# How the GPT‑Image2 Agent Skill Processes User Requests in freestylefly/awesome‑gpt‑image‑2

> Discover how the GPT-Image2 agent skill processes user requests using a deterministic pipeline: detection, classification, template matching, and prompt assembly for efficient image generation.

- Repository: [苍何/awesome-gpt-image-2](https://github.com/freestylefly/awesome-gpt-image-2)
- Tags: internals
- Published: 2026-09-01

---

**The agent skill processes user requests through a deterministic, rule‑based pipeline: language detection, target output classification, template matching against a curated style library, optional candidate presentation, and structured prompt assembly with copy‑ready output.**

The **GPT‑Image2 style‑library skill** transforms vague user intent into production‑ready image generation prompts according to the open‑source repository `freestylefly/awesome-gpt-image-2`. This agentic skill combines a curated dataset of reverse‑engineered GPT‑Image2 cases with explicit workflow rules defined in [`SKILL.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/SKILL.md). Understanding how the agent skill processes user requests helps developers extend the system or debug prompt generation failures.

## Agent Registration and Configuration

The skill registers itself in the OpenAI agent ecosystem through [`agents/openai.yaml`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/agents/openai.yaml). This YAML file supplies metadata that agent frameworks consume:

- `display_name` – human‑readable skill label
- `short_description` – quick capability summary
- `default_prompt` – fallback instruction when user input is ambiguous

```yaml

# From agents/skills/gpt-image-2-style-library/agents/openai.yaml

display_name: "GPT-Image2 Style Library"
short_description: "Generate optimized prompts for GPT-Image2 using reverse-engineered style templates"
default_prompt: "Analyze the user's image request and match it to the best-fitting template from the style library."

```

The `default_prompt` value seeds the agent's reasoning before any user‑specific processing begins.

## Step‑by‑Step Request Processing Workflow

### Step 1: Language Detection

The skill commits to **response language parity**. It analyzes the user's input to determine Chinese or English, then generates all subsequent output—including prompts, rationales, and constraints—in that language. This rule appears verbatim in [`SKILL.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/SKILL.md): *"Detect the user's language and answer in that language."*

### Step 2: Target Output Identification

The skill classifies the user's desired deliverable into a **Category** from the style library. Valid categories include UI design, poster, product photography, illustration, scenes, and storytelling. This classification determines which subset of templates the matching algorithm prioritizes.

From [`SKILL.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/SKILL.md): *"Identify the user's target output … This determines which Category from the style library to prioritize."*

### Step 3: Template Matching Hierarchy

The skill applies **fixed‑priority selection rules** from [`style-library.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/style-library.md):

1. **Category** — explicit product type or domain
2. **Visual style tag** — descriptive aesthetic keywords
3. **Scene tag** — context, environment, or narrative setting
4. **Closest example cases** — nearest neighbors from >500 reverse‑engineered samples

This ordering guarantees deterministic behavior: a "futuristic product render" request always matches `product` category templates before considering scene‑based alternatives.

### Step 4: Candidate Presentation (Optional Fork)

When multiple templates score equally, the skill **pauses for user disambiguation**. It returns 2–3 candidates with concise rationales, then awaits selection. This prevents arbitrary prompt construction when user intent remains ambiguous.

Workflow rule: *"If several are plausible, present 2‑3 options … and ask the user to choose."*

### Step 5: Prompt Construction with Structured Blocks

Upon template selection, the skill assembles the final prompt from six explicit blocks defined in [`SKILL.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/SKILL.md):

| Block | Purpose |
|-------|---------|
| Subject & task | Core entity and action |
| Composition & layout | Framing, perspective, spatial arrangement |
| Visual style & materials | Aesthetic direction, textures, rendering approach |
| Text & label requirements | Copy, typography, readability constraints |
| Aspect ratio & output format | Technical specifications |
| Constraints & negative details | Exclusions, avoided elements, quality guardrails |

This block structure ensures prompts remain **complete yet modular**—developers can override single blocks without rewriting entire prompts.

### Step 6: Output Formatting with Language Defaults

The skill always emits content in this order:

1. **Copy‑ready prompt first** — immediately usable in GPT‑Image2
2. **Concrete constraints** — exact text strings, aspect ratios, readability rules
3. **Modification offer** — explicit yes/no gate for refinement

For **Chinese requests**, the skill applies Chinese‑specific defaults (full‑width punctuation, Simplified Chinese technical terms, local aspect‑ratio conventions). For **English requests**, it defaults to ASCII punctuation and imperial/metric agnostic specifications.

## Code Example: End‑to‑End Interaction

Below is a representative user interaction demonstrating all processing stages.

**User input:**

```text
用 gpt-image-2-style-library 技能生成城市生命系统图谱

```

**Skill output:**

```markdown

### Selected Template

**Template:** City life system map  
**ID:** scene-storytelling  
**Category:** Scenes & Storytelling  
**Styles:** Scenes, Illustration  
**Scenes:** Story, Social  

### Prompt (copy‑ready)

/imagine prompt: 城市生命系统图谱, 俯视视角, 细节丰富的街道与建筑, 霓虹灯光, 未来城市氛围, 4:3, 高分辨率, --ar 4:3 --v 5

### Constraints

- Text: 必须包含 "城市" 与 "系统图谱"。  
- Aspect Ratio: 4:3（宽高比）。  
- Layout: 采用俯视构图，主体位于画面中心。  
- Negative Details: 避免过度写实的阴影、无关的文字标签。

Would you like to modify any part of the prompt? (yes/no)

```

The output demonstrates: Chinese language parity, category‑driven template selection (`Scenes & Storytelling`), visual/style tag matching (`Illustration`, `Story`), and full block‑structured assembly with copy‑first formatting.

## Maintenance and Synchronization Pipeline

The skill remains synchronized with its data source through automated regeneration. The `scripts/generate-style-skill.mjs` script consumes [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json) and produces [`references/style-library.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/references/style-library.md):

```bash

# Regenerate reference from source data

npm run generate:skill

```

This script executes three operations:

1. Parses [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json) containing >500 GPT‑Image2 cases
2. Rebuilds [`references/style-library.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/references/style-library.md) with updated categories, tags, and constraints
3. Ensures [`SKILL.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/SKILL.md) references remain valid

Developers install the updated skill into local Codex environments via:

```bash
npm run install:skill

```

The [`package.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/package.json) in the skill folder defines both scripts, making the maintenance loop reproducible.

## Key Source Files and Their Roles

| File | Responsibility |
|------|--------------|
| [`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) | Canonical workflow, selection rules, output formatting, and maintenance instructions |
| [`agents/skills/gpt-image-2-style-library/agents/openai.yaml`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/agents/skills/gpt-image-2-style-library/agents/openai.yaml) | Agent registration metadata for OpenAI‑compatible frameworks |
| [`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) | Human‑readable template reference used during matching |
| `scripts/generate-style-skill.mjs` | Build script syncing JSON data to markdown reference |
| [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json) | Source‑of‑truth dataset with reverse‑engineered cases |
| [`agents/skills/gpt-image-2-style-library/package.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/agents/skills/gpt-image-2-style-library/package.json) | NPM script definitions for `generate:skill` and `install:skill` |

## Summary

- The agent skill processes user requests through **six deterministic stages** from language detection to copy‑ready output.
- **Template matching follows strict priority**: Category → Visual style → Scene → Example cases.
- All behavior is **explicitly documented** in [`SKILL.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/SKILL.md) and **data‑driven** via [`style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/style-library.json).
- **Language parity** is enforced throughout—Chinese inputs yield Chinese prompts, constraints, and rationales.
- **Automated regeneration** via `generate-style-skill.mjs` keeps the skill synchronized with its underlying dataset.

## Frequently Asked Questions

### How does the skill handle ambiguous user requests?

The skill **pauses and presents 2–3 candidate templates** with brief rationales, then requires explicit user selection. This prevents arbitrary prompt construction when target output classification yields multiple valid categories or style combinations.

### Can I modify the template matching priority?

The matching order—Category, then Visual style, then Scene, then Example cases—is **hardcoded in [`SKILL.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/SKILL.md) and [`style-library.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/style-library.md)**. To change this behavior, fork the repository and edit these files, then regenerate the skill with `npm run generate:skill`.

### What happens when the underlying style data updates?

Running `npm run generate:skill` executes `scripts/generate-style-skill.mjs`, which rebuilds [`references/style-library.md`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/references/style-library.md) from [`data/style-library.json`](https://github.com/freestylefly/awesome-gpt-image-2/blob/main/data/style-library.json). The skill immediately uses updated templates without code changes, then reinstall via `npm run install:skill` to propagate to your agent environment.