How the GPT-Image2 Agent Skill Matches Templates and Style Tags: Algorithm and Implementation

The GPT-Image2 agent skill uses a deterministic tag-matching algorithm to score prompt templates by counting overlapping keywords between user requests and style tags defined in data/style-library.json, selecting the highest-scoring entry without employing heavy NLP.

The GPT-Image2 Style-Library skill in the freestylefly/awesome-gpt-image-2 repository enables AI agents like Claude Code and Cursor to automatically select optimal visual templates and style configurations. Rather than relying on complex natural language processing, the skill implements a fast, auditable token-matching system that compares extracted request keywords against predefined template metadata. This approach ensures consistent performance and reproducible template selection across different agent environments.

The Deterministic Matching Pipeline

The matching process operates through four distinct phases, implemented primarily in agents/skills/gpt-image-2-style-library/SKILL.md and its underlying logic.

Loading the Shared Style Library

The skill reads data/style-library.json, a centralized catalog generated by the build script scripts/generate-style-skill.mjs. Each entry contains:

• A template definition (JSON or textual prompt body)
• An array of style tags (e.g., ["infographic", "dark", "minimal"])
• A category classification
• Optional pitfalls describing constraints or negative prompts

Parsing User Requests

When receiving free-form input such as "Create a dark-mode infographic about renewable energy", the skill extracts keywords using a simple tokenizer. This process splits the string on whitespace and punctuation, converting all tokens to lowercase to produce an array like ["create", "a", "dark", "mode", "infographic", "about", "renewable", "energy"].

Scoring by Tag Overlap

For every template entry, the skill computes a match score by filtering the template's tags against the request tokens:

const score = tags.filter(t => requestTokens.includes(t)).length;

The score equals the count of style tags appearing in the user's request. Some implementations augment this with category weighting (e.g., boosting "Infographic" templates when the request contains related terms), though the primary metric remains exact string matching.

Selection and Prompt Assembly

The skill selects the entry with the highest score. When ties occur, secondary criteria may include:

• Minimizing the number of pitfalls triggered
• Preferring shorter template lengths
• Defaulting to the first entry if all scores equal zero

The chosen template's body is returned to the agent alongside the matched style tags, ready for variable injection (e.g., product names, brand colors) before calling the image-generation API.

Source Code Architecture

The implementation spans four critical files that form an end-to-end pipeline:

• data/style-library.json – The central JSON datastore containing all templates, tags, categories, and pitfalls. Both the website frontend and agent skills consume this single source of truth.
• agents/skills/gpt-image-2-style-library/SKILL.md – The skill entry point that loads the JSON and implements the extraction, scoring, and selection logic.
• scripts/generate-style-skill.mjs – Build-time script that parses docs/templates.md and regenerates the shared library when running npm run generate:style-skill.
• src/main.jsx (lines 2871–2876) – Client-side usage example demonstrating how the React frontend consumes the same styleLibrary object to display templates to users.

Practical Implementation Examples

Simple Tag-Matching Function

The core algorithm can be implemented in JavaScript as follows:

import styleLibrary from '../data/style-library.json';

/**
 * Find the best template for a request.
 * @param {string} request – free-form user request
 * @returns {object} – the selected template entry
 */
function matchTemplate(request) {
  const tokens = request.toLowerCase().split(/\W+/);
  
  const scored = styleLibrary.templates.map(t => ({
    entry: t,
    score: t.tags.filter(tag => tokens.includes(tag)).length,
  }));
  
  scored.sort((a, b) => b.score - a.score);
  return scored[0].entry;
}

// Example usage
const request = 'Create a dark infographic about renewable energy';
const chosen = matchTemplate(request);
console.log('Template:', chosen.name);
console.log('Matched tags:', chosen.tags.filter(tag => request.includes(tag)));

Agent Invocation

To invoke the skill from Claude Code or compatible agents:

/agent use gpt-image-2-style-library
Use gpt-image-2-style-library to create a dark‑mode infographic about renewable energy.

The agent internally executes the matching logic, injects any user-provided variables into the selected template, and calls the image-generation endpoint (e.g., APIMart) with the assembled prompt.

Regenerating the Style Library

Update template definitions by running:

npm run generate:style-skill

This executes scripts/generate-style-skill.mjs, which reads the markdown documentation and outputs the updated data/style-library.json.

Summary

• Deterministic matching eliminates ambiguity: The skill relies on exact tag token matching rather than probabilistic NLP, ensuring identical requests always yield identical template selections.
• Centralized data model: All templates and metadata reside in data/style-library.json, shared between the web frontend (src/main.jsx) and agent skills.
• Simple scoring algorithm: The system counts overlapping tags between user requests and template definitions, with optional secondary tie-breakers for edge cases.
• Build-time synchronization: The generate-style-skill.mjs script ensures template documentation and consumable JSON remain synchronized.

Frequently Asked Questions

How does the skill handle requests with no matching tags?

If no tags overlap between the request and any template, the skill typically falls back to the first entry in the library or a default template with a score of zero. This ensures the agent always receives a valid prompt structure even for ambiguous or novel requests.

What distinguishes the JSON library from the SKILL.md file?

data/style-library.json contains the machine-readable template data consumed by both agents and the website, while agents/skills/gpt-image-2-style-library/SKILL.md provides the human-readable documentation and the actual JavaScript logic implementation that loads and queries that JSON during runtime.

Can developers customize the matching algorithm for specific agents?

Yes, since the scoring logic is implemented in standard JavaScript within the skill definition, developers can modify the weighting system, add category-based boosts, or implement custom tokenizers by editing the filtering functions in agents/skills/gpt-image-2-style-library/SKILL.md.

Where does the style library content originate?

The content is authored in docs/templates.md as structured markdown, then processed by scripts/generate-style-skill.mjs during the build process (npm run generate:style-skill) to produce the final data/style-library.json consumed by the skill.

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