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Visual Prompt Engine

核心功能：依托Dribbble等平台的真实设计参考，通过结构化流程生成高质量、多样化的图像提示词，打破AI提示词的重复套路。典型场景：用户需要图像提示词、创意视觉灵感、优化现有提示词、解决AI生成图像重复问题。输入为用户需求及收集的设计参考（支持手动/半自动化收集），输出经质量审核、结构多元的最终提示词，避免视觉陈词滥调。

abdullah4ai
abdullah4ai
热度 87.70
最近更新 2026-05-11 17:26:07
分类：创作依赖：代码执行 / 联网浏览搜索 / 读写文件 / 第三方 API / 内部调用大模型应用场景：文案创作 / 图像生成 / UI 原型

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add visual-prompt-engine
Metadata
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:

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