Visual Prompt Engine

Generate diverse, non-repetitive image prompts powered by real visual references from Dribbble and design platforms. USE WHEN: user wants an image prompt, ne...

Security Scan

The skill's files, scripts, and runtime instructions are coherent with its stated goal of generating design-informed image prompts from Dribbble references and do not request unexplained credentials or unusual system privileges.

Purpose & Capability

Name and description match the included artifacts: a scraper, style-card builder, prompt patterns, and references. There are no unrelated required binaries or environment variables. All code and docs are proportional to a prompt-generation workflow grounded in visual references.

Instruction Scope

Runtime instructions operate on local files under data/ and instruct optionally scraping Dribbble (RSS or HTML) and using a browser tool. The skill also instructs an AI agent to 'view' image_url entries and perform visual analysis to populate style cards — this implies the agent may fetch remote images or use browser automation. Also, SKILL.md references data/prompt_history.json for deduplication but that file is not present in the shipped manifest and the scripts do not create it, which could cause runtime errors or require the agent to create/write that file.

Install Mechanism

There is no install spec; it's instruction-only with included scripts. Optional Python dependencies (requests, beautifulsoup4) are explicit and standard. Network operations target Dribbble (RSS/HTML) only; no downloads from arbitrary/untrusted URLs or extract operations are present.

Credentials

The skill requests no environment variables, secrets, or external credentials. A noteworthy implicit risk: visual-analysis steps may cause the agent to send images to third-party vision APIs if the agent is configured that way, but that is not requested by the skill itself.

Persistence & Privilege

always:false (default) and no unusual persistence or system-wide config changes are requested. The skill does not modify other skills or require elevated privileges.

Assessment

This skill is internally consistent and low-risk in terms of requested privileges, but review these practical points before installing: (1) The scraper fetches data from Dribbble — ensure you are comfortable with scraping and any site terms of service; (2) The agent is instructed to 'view' image_url entries, which will make the agent fetch remote images and could cause those images to be sent to external vision or LLM services if your agent is configured to do so (be careful with copyrighted content and privacy); (3) SKILL.md mentions data/prompt_history.json but that file isn't included — you may need to create it or handle deduplication yourself to avoid errors; (4) Optional dependencies (requests, beautifulsoup4) are standard but only needed for HTML scraping; (5) If you enable browser automation tools for scraping, avoid granting them unnecessary credentials. If you want higher assurance, run the scripts manually in a sandbox, review/curate the data/ files, and verify where any image analysis traffic is sent.

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:

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:

# 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