How the gpt-image-2-style-library Agent Skill Workflow Generates Production-Ready GPT-Image-2 Prompts

The gpt-image-2-style-library agent skill workflow transforms natural language image requests into structured GPT-Image-2 prompts through a six-stage pipeline involving language detection, target classification, hierarchical template matching, and modular prompt construction.

The gpt-image-2-style-library is a specialized agent skill within the freestylefly/awesome-gpt-image-2 repository that automates prompt engineering for OpenAI's GPT-Image-2 model. Unlike generic prompt generators, this skill leverages a curated style library with hierarchical matching rules to ensure consistent, production-quality output. By analyzing the source code in agents/skills/gpt-image-2-style-library/SKILL.md (lines 22-36) and its reference files, we can trace exactly how user requests map to specific templates and structured prompt blocks.

Understanding the Core Architecture

The workflow relies on three primary data sources that form a single source of truth for the agent.

SKILL.md: The Workflow Definition

Located at agents/skills/gpt-image-2-style-library/SKILL.md, this file encodes the operational logic for the skill. It defines the six-stage processing pipeline, selection rules for ambiguous matches, and the default output format required by the agent.

style-library.md: The Reference Index

The generated reference at agents/skills/gpt-image-2-style-library/references/style-library.md (lines 5-11) contains the complete template taxonomy. This includes selection rules, visual style tags, scene classifications, and example case mappings that the skill consults during the matching phase.

style-library.json: The Data Source

Raw template data lives in data/style-library.json. Both the skill agent and the repository website consume this single JSON file, ensuring that programmatic prompt generation stays synchronized with the latest style definitions.

The Six-Stage gpt-image-2-style-library Workflow Explained

According to the skill definition in SKILL.md, the end-to-end workflow processes every request through the following stages.

Stage 1: Language Detection and Localization

The skill first detects the input language of the user's request. It then formulates all subsequent responses—including template rationales and final prompts—in that same language, ensuring localized user experiences without manual translation.

Stage 2: Target Output Identification

Next, the agent classifies the requested output into specific categories: product, poster, UI, infographic, brand, photo, illustration, character, scene, history, document, or specialized task types. This classification determines which template categories are searched during the matching phase.

Stage 3: Hierarchical Template Matching

The request is matched against the style library using a strict hierarchy: template category → visual style tag → scene tag → nearest example cases. This cascading filter ensures the most specific templates are evaluated before falling back to general-purpose options.

Stage 4: Template Selection Logic

Two scenarios govern template selection:

• Single dominant match: When one template clearly outscores others across the hierarchy, the skill selects it immediately and proceeds to prompt construction.
• Ambiguous matches: When 2-3 templates score similarly, the skill presents each option with a short rationale and asks the user to select one before continuing.

Stage 5: Structured Prompt Construction

Once a template is locked, the skill assembles the final GPT-Image-2 prompt from six modular blocks:

1. Subject and task: The core content description and generation objective
2. Composition and layout: Spatial arrangement, framing, and structural guidelines
3. Visual style and materials: Artistic direction, textures, rendering techniques, and medium specifications
4. Text and label requirements: Typography rules, caption placements, and language-specific labeling needs
5. Aspect ratio and output format: Technical specifications for dimensions and file formats
6. Constraints and negative details: Exclusions, style avoidances, and quality guardrails

Stage 6: Result Output and Reference Linking

The final output delivered to the user includes three components: a copy-ready GPT-Image-2 prompt, the selected template name for documentation purposes, and links to relevant example case IDs from the style library index.

Installing and Using the Skill

You can integrate the gpt-image-2-style-library workflow into your agent environment using the CLI or programmatically.

CLI Installation

Install the skill globally for claude-code or codex agents using the following command:

npx skills add freestylefly/awesome-gpt-image-2 \
    --skill gpt-image-2-style-library \
    --agent claude-code codex \
    --global --yes --copy

Sample Interaction

A typical request flows as follows:

User: 用 gpt-image-2-style-library 技能生成城市生命系统图谱
Agent:
> Selected template: “UI Screenshot System”
> Prompt (copyable):
  <copy-ready GPT-Image-2 prompt generated according to the workflow>
> Example cases: case 17, case 2, case 4

Programmatic Usage

For Node.js applications, you can invoke the skill directly:

import { execSync } from 'child_process';

// Install the skill globally (once)
execSync('npm install -g gpt-image-2-style-library');

// Run the skill to generate a prompt
const result = execSync('gpt-image-2-style-library generate "Create an infographic about AI safety"');
console.log(result.toString());

Summary

• The gpt-image-2-style-library agent skill resides in the freestylefly/awesome-gpt-image-2 repository and automates GPT-Image-2 prompt generation through a six-stage workflow defined in SKILL.md.
• Hierarchical matching follows the priority: template category → visual style tag → scene tag → example cases, ensuring precise template selection.
• Template selection either proceeds automatically with a single dominant match or presents 2-3 options for user confirmation when ambiguity exists.
• Prompt construction assembles six distinct blocks covering subject, composition, style, text, technical specs, and constraints.
• The skill maintains consistency by sourcing template definitions from data/style-library.json, ensuring the agent and reference documentation remain synchronized.

Frequently Asked Questions

How does the gpt-image-2-style-library matching hierarchy prioritize templates?

The skill evaluates candidates in cascading order: first by template category (e.g., UI vs. poster), then visual style tag (e.g., minimalist vs. ornate), followed by scene tag (e.g., urban vs. natural), and finally nearest example cases for edge-case refinement. This structure, documented in references/style-library.md, ensures the most semantically relevant template wins.

What are the six components of the constructed GPT-Image-2 prompt?

The final prompt consists of: subject and task (what to depict), composition and layout (how to arrange it), visual style and materials (aesthetic qualities), text and label requirements (typography and copy), aspect ratio and output format (technical specs), and constraints and negative details (what to exclude). These blocks are concatenated in this specific order per the workflow definition in SKILL.md lines 22-36.

How does the skill handle ambiguous or multi-template scenarios?

When the hierarchical scoring yields multiple viable templates, the skill enters a clarification mode. It presents 2-3 top candidates with concise rationales explaining why each fits the request, then pauses for explicit user selection before proceeding to prompt construction. This prevents template misalignment on vague inputs like "create a modern illustration."

Where does the gpt-image-2-style-library source its template definitions?

All template categories, style tags, and selection rules originate from data/style-library.json in the repository root. The skill consumes this JSON directly, while human-readable versions are generated into agents/skills/gpt-image-2-style-library/references/style-library.md. This single-source architecture ensures that updates to style-library.json immediately propagate to both the agent skill and the project documentation.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}