image-gen-router

name

image-gen-router

description

Generate an image from a text prompt, choosing between two local headless backends - GPT (Codex CLI imagegen, runs under your ChatGPT login) or Gemini (Antigravity agy CLI, runs under your Google login). Both are API-key-free and incur no per-image API billing. Use this whenever the user wants to create, generate, draw, make, or render a picture, illustration, icon, thumbnail, logo, poster, artwork, or background from a description - for example "generate an image of a mountain at sunset", "make me a flat-vector server icon", or "draw a cat in watercolor". The defining feature is model choice. If the user names a backend (GPT, OpenAI, codex OR Gemini, Google, agy, antigravity, nano banana), use it directly. If they do NOT name one, ASK which to use before generating. Trigger even when the user does not say GPT, Gemini, or codex - any free-form image-generation request that does not already point at a different tool (Midjourney, Stable Diffusion, DALL-E API, the OpenAI Images API) belongs here. Do NOT use for editing an existing image file, removing backgrounds, or pure HTML/CSS-rendered graphics.

Image Gen Router

Generate a raster image from a text prompt using one of two local, headless, API-key-free backends. Both run under an existing logged-in account (no API key, no per-image API billing) exactly as if the user typed the prompt into that tool themselves:

• GPT — Codex CLI's imagegen tool, under the user's ChatGPT login.
• Gemini — Antigravity agy CLI headless, under the user's Google login.

The whole point of this skill is letting the user pick the model. Everything else (subject, style, aspect, palette, mood) flows straight from the prompt into the chosen backend.

The one decision that matters: which backend

Before generating, you must know which backend to use. There are exactly two cases:

1. The user already named a backend → use it, don't ask. Honor whatever they said and skip straight to generation. Map their words like this:

• GPT, OpenAI, ChatGPT, codex, "the gpt one", DALL·E (as a model family, not the API) → gpt
• Gemini, Google, agy, Antigravity, "nano banana", "the google one" → gemini

Asking again when they've already told you is annoying and wastes a turn — the user was explicit for a reason.

2. The user did NOT name a backend → ask, with no default. Use the AskUserQuestion tool with a single question and exactly two options, presented as equals (neither marked "recommended" — the user didn't express a preference, so don't manufacture one). For example:

AskUserQuestion(questions=[{
  "question": "Which model should generate this image?",
  "header": "Image model",
  "multiSelect": false,
  "options": [
    {"label": "GPT (codex)", "description": "OpenAI image model via Codex CLI, under your ChatGPT login."},
    {"label": "Gemini (agy)", "description": "Google image model via Antigravity CLI, under your Google login."}
  ]
}])

Read the answer, map it to gpt or gemini, and proceed. (If the user picks "Other" and types something, interpret it — e.g. "whatever's faster" → pick either and say which you used.)

Once you know the backend, never re-ask within the same request.

Where to save the file

Ask the user for an output path only if the request is about a deliverable where location obviously matters and they haven't said. Otherwise don't block on it:

• If they gave a path or an obvious destination ("save it to my Desktop"), pass it via --out.
• If they don't care, omit --out — the script reports wherever the file landed.

Running the chosen backend

Each backend has its own script. Both take the same arguments and both print the final location on their last line as IMAGE: <abs-path>. Parse that line to learn where the file is, then show or describe it to the user.

GPT backend:

python3 ~/.claude/skills/image-gen-router/scripts/gen_gpt.py "<prompt>" [--out <path>] [--timeout <sec>]

Gemini backend:

python3 ~/.claude/skills/image-gen-router/scripts/gen_gemini.py "<prompt>" [--out <path>] [--timeout <sec>]

Shared options:

• positional prompt — subject + style. Be specific: medium, lighting, palette, composition, aspect ratio. Both models honor detail. Example: "isometric 3D illustration of a coffee shop, warm pastel palette, soft shadows, 16:9".
• --prompt-file <path> — for long prompts, avoids shell-quoting pain.
• --out <path> — destination PNG. Omit to let the backend keep its own file and just report the path.
• --timeout <sec> — default 600. Image gen usually finishes in under a minute, but image models can be slow on a cold start, so don't lower this without reason.

After it runs, parse the IMAGE: line and tell the user the absolute path. When helpful, Read the image to confirm it matches the request before reporting success.

Worked examples

1 — user named the model, gave a path:

"use gemini to make a minimalist line-art mountain logo, black on white, save to ~/Desktop/logo.png"

Backend is explicit (gemini) and path is explicit → no questions, just run:

python3 ~/.claude/skills/image-gen-router/scripts/gen_gemini.py \
  "minimalist line-art mountain logo, single weight stroke, black on white" \
  --out ~/Desktop/logo.png

2 — user did NOT name the model:

"generate an image of a red panda astronaut floating in space, cinematic photorealistic"

No backend named → AskUserQuestion (GPT vs Gemini, no default) → run the chosen script with the prompt. Don't ask about the path; they didn't mention one and it's a casual request, so omit --out and report where it landed.

3 — long prompt, model named, no path:

"with gpt, render this scene:" (followed by a long paragraph)

Write the paragraph to a temp file and use --prompt-file:

python3 ~/.claude/skills/image-gen-router/scripts/gen_gpt.py \
  --prompt-file /tmp/scene_prompt.txt

Failure handling

Both scripts exit non-zero and explain themselves on stderr:

• exit 2 — the backend's CLI is missing or not authenticated. For GPT, that's codex login status; for Gemini, check agy --version and that the user is logged into Antigravity. Tell the user exactly which login to fix.
• exit 3 — the CLI ran but produced no image, or timed out. Usually a transient backend hiccup or a model that lacks image gen. Re-run once; if it persists, report the stderr the script printed.

Don't silently fall back to the other backend or to a billed image API — the user chose a backend (or you asked them to), and switching it without saying so hides what actually happened. If the chosen backend genuinely can't produce an image, surface the failure and let the user decide whether to try the other one.