Can I use this for specific cinematic visual styles?

Yes, it includes a director style recognition table that maps specific creators like Wong Kar-wai or Zhang Yimou to precise lighting and atmosphere parameters.

How does the Intelligent Prompt Generator handle incomplete requests?

The skill identifies missing required elements—such as lighting or makeup—and intelligently supplements them based on the era and cultural context provided in the user's intent.

What is the global optimal selection strategy?

Instead of a simple keyword match, it uses a multi-dimensional scoring mechanism that evaluates keyword coverage, element quality, and semantic consistency to choose the best possible prompt components.

Does it prevent logical conflicts in prompts?

Yes, it performs consistency checks to ensure elements match, such as preventing modern neon lighting in ancient historical scenes or correcting eye color inconsistencies based on ethnicity.

Intelligent Prompt Generator

Name: Intelligent Prompt Generator
Author: tianzecn

bytianzecn

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Design Tools

Generates logically consistent, high-fidelity image prompts using semantic reasoning and a structured element database.

About

The Intelligent Prompt Generator is a sophisticated Claude Code skill that transforms brief user requests into detailed, production-ready image generation prompts. It utilizes a robust framework spanning seven core categories—subject, facial, styling, expression, lighting, scene, and technical—to ensure every prompt is semantically coherent and culturally accurate. By leveraging common-sense reasoning and a global optimal selection algorithm, the skill automatically resolves logical conflicts, such as era-inappropriate lighting or ethnic inconsistencies, making it an essential tool for developers and designers working with AI visual models.

Key Features

Cinematic Style Mapping: Built-in support for specific director aesthetics including Zhang Yimou, Wong Kar-wai, and Tsui Hark.
Semantic Intent Construction: Automatically infers and fills missing requirements like lighting, era, and makeup based on context.
7-Category Framework: Structured prompt construction covering subject, facial, styling, expression, lighting, scene, and technical specs.
Common Sense Reasoning: Enforces biological and cultural consistency, such as matching eye colors and hairstyles to specific ethnicities.
Global Optimal Selection: Uses a weighted scoring algorithm (0-100) to select the most relevant elements from a comprehensive database.
1 GitHub stars

Use Cases

Ensuring stylistic consistency across large-scale AI image generation workflows.
Developing high-fidelity character concepts with accurate historical costuming and lighting.
Automating the generation of complex technical prompts for Stable Diffusion or Midjourney.

About

Key Features

Cinematic Style Mapping: Built-in support for specific director aesthetics including Zhang Yimou, Wong Kar-wai, and Tsui Hark.
Semantic Intent Construction: Automatically infers and fills missing requirements like lighting, era, and makeup based on context.
7-Category Framework: Structured prompt construction covering subject, facial, styling, expression, lighting, scene, and technical specs.
Common Sense Reasoning: Enforces biological and cultural consistency, such as matching eye colors and hairstyles to specific ethnicities.
Global Optimal Selection: Uses a weighted scoring algorithm (0-100) to select the most relevant elements from a comprehensive database.
1 GitHub stars

Use Cases

Ensuring stylistic consistency across large-scale AI image generation workflows.
Developing high-fidelity character concepts with accurate historical costuming and lighting.
Automating the generation of complex technical prompts for Stable Diffusion or Midjourney.