# How the Agent Skill in Awesome-GPT-Image-2 Automates Prompt Construction

> Discover how the Agent skill in awesome-gpt-image-2 automates prompt construction with an 8-step workflow, turning user intent into production-ready GPT-Image-2 prompts.

- Repository: [苍何/awesome-gpt-image-2](https://github.com/freestylefly/awesome-gpt-image-2)
- Tags: deep-dive
- Published: 2026-09-10

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**The Agent skill automates prompt construction by executing a deterministic 8-step workflow that transforms vague user intent into production-ready GPT-Image-2 prompts through hierarchical template matching, six-block assembly logic, and data-driven constraint enforcement.**

The **awesome-gpt-image-2** repository provides a specialized Agent-compatible skill called `gpt-image-2-style-library` that eliminates manual prompt engineering for OpenAI's image models. 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), this skill systematizes automation by leveraging a structured JSON index to construct, refine, and localize outputs based on repository-backed templates rather than memory or hard-coded defaults.

## The 8-Step Deterministic Workflow

The skill follows a strict procedural pipeline encoded in [`SKILL.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/SKILL.md) that converts raw requests into precise image generation instructions.

### Step 1: Language Detection and Response Alignment

The skill detects the language of the incoming request and locks the entire workflow to that language. If the user inputs Chinese, the Agent processes, reasons, and outputs in Chinese unless explicitly instructed otherwise.

### Step 2: Target Output Type Classification

Next, the Agent identifies the intended **target output type** from a fixed taxonomy of twelve categories: product, poster, UI, infographic, brand, photo, illustration, character, scene, history, document, or special task. This classification filters the available template pool.

### Step 3: Hierarchical Template Matching

The skill queries the **style-library index** ([`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json)) using a prioritized matching sequence:

1. Template category
2. Visual-style tag
3. Scene tag
4. Nearest example cases

This hierarchy ensures the Agent selects the most specific template available before falling back to broader matches.

### Step 4: Candidate Presentation and Selection

When multiple templates satisfy the matching criteria, the skill returns **2-3 strong candidates** with brief rationales explaining why each fits the request. The skill pauses for explicit user confirmation before proceeding, preventing arbitrary template selection.

### Step 5: The Six Building Blocks of Prompt Construction

Upon template selection, the skill assembles the final prompt using six mandatory building blocks:

- **Subject & task** – core content and action description
- **Composition & layout** – spatial arrangement and structural rules
- **Visual style & materials** – aesthetic direction and texture specifications
- **Text & label requirements** – typography, language, and readability constraints
- **Aspect ratio & output format** – technical specifications such as 16:9 or PNG
- **Constraints & negative details** – specific elements to exclude or avoid

### Step 6: Copy-Ready Output Generation

The skill formats the finalized prompt for immediate copying, placing it at the top of the response. Metadata follows, including the selected template name and relevant example-case IDs for traceability and reproducibility.

### Step 7: Concrete Constraint Enforcement

Before finalizing, the skill validates that all constraints remain concrete and actionable. This includes verifying exact wording for labels, confirming aspect ratio compatibility, ensuring text hierarchy readability, and explicitly listing artifacts to avoid.

### Step 8: Final Localization

The skill performs a final localization pass, ensuring Chinese requests produce Chinese prompts (and vice versa) unless the user explicitly requests English output, maintaining linguistic consistency across all prompt components.

## Architecture and Data Sources

The automation relies on two primary components that separate data from logic.

### The Style Library Index ([`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json))

This JSON file serves as the master data source containing all available templates, categories, visual-style tags, scene tags, known pitfalls, and example cases. The Agent queries this index during the matching phase to ensure selections reflect the most current repository state without embedding data directly into the workflow logic.

### The Skill Manifest ([`SKILL.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/SKILL.md))

Located at [`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), this human-readable manifest encodes the deterministic workflow, decision trees, and assembly instructions that the Agent follows. It functions as the execution blueprint, instructing the Agent how to process requests and construct prompts without requiring hard-coded implementations.

## Installation and Agent Integration

Install the skill into local Agent environments (Codex, Claude Code, or shared agents) using the provided CLI script:

```bash

# Installs to ~/.codex/skills, ~/.claude/skills, or ~/.agents/skills

npm run install:skill

```

This command executes `agents/skills/gpt-image-2-style-library/bin/install.mjs`, which copies the skill package into the appropriate Agent skill directories.

## Usage and Skill Regeneration

Once installed, Agents invoke the skill programmatically:

```javascript
const request = "用 gpt-image-2-style-library 技能生成城市生命系统图谱";

agent.invokeSkill('gpt-image-2-style-library', request).then(result => {
  console.log(result.prompt);   // Copy-ready GPT-Image-2 prompt
  console.log(result.template); // Selected template name
  console.log(result.examples);   // Relevant example-case IDs
});

```

When the underlying [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json) changes, regenerate the skill to synchronize the manifest with the latest templates:

```bash
npm run generate:style-skill

```

This command updates [`SKILL.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/SKILL.md) and related references 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) to reflect new additions to the style library index.

## Summary

- The **Agent skill** automates prompt construction through an **8-step deterministic workflow** defined in [`SKILL.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/SKILL.md).
- **Hierarchical matching** prioritizes template category, then visual-style tags, scene tags, and example cases from [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json).
- Prompt assembly uses **six building blocks**: subject/task, composition/layout, style/materials, text/labels, aspect ratio/format, and constraints/negative details.
- The skill supports **12 target output types** including product, poster, UI, infographic, and illustration.
- **Localization** ensures input and output languages match unless explicitly overridden.
- Installation occurs via `npm run install:skill`, with regeneration available through `npm run generate:style-skill`.

## Frequently Asked Questions

### How does the skill handle ambiguous requests?

When a request matches multiple templates, the skill presents **2-3 strong candidates** with brief rationales and asks the user to select one. This prevents arbitrary selection and ensures the final prompt aligns with specific user intent rather than defaulting to the first match.

### What happens when the style library is updated?

Run `npm run generate:style-skill` to regenerate the skill manifest from the latest [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json). This updates the template pool, tags, and example cases available to the Agent without requiring manual edits to [`SKILL.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/SKILL.md).

### Can the skill generate prompts in languages other than English?

Yes. The skill **detects the input language** at the start of the workflow and localizes the entire process, producing Chinese prompts for Chinese requests (and vice versa) unless the user explicitly requests English output.

### Where does the skill store its template data?

Template definitions, categories, and example cases reside in **[`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json)** at the repository root. The skill manifest at **[`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)** contains the procedural logic, while **[`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)** provides a human-readable reference derived from the JSON index.