image-generator

Generate images through multi-turn reasoning with six quality gates.

Updated Nov 28, 2025

Search Tags:#automation #reasoning #pedagogy #image-generation #quality-gates #visual-design #batch-processing

Ready to run

npx skills add https://github.com/syeda-hoorain-ali/physical-ai --skill image-generator-syeda-hoorain-ali

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: image-generator
Source: https://github.com/syeda-hoorain-ali/physical-ai/tree/main/.claude/skills/image-generator
Command: npx skills add https://github.com/syeda-hoorain-ali/physical-ai --skill image-generator-syeda-hoorain-ali

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Visual generation frequently stops at the first pass, missing deeper reasoning and standards. This skill provides a structured, multi-turn reasoning partnership with professional quality gates to ensure deliberate, high-quality visuals.

Core Features & Use Cases

Reasoning-mode image generation guides visuals beyond first output
Multi-turn partnership with principled feedback to teach quality standards
Autonomous batch mode and 6+ quality gates to ensure production-ready results
Immediate embedding workflow to avoid orphaned assets and ensure integration

Quick Start

Provide a condensed creative brief to Gemini per visual, verify all gates, embed immediately, and proceed to the next visual without interruption.

Frequently Asked Questions about image-generator

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What is multi-turn reasoning for image generation and why does it matter?▼

Multi-turn reasoning for image generation applies principled feedback across iterations to guide visuals beyond the first output, ensuring results meet professional quality standards rather than stopping at the initial draft.

How do I automate batch image generation for instructional content?▼

Automate batch image generation by providing condensed creative briefs for each visual, letting the autonomous workflow verify quality gates, embed assets immediately into lesson materials, and proceed without interruption.

Can I use this visual design workflow to embed generated assets directly into lesson materials?▼

Yes, you can use this visual design workflow to embed generated assets directly into lesson materials through the immediate embedding workflow, which avoids orphaned assets and ensures direct integration.

What are the quality gates applied during automated visual design processing?▼

Automated visual design processing applies six quality gates to evaluate generated assets, ensuring production-ready results that meet professional standards for instructional content and design assets.

How do I write token-conserving briefs for autonomous batch image processing?▼

Write token-conserving briefs for batch image processing by condensing creative requirements into concise instructions, allowing the autonomous workflow to generate, verify, and embed visuals efficiently.

When should I avoid first-pass image generation for design assets?▼

Avoid first-pass image generation for design assets when visuals require deeper reasoning and strict standards, as first outputs frequently miss professional quality gates and lack deliberate, principled design.