Noureddine RAMDI / gpt_image_2_skill: modular AI image generation prompts as an agent skill and CLI

Created Mon, 04 May 2026 10:23:01 +0000 Modified Sat, 23 May 2026 20:41:27 +0000
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github-stars python openai agent-skill image-generation cli prompt-engineering

wuyoscar/gpt_image_2_skill

AI image generation is evolving fast, but managing prompt libraries and API usage efficiently remains a challenge. The gpt_image_2_skill repo solves this by packaging a curated set of 162 GPT Image 2 prompts as a reusable agent skill and standalone CLI, enabling seamless natural language invocation across different AI agent runtimes while controlling API usage costs.

What gpt_image_2_skill offers and how it works

gpt_image_2_skill is a Python-based package that organizes a large, curated library of 162 image generation and editing prompts specifically designed for OpenAI’s Image API endpoints: /v1/images/generations and /v1/images/edits. The prompts cover a broad range of use cases such as research figures, UI mockups, photography styles, anime art, typography, and workflows for reference-image editing.

The repo packages these prompts both as an “agentic skill” compatible with AI coding agents like Claude Code, Codex, OpenClaw, and Hermes Agent, and as a standalone command-line interface (CLI). This dual interface design allows developers and AI agents to invoke image generation or editing tasks in natural language, leveraging the structured prompt library under the hood.

The architecture follows a modular skill pattern, placing the skill under a standardized AGENT_SKILLS_DIR convention. This enables seamless integration with agent runtimes that support such modular skills. The CLI wraps OpenAI’s image generation APIs, adding parameter validation, quality-based budget control to manage API cost, and support for editing images with multiple reference inputs.

Under the hood, the repo relies on Python packaging tools (like uv) and reads the OpenAI API key from environment variables or .env files to securely manage credentials. This setup ensures a clean separation of configuration and code.

Technical strengths in prompt engineering and API integration

What sets this repo apart is its focus on packaging a high-quality, curated prompt library as a reusable skill. Instead of having to craft image prompts from scratch or manage them manually, users get access to 162 tested prompts that cover a wide variety of image generation scenarios. This improves developer experience and consistency.

The skill design enables invocation via natural language from agent runtimes, which means AI assistants can call the skill without hardcoding parameters, making workflows more dynamic and adaptive.

Technically, the CLI wraps OpenAI’s image endpoints with additional functionality:

  • Parameter validation: Ensures requests to the API are well-formed before sending, reducing errors.
  • Quality-based budget control: Allows controlling API usage costs by adjusting image quality and generation parameters intelligently.
  • Multi-reference image editing: Supports workflows where multiple reference images are used for editing, a non-trivial feature given API constraints.

This combination of prompt engineering and API wrapping is a practical example of how to build modular AI capabilities that handle complexity (like budget control and multi-reference editing) transparently.

The code quality appears solid, with clean packaging and environment-based configuration. The modular skill pattern follows conventions that make it easy to integrate and update in agent runtimes.

Tradeoffs include the dependency on OpenAI’s API availability and pricing, as well as the need to manage environment variables securely. The skill approach abstracts complexity but adds a layer of indirection that requires familiarity with agent skill conventions.

How to install and get started with gpt_image_2_skill

The repo provides detailed installation instructions for multiple agent environments:

  • Claude Code: Install via the plugin marketplace with

    /plugin marketplace add wuyoscar/gpt_image_2_skill
/plugin install gpt-image@wuyoscar-skills

  • Codex: Use built-in skill helpers to install from GitHub:

    $skill-installer install https://github.com/wuyoscar/gpt_image_2_skill/tree/main/skills/gpt-image

    This places the skill under the usual ~/.codex/skills/gpt-image directory.

  • Manual agent skill installation:

    Clone the repo, then symlink or copy the skill folder into the agent’s skills directory defined by the AGENT_SKILLS_DIR environment variable.

  • Standalone CLI:

    Install the tool with uv:

    uv tool install git+https://github.com/wuyoscar/gpt_image_2_skill

    Then invoke from the command line:

    gpt-image -p "a cat astronaut"

The CLI provides direct access to image generation and editing capabilities using the curated prompt library.

Verdict: who should consider using gpt_image_2_skill

This repo is relevant for developers and AI practitioners building AI agents that need sophisticated image generation or editing capabilities integrated into their workflows. Its modular skill design makes it a good fit for those using Claude Code, Codex, or similar AI agent runtimes that support skill invocation.

The curated prompt library saves time and effort in prompt engineering, while the CLI offers a quick way to experiment or integrate image generation in scripts.

Limitations include reliance on OpenAI’s image APIs and the associated cost and rate limits, as well as the learning curve around agent skill conventions. The quality-based budget control helps mitigate some cost concerns but requires tuning for production use.

Overall, gpt_image_2_skill is a practical example of packaging AI capabilities as reusable skills with thoughtful API wrapping and prompt management. It’s worth exploring if your projects involve AI-driven image generation or editing triggered by natural language commands.

→ GitHub Repo: wuyoscar/gpt_image_2_skill ⭐ 1,340 · Python