image-generator

Generate compliant visuals from creative briefs with quality gates.

Updated Nov 29, 2025
One-click install
npx skills add https://github.com/92Bilal26/physical-ai-textbook --skill image-generator-92bilal26
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: image-generator
Source: https://github.com/92Bilal26/physical-ai-textbook/tree/main/.claude/skills/image-generator
Command: npx skills add https://github.com/92Bilal26/physical-ai-textbook --skill image-generator-92bilal26

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Visual generation often settles for generic aesthetics when prompts focus on technical specs. This skill provides a multi-turn reasoning partnership with Gemini to craft professional visuals that meet high standards and teaching goals.

Core Features & Use Cases

  • Reasoning over prediction: Activate reasoning with narrative briefs (Story/Intent/Metaphor) rather than technical specs.
  • Multi-turn partnership: Teach Gemini your standards through principle-based feedback across iterations.
  • Quality gates: Explicit pass/fail criteria ensure clarity, spelling, layout, color, typography, and teaching effectiveness.
  • Autonomous batch mode: Generate multiple visuals without back-and-forth permission prompts.

Quick Start

Provide a creative brief to Gemini, including The Story, Emotional Intent, Visual Metaphor, Subject / Composition / Action / Location / Style / Camera / Lighting, Color Semantics, Typography Hierarchy, and Pedagogical Reasoning; then trigger the browser-based generation workflow and deploy the result into your lesson.

Frequently Asked Questions about image-generator

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

FAQPage Schema
How do I generate professional-quality visuals from creative briefs using AI?

Professional-quality visual generation applies multi-turn reasoning with Gemini to transform narrative briefs—including story, intent, metaphor, composition, color semantics, and pedagogical goals—into compliant visuals. This skill automates batch browser-based generation with 5-gate quality checks (spelling, layout, color, typography, teaching effectiveness) and iterative feedback loops to meet high standards without generic output.

Can I automate batch image generation with quality gates and feedback loops?

Yes. Batch image generation with autonomous workflows and gate-based quality checks enables multi-turn brief-to-visual cycles without back-and-forth prompts. The skill applies principle-based feedback across iterations, enforces pass/fail criteria for clarity and teaching effectiveness, and integrates results into lesson deployment pipelines.

What's the best way to structure creative briefs for AI image generation?

Structure briefs with narrative reasoning layers—The Story, Emotional Intent, Visual Metaphor—paired with technical specification layers: Subject/Composition/Action/Location/Style/Camera/Lighting, Color Semantics, Typography Hierarchy, and Pedagogical Reasoning. This framework activates reasoning over prediction and teaches Gemini your standards through principle-based iteration.

How do I check if generated visuals meet teaching and design standards?

Quality gates enforce explicit pass/fail criteria across five dimensions: spelling accuracy, layout coherence, color semantics alignment, typography hierarchy clarity, and teaching effectiveness. Automated gate-based checks validate each visual and trigger per-visual iteration feedback without manual review overhead.

Does this approach work for scaling visual generation across multiple lessons?

Yes. The skill supports autonomous batch workflows with token-conservation prompts and embedded uniqueness checks, enabling multi-visual generation at scale. Results integrate directly into asset pipelines and lesson deployment, reducing manual iteration cycles while maintaining compliance with design and pedagogical standards.

What limitations should I know before using browser-based batch image generation?

Batch generation depends on Gemini's reasoning capabilities and Playwright's browser automation scope. Complex briefs require well-structured narrative and technical specification layers; incomplete briefs may produce generic output. Gate-based quality checks validate compliance but cannot guarantee creative novelty beyond embedded uniqueness constraints.