Skill: abdullah4ai/visual-prompt-engine (version 1.0.0)

--- name: visual-prompt-engine description: "Generate diverse, non-repetitive image prompts powered by real visual references from Dribbble and design platforms. USE WHEN: user wants an image prompt, needs creative visual inspiration, asks for design-informed prompts, wants to avoid repetitive AI image generation, or says 'generate a prompt for an image', 'give me a creative image idea', 'make me a unique visual prompt'. DON'T USE WHEN: user wants to generate the image itself (use an image generation tool), wants to edit an existing image, or needs text-only content. EDGE CASES: 'make me an image' → use image generation tool, then optionally this skill for the prompt. 'improve this image prompt' → this skill. 'I keep getting similar AI images' → this skill (solves repetition)." --- # 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: ```bash python3 scripts/scrape_dribbble.py --method import --import-file manual_shots.json --output data/references.json ``` **Alternative: RSS/HTML** (may be blocked by WAF) ```bash 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: ```bash 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: 1. Read `data/style_cards.json` for available visual references 2. Select 1-3 cards relevant to the user's goal 3. Read `references/prompt-patterns.md` for diverse prompt structures 4. Read `references/visual-vocabulary.md` for precise design terminology 5. Compose a prompt combining: user goal + style card elements + varied pattern 6. Check against recent prompts in `data/prompt_history.json` to prevent repetition 7. 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: ```bash # 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: ```bash pip install requests beautifulsoup4 ```


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)