gpt-image-2-skill

# GPT Image 2 Skill > Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection. A prompt gallery, CLI, and agentic skill for OpenAI's `gpt-image-2` model. Provides 162 curated prompts across categories (research figures, UI mockups, typography, photography, anime, maps, product shots), a full-featured CLI, and skill integrations for Claude Code, Codex, and other agent runtimes. --- ## Install ### CLI (fastest) ```bash # Run without installing uvx --from git+https://github.com/wuyoscar/gpt_image_2_skill gpt-image -p "a cat astronaut" # Install to PATH permanently uv tool install git+https://github.com/wuyoscar/gpt_image_2_skill gpt-image -p "a cat astronaut" ``` ### Claude Code ```text /plugin marketplace add wuyoscar/gpt_image_2_skill /plugin install gpt-image@wuyoscar-skills ``` ### Codex ```text $skill-installer install https://github.com/wuyoscar/gpt_image_2_skill/tree/main/skills/gpt-image ``` ### Manual agent-skill install ```bash git clone https://github.com/wuyoscar/gpt_image_2_skill.git cd gpt_image_2_skill export AGENT_SKILLS_DIR="/path/to/your/agent/skills" mkdir -p "$AGENT_SKILLS_DIR" ln -s "$PWD/skills/gpt-image" "$AGENT_SKILLS_DIR/gpt-image" ``` --- ## Configuration The CLI and skill read your OpenAI key from the environment or `~/.env`: ```bash export OPENAI_API_KEY="sk-..." ``` No other configuration is required. --- ## CLI Reference ### Text → Image (generation) ```bash # Basic generation gpt-image -p "a photorealistic convenience store at 10pm" # With size, quality, and explicit output file gpt-image -p "a neon-lit Tokyo alley at midnight" \ --size portrait --quality high -f tokyo-alley.png # Square, low quality (cheap draft) gpt-image -p "watercolor mountains at sunrise" \ --size 1k --quality low -f draft.png # Batch: generates 4 variants, saved as out_0.png … out_3.png gpt-image -p "product shot of a ceramic mug on white" \ --size square --quality medium -n 4 -f out.png ``` ### Text + Reference Image → Image (edit / restyle) ```bash # Single reference restyle gpt-image -p "Make it a winter evening with heavy snowfall" \ -i chess.png --quality high -f chess-winter.png # Multi-reference composite: dog from image 2, scene from image 1 gpt-image -p "Place the dog from image 2 next to the woman in image 1. \ Match the same lighting, composition, and background." \ -i woman.png -i dog.png --size portrait --quality medium -f woman-with-dog.png ``` ### Mask-based Inpainting ```bash # opaque pixels = keep, transparent pixels = regenerate gpt-image -p "replace sky with aurora borealis" \ -i photo.jpg -m sky_mask.png -f aurora.png ``` ### Full Parameter Reference | Flag | Values | Default | Notes | |---|---|---|---| | `-p, --prompt` | string | required | Full prompt text | | `-f, --file` | path | auto-timestamped `.png` | Output file path | | `-i, --image` | path (repeatable) | — | Triggers `/v1/images/edits`; pass multiple for multi-ref | | `-m, --mask` | path (PNG with alpha) | — | Requires `-i`; transparent = regenerate | | `--size` | `1k` `2k` `4k` `portrait` `landscape` `square` `wide` `tall` or `1024x1024` | `1024x1024` | Literals must be 16-px multiples, max edge 3840 | | `--quality` | `auto` `low` `medium` `high` | `high` | Budget dial: `low`=drafts, `high`=final/text-heavy | | `-n, --n` | int | 1 | Batch count; suffixes files `_0`, `_1`, … | | `--background` | `auto` `opaque` | API default | `opaque` disables transparency | | `--moderation` | `auto` `low` | `low` | `low` for broader exploration | | `--format` | `png` `jpeg` `webp` | `png` | Response encoding format | | `--compression` | 0–100 | — | JPEG/WebP only | **Exit codes:** `0` success · `1` API/refusal error · `2` bad args or missing key --- ## Python SDK Usage ### Text → Image ```python from openai import OpenAI client = OpenAI() # reads OPENAI_API_KEY from environment result = client.images.generate( model="gpt-image-2", prompt="A photorealistic ceramic mug on a white studio background, " "soft directional light, light shadow beneath", size="1024x1024", # square quality="high", ) # Save result import base64 from pathlib import Path image_bytes = base64.b64decode(result.data[0].b64_json) Path("mug.png").write_bytes(image_bytes) print("Saved mug.png") ``` ### Portrait / Tall Generation ```python result = client.images.generate( model="gpt-image-2", prompt="Minimalist event poster: 'Boston Spring Jazz Festival · April 2026' " "in bold serif, pastel cherry-blossom watercolor background, centered layout", size="1024x1536", # portrait (3:4) quality="high", ) ``` ### Image Edit (single reference) ```python result = client.images.edit( model="gpt-image-2", image=open("chess.png", "rb"), prompt="Make it a winter evening with heavy snowfall, keep the chess pieces identical", size="1024x1024", quality="high", ) ``` ### Multi-Reference Edit ```python result = client.images.edit( model="gpt-image-2", image=[open("woman.png", "rb"), open("dog.png", "rb")], prompt="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.", size="1024x1536", quality="medium", ) ``` ### Mask-Based Inpainting ```python result = client.images.edit( model="gpt-image-2", image=open("photo.jpg", "rb"), mask=open("sky_mask.png", "rb"), # transparent = regenerate prompt="Replace the sky with dramatic aurora borealis, keep everything below the horizon identical", size="1024x1024", quality="high", ) ``` ### Batch Generation with Saving ```python import base64 from pathlib import Path from openai import OpenAI def generate_batch(prompt: str, n: int = 4, size: str = "1024x1024", quality: str = "medium", out_prefix: str = "variant") -> list[Path]: client = OpenAI() result = client.images.generate( model="gpt-image-2", prompt=prompt, size=size, quality=quality, n=n, ) paths = [] for i, item in enumerate(result.data): path = Path(f"{out_prefix}_{i}.png") path.write_bytes(base64.b64decode(item.b64_json)) paths.append(path) print(f"Saved {path}") return paths # Usage variants = generate_batch( prompt="product shot of a blue glass water bottle, white background, studio lighting", n=4, quality="low", # cheap sweep; rerun winner at high ) ``` --- ## Prompt Engineering Patterns ### Structure template ``` [background/scene] → [subject] → [key details] → [constraints/intended use] ``` ### Research paper figure ```bash gpt-image -p "Clean scientific diagram: transformer architecture overview. \ White background, labeled encoder/decoder blocks with arrows, \ color-coded attention heads in teal and orange, \ sans-serif labels, publication-ready, 4K resolution" \ --size landscape --quality high -f transformer-diagram.png ``` ### UI mockup ```bash gpt-image -p "Mobile app UI mockup, iOS style, dark mode. \ Fitness tracking dashboard: circular progress ring in neon green, \ daily steps '8,432', heart rate '74 bpm', \ bottom nav with 4 icons, pixel-perfect, no lorem ipsum" \ --size portrait --quality high -f fitness-app.png ``` ### Typography poster ```bash gpt-image -p "Event poster. Text: 'SUMMER SONIC 2026' in bold condensed sans-serif. \ Subtext: 'Tokyo · August 9–10'. Vivid sunset gradient background (magenta to amber). \ Geometric grid overlay, high contrast, print-ready" \ --size portrait --quality high -f poster.png ``` ### Photorealistic product shot ```bash gpt-image -p "Photorealistic product photo: matte black insulated coffee thermos, \ condensation droplets, placed on dark slate surface, \ single soft key light from upper-left, shallow depth of field, \ shot on Canon 5D, 85mm lens, commercial quality" \ --size square --quality high -f thermos.png ``` ### Put required text in quotes ```python # Any text that must appear verbatim in the image — put in straight quotes in the prompt prompt = '''Storefront sign reading "OPEN 24/7" in red neon. Below it: "Est. 1987" in smaller white block letters. Realistic neon glow, night scene, rain-slicked pavement.''' ``` --- ## Quality / Budget Strategy | Stage | `--quality` | When to use | |---|---|---| | Exploration sweep | `low` | Generating 8–16 variants to find direction | | Normal iteration | `medium` | Style probing, layout checks | | Final / shipping | `high` | In-image text, dense diagrams, posters, paper figures | **Rule of thumb:** start every new concept at `low`, run 4 variants, pick the best, then rerun at `high`. ```bash # Step 1: cheap sweep gpt-image -p "minimalist logo for a coffee brand" --quality low -n 4 -f logo.png # Step 2: pick winner (e.g. logo_2.png), rerun at high gpt-image -p "minimalist logo for a coffee brand" --quality high -f logo-final.png ``` --- ## Size Reference | Alias | Pixels | Ratio | Best for | |---|---|---|---| | `square` / `1k` | 1024×1024 | 1:1 | Social posts, icons, product shots | | `portrait` | 1024×1536 | 2:3 | Mobile UI, posters, stories | | `landscape` | 1536×1024 | 3:2 | Web banners, diagrams | | `wide` | 1792×1024 | 7:4 | Cinematic, hero sections | | `tall` | 1024×1792 | 4:7 | Long-form mobile content | | `2k` | 2048×2048 | 1:1 | High-res assets | --- ## Common Patterns & Recipes ### Virtual try-on (multi-ref edit) ```python # image 1 = person, image 2 = garment result = client.images.edit( model="gpt-image-2", image=[open("person.png", "rb"), open("shirt.png", "rb")], prompt="Dress the person in image 1 wearing the shirt from image 2. " "Keep the person's face, pose, and background identical. " "Natural fabric draping and lighting.", size="1024x1536", quality="high", ) ``` ### Billboard / signage mockup ```python result = client.images.edit( model="gpt-image-2", image=open("billboard_photo.jpg", "rb"), mask=open("billboard_mask.png", "rb"), prompt='Replace the billboard face with: "SALE ENDS SUNDAY" ' 'in bold white text on solid red background. ' 'Match perspective and lighting of surrounding scene.', size="1536x1024", quality="high", ) ``` ### Anime / manga style transfer ```bash gpt-image -p "Anime key visual style (Studio Ghibli-inspired): \ young woman standing on a hillside overlooking a coastal town at golden hour, \ painterly backgrounds, soft cel shading, \ detailed environmental storytelling, cinematic composition" \ --size landscape --quality high -f anime-scene.png ``` ### Translation / text replacement edit ```python # Replace text in an existing image in a different language result = client.images.edit( model="gpt-image-2", image=open("menu_english.png", "rb"), prompt='Replace all English text with Japanese translations. ' 'Keep the exact same layout, fonts, colors, and imagery. ' 'Translate "Grilled Salmon" → "グリルサーモン", ' '"Caesar Salad" → "シーザーサラダ".', size="1024x1024", quality="high", ) ```