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openai-gpt-image

Generate, edit, and composite still images with OpenAI GPT Image models.

123|21|Updated Jul 11, 2026
Search Tags:#prompt engineering#image generation#image editing#openai#media production#visual qa#gpt image
One-click install
npx skills add https://github.com/calesthio/generative-media-skills --skill openai-gpt-image
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openai-gpt-image
Source: https://github.com/calesthio/generative-media-skills/tree/main/skills/providers/image-generation/openai-gpt-image
Command: npx skills add https://github.com/calesthio/generative-media-skills --skill openai-gpt-image

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps AI agents reliably generate, edit, composite, and review still images with OpenAI GPT Image models while avoiding deprecated integrations, invalid parameters, unsafe likeness workflows, and incomplete deliverables.

Core Features & Use Cases

  • Model and API Selection: Choose GPT Image 2, distinguish the direct Image API from conversational Responses workflows, and migrate legacy GPT Image and DALL-E integrations.
  • Production Image Workflows: Create detailed prompts, generate variants, edit and composite reference images, use masks and multiple inputs, preserve visual invariants, and handle exact in-image text.
  • Output and Reliability Controls: Decode and save base64 image output, process partial-image streams, handle moderation and transient failures, estimate cost and rate limits, and record request metadata.
  • Safety and Release Review: Apply consent, rights, privacy, provenance, authenticity, brand, and final-asset QA checks for customer-facing media.
  • Use Case: Create a branded product poster, replace an object in a photographed scene with a masked reference edit, or run a conversational image workflow with a controlled follow-up change.

Quick Start

Use the openai-gpt-image skill to plan, generate, or edit a still image with GPT Image 2 and return the saved asset, metadata, safety checks, and final QA findings.

Frequently Asked Questions about openai-gpt-image

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate production-ready images with OpenAI GPT Image models?▼

To generate production-ready images with OpenAI GPT Image, you must explicitly select the model and API, provide valid image and mask inputs, handle base64 output, and run final-asset QA checks. This ensures reliable media production while preventing deprecated integrations.

Can I edit existing photos and composite multiple reference images using GPT Image?▼

Yes, you can edit and composite reference images using GPT Image by applying masked edits and multiple inputs. The workflow requires preserving visual invariants, applying valid mask inputs, and passing the generated assets through final-asset QA and safety checks.

What's the best way to migrate legacy DALL-E integrations to GPT Image 2?▼

Migrating legacy DALL-E integrations to GPT Image 2 requires distinguishing the direct Image API from conversational Responses workflows and planning the migration to avoid deprecated integrations and invalid parameters during media production.

Does GPT Image support exact text rendering inside generated images?▼

Yes, GPT Image supports exact in-image text rendering within production workflows. Generating images with exact text requires explicit model selection and proper prompt engineering to ensure the visual invariants are preserved during output.

How do I handle base64 image output and partial-image streams from the GPT Image API?▼

Handling base64 image output and partial-image streams from the GPT Image API requires decoding and saving the output, processing transient-failure recovery, estimating cost and rate limits, and recording request metadata to ensure reliable asset delivery.

Why does my customer-facing media release need rights, consent, and provenance checks?▼

Customer-facing media releases require rights, consent, privacy, provenance, and authenticity checks to ensure safety. Applying these final-asset QA checks during GPT Image workflows prevents unsafe likeness workflows and incomplete deliverables.

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