A data-driven engine that creates high-quality, non-repetitive AI image prompts based on real-world design trends and structured visual vocabularies.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install visual-prompt-engine
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install visual-prompt-engine using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Visual Prompt Engine?
The Visual Prompt Engine is a specialized tool designed to solve the common problem of repetitive AI-generated imagery. By grounding prompt generation in real visual references from platforms like Dribbble, this Openclaw Skills entry ensures that your AI coding agent produces fresh, design-forward prompts rather than relying on clichéd AI patterns.
This skill uses a sophisticated pipeline that transforms raw design data into structured Style Cards. These cards capture essential design elements like color palettes, lighting, and composition, allowing the agent to synthesize prompts that are both technically precise and creatively inspired.
Visual Prompt Engine Use Cases
- When an AI agent needs to generate professional, high-quality image prompts for UI/UX or marketing.
- To overcome the repetitive "AI look" by using real-world design references.
- When seeking design-informed inspiration grounded in current industry trends.
- To transform vague user requests into structured, technical prompts for Midjourney, DALL-E, or Stable Diffusion.
How Visual Prompt Engine Works
- Reference Collection: The skill scrapes or imports visual data from Dribbble to gather trending design references.
- Style Card Construction: Raw data is processed into Style Cards that categorize palettes, textures, and moods.
- Pattern Selection: The engine selects from over 12 distinct prompt patterns to ensure structural variety.
- Vocabulary Synthesis: Precise design terminology is applied to describe composition and lighting.
- Deduplication: The engine checks the prompt history to ensure the new output is distinct from the last five generations.
- Final Review: The prompt is verified for specific visual language before being delivered to the user.
Visual Prompt Engine Setup
To get started with this Openclaw Skills tool, install the optional dependencies for live scraping:
pip install requests beautifulsoup4
Initialize your local data environment and build your first set of design style cards:
# Collect references
python3 scripts/scrape_dribbble.py --output data/references.json --count 20
# Build style cards
python3 scripts/style_card.py build --input data/references.json --output data/style_cards.json
Visual Prompt Engine Data Schema & Taxonomy
The Visual Prompt Engine organizes data in a structured data/ directory to maintain consistency across sessions:
| File | Description |
|---|---|
references.json |
Raw metadata including source URLs and image links. |
style_cards.json |
Processed attributes such as palette, typography, and lighting. |
prompt_history.json |
A record of generated prompts used for deduplication logic. |
prompt-patterns.md |
A reference library of 12+ distinct prompt structures. |
visual-vocabulary.md |
A taxonomy of precise design terms for high-fidelity prompting. |
Visual Prompt Engine Advanced Features
- Automated Trend Refresh: Set up a daily cron job to automatically update visual references and style cards.
- Deduplication Logic: Integrated history tracking prevents the AI from falling into repetitive output loops.
- Multi-Source Ingest: Support for manual JSON imports allows for curated design references from any platform.
- Design-Centric Schema: Extracts specific technical details like hex codes and layout composition for hyper-realistic prompting.
SKILL.md
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