Visual Prompt Engine
Generate high-quality, diverse image prompts by feeding real visual references into a structured prompt pipeline.
Problem
AI agents reuse the same visual patterns and clichés when writing image prompts. This skill breaks that cycle by grounding prompts in real, trending design work.
Architecture
Dribbble Scraper → Style Cards → Prompt Generator → Quality Reviewer → Final Prompt
Quick Start
1. Collect Visual References
Recommended: Browser-based collection (Dribbble blocks automated requests)
Browse https://dribbble.com/shots/popular with a browser tool (Camofox, Playwright, etc.), collect shot URLs, titles, and image URLs, then save as JSON:
python3 scripts/scrape_dribbble.py --method import --import-file manual_shots.json --output data/references.json
Alternative: RSS/HTML (may be blocked by WAF)
python3 scripts/scrape_dribbble.py --output data/references.json --count 20
The import JSON format: [{"title": "...", "url": "https://dribbble.com/shots/...", "image_url": "..."}]
2. Build Style Cards
Convert raw references into style cards:
python3 scripts/style_card.py build --input data/references.json --output data/style_cards.json
3. Generate Prompts
When the user requests an image prompt:
- Read
data/style_cards.jsonfor available visual references - Select 1-3 cards relevant to the user's goal
- Read
references/prompt-patterns.mdfor diverse prompt structures - Read
references/visual-vocabulary.mdfor precise design terminology - Compose a prompt combining: user goal + style card elements + varied pattern
- Check against recent prompts in
data/prompt_history.jsonto prevent repetition - Append the new prompt to history
4. Review and Deliver
Before delivering, verify the prompt:
- Uses specific visual language (not generic adjectives)
- References concrete design elements from the style card
- Follows a pattern different from the last 5 prompts
- Includes composition, lighting, color palette, and mood
Style Card Schema
See references/style-card-schema.md for the full schema. A style card contains:
| Field | Description |
|---|---|
palette | Hex colors extracted from the design |
composition | Layout structure (grid, asymmetric, centered, etc.) |
typography | Font style and weight characteristics |
mood | Emotional tone (bold, minimal, playful, etc.) |
textures | Surface qualities (glass, grain, matte, etc.) |
lighting | Light direction and quality |
source_url | Original Dribbble shot URL |
tags | Design categories |
Prompt Patterns
See references/prompt-patterns.md for 12+ distinct prompt structures that prevent repetition. Rotate through patterns to keep outputs fresh.
Visual Vocabulary
See references/visual-vocabulary.md for precise design terminology covering color, composition, lighting, texture, and typography. Use these terms instead of generic words like "beautiful" or "nice".
Automation (Optional)
Set up a daily cron to refresh visual references:
# Run daily to keep references current
python3 scripts/scrape_dribbble.py --output data/references.json --count 20
python3 scripts/style_card.py build --input data/references.json --output data/style_cards.json
Data Directory
The skill stores working data in data/:
data/
├── references.json # Raw Dribbble scrape results
├── style_cards.json # Processed style cards
└── prompt_history.json # Generated prompts (for deduplication)
Create the data/ directory on first run if it does not exist.
Dependencies
Python 3.9+ with standard library only. Optional: requests, beautifulsoup4 for live scraping (falls back to Dribbble RSS if not installed).
Install optional dependencies:
pip install requests beautifulsoup4
