# How the GPT-Image2 Skill Handles Multiple Plausible Template Options

> Discover how the GPT-Image2 skill navigates multiple template options. It presents user choices with clear reasons, ensuring the best fit.

- Repository: [苍何/awesome-gpt-image-2](https://github.com/freestylefly/awesome-gpt-image-2)
- Tags: internals
- Published: 2026-09-05

---

**When the GPT-Image2 skill encounters multiple suitable templates, it presents 2-3 candidate options with specific rationales and defers the final selection to the user rather than making an arbitrary choice.**

The `gpt-image-2-style-library` skill in the `freestylefly/awesome-gpt-image-2` repository implements a deterministic workflow for resolving ambiguity in template selection. This approach ensures that users maintain creative control while the skill provides structured guidance through its curated style library.

## Deterministic Template Matching Hierarchy

The skill follows a strict priority order when evaluating user requests against the template database defined in [`agents/skills/gpt-image-2-style-library/references/style-library.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/agents/skills/gpt-image-2-style-library/references/style-library.md).

### Category-Based Matching

First, the skill attempts to match the request by **template category**. This primary filter checks if the user's intent aligns with high-level classifications such as poster layouts, UI dashboards, or product photography styles. The category matching logic evaluates the request against the structured taxonomy defined in [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json).

### Fallback Tag Matching

If category matching yields no single clear result, the skill falls back to **visual-style tags** and then **scene tags**. According to [`agents/skills/gpt-image-2-style-library/SKILL.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/agents/skills/gpt-image-2-style-library/SKILL.md) (lines 26-27), this secondary analysis examines descriptive attributes like "minimalist," "cyberpunk," or "studio lighting" to narrow the candidate pool. The system cross-references these tags against the auto-generated index in the reference file to identify potentially compatible templates.

## Resolving Ambiguity Through User Choice

When the matching algorithm identifies multiple templates with equal relevance scores, the skill explicitly avoids probabilistic selection.

### Presenting Candidate Templates

Instead of guessing, the skill **presents 2-3 candidate templates**, each accompanied by a concise rationale explaining its suitability (SKILL.md, lines 27-28). For example, a request for a product promotion might return:
- `poster-layout-system` with the reason "强大的版式结构，适合商业海报" (strong layout structure suitable for commercial posters)
- `product-commerce-visual` with the reason "突出产品细节和包装展示" (highlights product details and packaging display)

The `askUserChoice` flag is set to `true`, pausing the workflow until the user selects their preferred direction.

### Final Prompt Assembly

After the user selects a template via the `finalize()` method, the skill constructs the final GPT-Image2 prompt using the selected template name, example case IDs, and standard prompt blocks (SKILL.md, lines 29-35). These blocks include subject definition, composition guidelines, style parameters, text placement rules, aspect ratio constraints, and technical constraints specific to the chosen template.

## Code Implementation Examples

The skill exposes a programmatic interface that integrates with agent-based architectures.

### JavaScript Integration

Developers interact with the skill through the agent's skill registry:

```javascript
// Initialize the skill
const skill = await agent.useSkill('gpt-image-2-style-library');

// Request template options
const response = await skill.run({
  request: '生成一个适合产品宣传的海报',
  language: 'zh'  // Language detection is automatic
});

// Response contains multiple options for user selection
{
  options: [
    { id: 'poster-layout-system', reason: '强大的版式结构，适合商业海报' },
    { id: 'product-commerce-visual', reason: '突出产品细节和包装展示' }
  ],
  askUserChoice: true
}

// Finalize after user selection
await skill.finalize({ chosenId: 'poster-layout-system' });

```

### Agent Configuration

The skill is registered declaratively in [`agents/openai.yaml`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/agents/openai.yaml) with explicit instructions for handling ambiguity:

```yaml
name: gpt-image-2-style-library
description: |
  Choose a GPT-Image2 visual style / template from the style library.
  If several templates are plausible, present 2-3 options with short reasons
  and ask the user to choose before generating the final prompt.

```

The build script `scripts/generate-style-skill.mjs` automatically regenerates both [`SKILL.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/SKILL.md) and the reference documentation whenever the underlying [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json) is updated, ensuring the selection logic remains synchronized with the latest template definitions.

## Summary

- The skill uses a **three-tier matching system**: template category first, then visual-style tags, then scene tags.
- When faced with **multiple plausible matches**, the skill explicitly refuses to guess and instead surfaces 2-3 curated options.
- Each candidate includes a **specific rationale** explaining its relevance to the user's request.
- The workflow pauses for **explicit user confirmation** via the `finalize()` method before constructing the final GPT-Image2 prompt.
- All template definitions and matching logic are sourced from [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json) and documented in [`references/style-library.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/references/style-library.md).

## Frequently Asked Questions

### What happens if only one template matches the request?

If the deterministic matching process yields a single clear candidate, the skill bypasses the selection dialog and proceeds directly to final prompt construction using the matched template's specifications.

### How does the skill determine which templates are "plausible"?

The skill evaluates templates against the request using weighted criteria defined in the style library schema. A template becomes a candidate when it meets threshold scores for either category alignment, tag intersection, or a combination of both visual-style and scene descriptors.

### Can the skill automatically select a template without user input?

No. According to the source code in [`agents/skills/gpt-image-2-style-library/SKILL.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/agents/skills/gpt-image-2-style-library/SKILL.md), when multiple templates are equally suitable, the skill is explicitly configured to set `askUserChoice: true` and wait for user confirmation. This design prevents arbitrary selections that might not align with the user's specific creative intent.

### Where are the template definitions stored?

The canonical template data resides in [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json), which powers the auto-generated reference file at [`agents/skills/gpt-image-2-style-library/references/style-library.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/agents/skills/gpt-image-2-style-library/references/style-library.md). The `scripts/generate-style-skill.mjs` build script synchronizes these files with the skill's selection logic.