GPT-Image2 Agent Skill Workflow: A Deep Dive into the Six-Step Pipeline

The GPT-Image2 agent skill executes a deterministic six-step pipeline that detects input language, classifies output intent, hierarchically matches templates, and constructs production-ready prompts from six structured building blocks.

Located in the freestylefly/awesome-gpt-image-2 repository, this skill transforms high-level creative requests into concrete, reproducible prompts for GPT-Image2. The entire logic is defined in agents/skills/gpt-image-2-style-library/SKILL.md, which orchestrates the workflow from initial language detection through final template assembly.

How the GPT-Image2 Agent Skill Processes Requests

The core execution engine operates through six discrete stages, each documented in the SKILL.md specification. This pipeline ensures consistent, high-quality output regardless of input ambiguity.

1. Language Detection and Consistency

The workflow initiates by analyzing the user's prompt to detect the input language. According to SKILL.md lines 24‑25, the system commits to responding entirely in the same language, ensuring localized user experiences without requiring explicit language parameters.

2. Target Output Classification

Next, the skill classifies the intended output category. As defined in lines 25‑26 of SKILL.md, valid targets include product, poster, UI, infographic, brand, photo, illustration, character, scene, history, document, or specialized task types. This classification drives the subsequent template matching process.

3. Hierarchical Template Matching

The matching process follows a strict priority order defined in SKILL.md lines 26‑27: template category → visual style tag → scene tag → nearest example cases. This hierarchical approach ensures the system selects the most specific template available before falling back to broader style or scene cues.

4. Ambiguity Resolution Through Option Presentation

When the matching process yields multiple plausible templates, the skill implements a clarification protocol. Per lines 27‑28, it presents 2‑3 candidate templates with concise rationales and solicits user selection. If a single template dominates relevance scores, the workflow proceeds directly to prompt construction without interruption.

5. Structured Prompt Construction

The final prompt assembly follows a strict six-block architecture specified in SKILL.md lines 29‑34:

• Subject and task – Core content description
• Composition and layout – Spatial organization and structural elements
• Visual style and materials – Aesthetic qualities and texture specifications
• Text and label requirements – Typography and copy specifications
• Aspect ratio and output format – Technical delivery specifications
• Constraints and negative details – Exclusions and limitations to avoid

This structured approach guarantees complete, production-ready GPT-Image2 prompts that minimize hallucination and maximize reproducibility.

6. Template Metadata Inclusion

The final step appends the chosen template's name and relevant example case IDs to the output, as documented in lines 35‑36. This provides downstream agents with concrete reference points from the shared data/style-library.json repository.

Practical Examples of the GPT-Image2 Agent Skill Workflow

The following examples demonstrate how the pipeline handles both straightforward and ambiguous inputs.

Example 1: Direct Template Selection (Chinese Input)

使用 gpt-image-2-style-library 生成一个产品包装海报，突出环保主题。

Execution flow:

• Language detection identifies Chinese → response generated in Chinese
• Target classification identifies Product category
• Hierarchical matching selects the "Product Commerce Visual" template
• Prompt construction populates all six blocks (subject, layout, style, text, aspect ratio, constraints)
• Output appends template name and case IDs from data/style-library.json

Example 2: Ambiguous Request Resolution (English Input)

Create a visual for a tech infographic about AI workflow.

Execution flow:

• Language detection identifies English → response generated in English
• Target classification identifies Infographic category
• Hierarchical matching returns two candidates: "Infographic Engine" and "Scientific Scale Diagram"
• Option presentation displays both templates with brief rationales, requesting user selection
• Upon selection, prompt construction proceeds through the six-block assembly process

Core Infrastructure Supporting the Workflow

The GPT-Image2 agent skill relies on three primary assets defined in the repository structure:

• agents/skills/gpt-image-2-style-library/SKILL.md – Contains the complete workflow specification, step definitions, and execution logic
• data/style-library.json – Central repository of categories, visual styles, scene tags, and template metadata queried during the matching phase
• docs/templates.md – Houses the full textual templates referenced when constructing the six-block prompts

Summary

• The GPT-Image2 agent skill executes a deterministic six-step pipeline defined in SKILL.md
• Language detection ensures localized responses without explicit parameters
• Hierarchical matching prioritizes template categories over style or scene tags
• Ambiguity resolution presents 2‑3 candidates when multiple templates match
• Prompt construction follows a strict six-block structure ensuring production-ready outputs
• The system leverages data/style-library.json as its centralized style repository

Frequently Asked Questions

How does the GPT-Image2 agent skill handle multilingual requests?

The skill automatically detects the input language from the user's request and commits to responding entirely in that same language. According to SKILL.md lines 24‑25, this detection occurs as the first step in the workflow, eliminating the need for users to specify language parameters manually.

What happens when multiple templates match a single request?

When hierarchical matching yields multiple viable candidates, the skill enters an ambiguity resolution phase. As implemented in SKILL.md lines 27‑28, it presents 2‑3 template options with concise rationales for each, allowing the user to select the most appropriate match before proceeding to prompt construction.

What are the six building blocks used in GPT-Image2 prompt construction?

The final prompt is assembled from six specific blocks defined in SKILL.md lines 29‑34: subject and task, composition and layout, visual style and materials, text and label requirements, aspect ratio and output format, and constraints with negative details. This structure ensures comprehensive, reproducible GPT-Image2 prompts.

Where does the agent skill store its style templates and categories?

The skill queries data/style-library.json as its central repository for all template categories, visual style tags, scene classifications, and example case metadata. This shared JSON file serves both the agent skill and the accompanying website interface, ensuring consistency across implementations.

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