing AI-generated visuals of a research workflow for posters or proposals.
- Drafting a visual schema to accompany a systematic literature review.
- Explaining a complex algorithm with a region-based diagram for a grant application.
Quick Start
- Step 1: Upload the paper PDF (focus on the Methodology/Abstract) and specify the target section.
- Step 2: Let the tool auto-detect a layout pattern or manually choose one.
- Step 3: Review and export the Golden Schema Markdown to feed AI image generators.
Best Practices
- Extract concrete steps, components, and data flows from the Methodology/Abstract to feed the schema.
- Choose a layout pattern first, then map content into 2–5 zones.
- Use 2–5 distinct zones with tangible objects (icons, grids, stacks) rather than abstract concepts.
- Include explicit connections (e.g., curved arrows) to clearly show flow and dependencies.
- Review the Golden Schema and tailor prompts for the target AI image generator before rendering.
Example Use Cases
- Visualize a machine learning paper's data preprocessing and training pipeline.
- Illustrate a cyclic optimization loop in an RL or optimization-focused paper.
- Depict a hierarchical feature extraction workflow in a computer vision study.
- Show a multi-modal fusion study using parallel streams for inputs and outputs.
- Map a biology paper's gene regulation network using a central hub layout.