GPT Image 2 Prompt Gallery + Agentic Skill + CLI

OpenAI GPT Image 2 prompt gallery, image prompt library, agentic skill, and CLI — curated, copy-paste prompts and runnable examples for skill-capable agents.

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✨ At a glance

🔎 What this repo is for

Use this repo as a GPT Image 2 prompt gallery, image prompt library, example of generation showcase, Codex / Claude Code agent skill, and gpt-image-2 CLI. It includes reusable AI image prompts for research paper figures, posters, UI mockups, game HUDs, anime / manga, photography, typography, maps, tattoo design, and reference-image editing workflows.

This project is not trying to collect every prompt on the internet. We keep a selected set of examples that show what GPT Image 2 can do and how to use it well. Thanks for all the love this little gallery has received 🫶 — if time allows, I will also share the automated patch/update workflow behind it later.

[!CAUTION] For research figures, treat generated images as references, workflow sketches, or reproducible style targets. We do not recommend dropping GPT Image 2 outputs directly into a paper as-is; for academic communication, that can be misleading and is generally bad practice.

Contributions are welcome — see CONTRIBUTING.md, CODE_OF_CONDUCT.md, and SECURITY.md.

📥 Install

Before installing, check whether the skill or CLI is already available. Do not reinstall blindly, overwrite an existing skill folder, or create/replace API-key files. Use your runtime's own skill list/status command when available; global/shared installs should be an explicit user choice, not an automatic setup step.

command -v gpt-image || true
command -v uv >/dev/null && uv tool list | grep -E '^gpt-image-cli([[:space:]]|$)' || true
test -n "${OPENAI_API_KEY:-}" && echo "OPENAI_API_KEY is already set (value hidden)"

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

Codex ships with built-in skill helpers such as $skill-installer and $skill-creator. Open Codex and invoke the built-in installer with this GitHub skill-folder URL:

$skill-installer
Install this skill from GitHub:
https://github.com/wuyoscar/gpt_image_2_skill/tree/main/skills/gpt-image

The installer downloads that GitHub folder and places it under your Codex skills directory, usually:

~/.codex/skills/gpt-image

Restart Codex after installation so the new $gpt-image skill is loaded.

If you prefer to install it manually, copy the skill folder into Codex's skills directory:

git clone https://github.com/wuyoscar/gpt_image_2_skill.git
cd gpt_image_2_skill

mkdir -p "${CODEX_HOME:-$HOME/.codex}/skills"
test -e "${CODEX_HOME:-$HOME/.codex}/skills/gpt-image" && echo "gpt-image skill already exists; stop before overwriting" && exit 1
cp -R skills/gpt-image "${CODEX_HOME:-$HOME/.codex}/skills/"

For runtimes supported by the cross-agent skills installer, install the same skills/gpt-image folder directly from GitHub:

# Codex
npx --yes skills@latest add wuyoscar/gpt_image_2_skill \
  --skill gpt-image --agent codex --copy

# OpenClaw
npx --yes skills@latest add wuyoscar/gpt_image_2_skill \
  --skill gpt-image --agent openclaw --copy

These examples intentionally avoid --global. Add --global only when you explicitly want this skill installed into that runtime's global/shared skills directory.

If your runtime is not listed by skills@latest yet, use the manual Agent Skill install below.

Set AGENT_SKILLS_DIR to the skills directory used by your agent runtime, then symlink this repo's skill folder into it.

git clone https://github.com/wuyoscar/gpt_image_2_skill.git
cd gpt_image_2_skill

# Choose the skill directory for your runtime.
# Examples:
#   Codex:      ~/.codex/skills
#   Claude Code / OpenClaw / Hermes Agent / other runtimes: use that runtime's documented skills directory.
export AGENT_SKILLS_DIR="/path/to/your/agent/skills"

mkdir -p "$AGENT_SKILLS_DIR"
test -e "$AGENT_SKILLS_DIR/gpt-image" && echo "gpt-image skill already exists; stop before overwriting" && exit 1
ln -s "$PWD/skills/gpt-image" "$AGENT_SKILLS_DIR/gpt-image"

uvx --from git+https://github.com/wuyoscar/gpt_image_2_skill gpt-image -p "a cat astronaut"

# or install to PATH if not already installed
command -v gpt-image >/dev/null || uv tool install git+https://github.com/wuyoscar/gpt_image_2_skill
gpt-image -p "a cat astronaut"

# plugin: use Claude Code's update flow
# codex skill: rerun the installer
# manual git clone
cd gpt_image_2_skill && git pull

# CLI
uv tool upgrade gpt-image-cli

Reads OPENAI_API_KEY from process env, then .env, then ~/.env without overriding an already-set env var.

Agent + API-key note. Codex also has its own built-in image-generation skill, but that path is black-box and cannot be edited here; Codex users can switch to it if they prefer. Thanks to the related issue discussion for the simple safety tip: if you do not want an agent to accidentally use your OpenAI API key, run unset OPENAI_API_KEY before invoking the local CLI/skill.

⚡ Quick Usage & Prompting Fundamentals

After install, every gallery entry below can be copy-pasted as gpt-image -p "…" or requested from any skill-capable agent runtime in natural language, e.g. "generate the Boston Spring poster from the skill gallery".

Text → image

gpt-image -p "a photorealistic convenience store at 10pm" --size 1k --quality high -f store.png

Under the hood: POST /v1/images/generations with model=gpt-image-2.

Text + reference image → image (edit)

# Single-reference edit / restyle
gpt-image -p "Make it a winter evening with heavy snowfall" \
  -i chess.png --quality high -f chess-winter.png

# Multi-reference edit: the edits endpoint accepts multiple input images
gpt-image -p "Place the dog from image 2 next to the woman in image 1. Match the same lighting, composition, and background. Do not change anything else." \
  -i woman.png -i dog.png --size portrait --quality medium -f woman-with-dog.png

# Mask-based inpaint: opaque = keep, transparent = regenerate
gpt-image -p "replace sky with aurora" \
  -i photo.jpg -m sky_mask.png -f aurora.png

Under the hood: POST /v1/images/edits (multipart form), the official endpoint in the OpenAI cookbook. gpt-image-2 supports image, mask, prompt, size, quality, background, output_format, and n. Multiple -i inputs are supported for multi-reference edits.

Parameters (complete)

Budget / quality guide

There is no separate budget flag here — use --quality as the budget knob.

• low = cheap draft / collect / many variants
• medium = normal exploration / style probing
• high = final posters, Chinese text, diagrams, paper figures, banners

If you are generating dozens of candidates, start at low and only rerun finalists at high.

From gallery prompt → CLI / SDK

Every entry below ships just the prompt plus a metadata line ("size" · "quality" · source). Assemble the CLI / SDK call the same way every time — worked once here so per-entry code blocks can stay out of your way. Example for a "portrait" · "high" entry:

# CLI
gpt-image -p "<PROMPT FROM ENTRY>" --size portrait --quality high -f out.png

# OpenAI SDK — `size` is the literal pixels; the CLI shortcut maps to `1024x1536` for portrait
from openai import OpenAI
client = OpenAI()
result = client.images.generate(
    model="gpt-image-2",
    prompt="<PROMPT FROM ENTRY>",
    size="1024x1536",
    quality="high",
)

For reference-based edits, add -i ref.png (repeatable) and optionally -m mask.png on the CLI, or call client.images.edit(...) with image=[open(p, "rb") for p in refs]. Everything else stays identical to the generate path.

Exit codes: 0 success · 1 API/refusal error (full response body echoed to stderr) · 2 bad args or missing OPENAI_API_KEY.

📖 Prompting Fundamentals

Distilled from OpenAI's official GPT Image prompting guide (also archived locally at skills/gpt-image/references/openai-cookbook.md — loaded on demand by the skill when you ask about parameter semantics, edits, UI mockups, pitch-deck slides, scientific visuals, virtual try-on, billboard mockups, or translation edits):

1. Structure, then goal. Use a consistent order: background/scene → subject → key details → constraints, and state the intended use (ad, UI mock, infographic) so the model picks the right mode and polish level.
2. Any format works; consistency matters more. Minimal prompts, descriptive paragraphs, JSON-style structures, instruction-style prompts, and tag-based prompts all work. For production, prefer a skimmable template over clever syntax.
3. Specificity + quality cues. Be concrete about materials, shapes, textures, and medium (photo, watercolor, 3D render). Add targeted levers only when they matter: film grain, textured brushstrokes, macro detail. For photorealism, say "photorealistic" directly; "real photograph", "taken on a real camera", and "iPhone photo" also help.
4. Put required text in quotes. Any text that must appear in the image — slogans, prices, kanji — should be in straight quotes. Do not paraphrase it inside the prompt.
5. Choose aspect ratio early. Decide 1:1 / 3:4 / 4:3 / 9:16 / 16:9 / 3:1 before writing the prompt. Reinforce it in the prompt text, not only with --size.
6. One hero, supporting cast. Complex scenes work best when one subject is clearly primary and the rest is framed as supporting detail.
7. **Use quality="high" for in-image text, dense diagrams, small la