zenmux-image-generation

Turn the user's visual intent into an optimized prompt, confirm the prompt and parameters, then generate or edit images through ZenMux and report the saved files.

Defaults when the user does not specify them:

• Model: openai/gpt-image-2
• Count: 4
• Size: 1024x1024
• Quality: medium
• Format: png
• Output directory: skills/zenmux-image-generation/output/

Prompt history belongs to the current project, not this skill. Save it under .context/prompts/zenmux-image-generation/ relative to the project root.

1. Prepare the runtime and refresh sources

Require Node.js 22 or newer. Install locked dependencies once per clone or after dependency changes:

npm ci --prefix skills/zenmux-image-generation

Generation requires ZENMUXAPIKEY. Never accept the key as a CLI argument or print it:

export ZENMUX_API_KEY=...

At the beginning of every invocation, refresh the bundled documentation, prompt cookbooks, and offline model snapshot:

bash skills/zenmux-image-generation/scripts/refresh_references.sh --quiet

If the user named a model, preserve it. Otherwise, list current image models unless the simple default clearly applies:

bash skills/zenmux-image-generation/scripts/list_models.sh
# Machine-readable alternatives:
bash skills/zenmux-image-generation/scripts/list_models.sh --names-only
bash skills/zenmux-image-generation/scripts/list_models.sh --json

The model command queries ZenMux live first and falls back to references/zenmux-image-models.json when offline. Never invent a model ID from memory.

Use sources in this order:

1. references/zenmux-openai-image-generation.md for ZenMux OpenAI Images

protocol and TypeScript examples.

1. references/zenmux-create-image-edit.md for edit fields, masks, limits, and

request shapes.

1. references/openai-typescript-images-generate.md or

references/openai-typescript-images-edit.md for the current OpenAI SDK signature.

1. references/zenmux-image-generation.md for the Gemini/Vertex-compatible

protocol.

1. references/zenmux-generate-images.md for the complete Vertex AI

generateImages / editImage parameter map.

1. references/google-gemini-image-generation.md for Google's latest native

Interactions API and the explicit ZenMux compatibility boundary.

1. The awesome-* files for prompt inspiration only, never API truth.

2. Resolve intent

Extract the following. Ask only for missing information that materially changes the result, with at most three focused questions.

Aspect shortcuts:

• Portrait, 竖版, phone wallpaper, story: 1024x1536
• Landscape, 横版, banner, widescreen: 1536x1024
• Square, 方形, logo, icon, social post: 1024x1024
• 4K for gpt-image-2: 3840x2160
• 2K/QHD for gpt-image-2: 2560x1440

Capture every reference path or URL in user-supplied order. Accept local paths, file://, http(s)://, and base64 image data URLs. Number references as [Image #1], [Image #2], and so on in both prompt metadata and prompt text. The flag order must match this numbering.

3. Choose the current model and protocol

The live model catalog is authoritative. As of the bundled snapshot, useful choices include:

Route by protocol:

• openai/gpt-image-, bare gpt-image-, and chatgpt-image-latest use

scripts/generate-openai.ts and https://zenmux.ai/api/v1 by default.

• Every other model uses scripts/generate-gemini.ts and

https://zenmux.ai/api/vertex-ai.

• If the user explicitly requests Gemini protocol for an OpenAI model, honor it

with generate-gemini.ts.

Google’s direct Gemini API now documents client.interactions.create as its native image-generation surface. ZenMux has not documented that route. Through ZenMux, continue using generateContent for Google Gemini image models and generateImages / editImage for all other image models. Do not send an Interactions API payload to ZenMux until ZenMux explicitly announces support. The current @google/genai SDK warns that generateImages is deprecated and will be removed in its next major release no earlier than 2027-01-01. Treat the method as a ZenMux compatibility bridge and re-check ZenMux docs before a major SDK upgrade.

For gpt-image-2, custom WIDTHxHEIGHT values must have both edges divisible by 16, an aspect ratio between 1:3 and 3:1, and 655,360-8,294,400 total pixels. Resolutions above 2560x1440 are experimental; maximum is 3840x2160. Other OpenAI image models should use auto, 1024x1024, 1536x1024, or 1024x1536.

4. Follow OpenAI Create/Edit best practices

Use the Images API for one-shot generation or editing. Use Responses API only when the user's actual product needs conversational, multi-turn image state; the bundled helper intentionally uses Images API.

For generation:

• Call client.images.generate through the TypeScript SDK.
• GPT Image returns b64_json; decode and save the bytes without transcoding.
• Use n for multiple outputs in one request when the model permits it.
• transparent backgrounds require PNG or WebP.
• gpt-image-2 transparent PNG/WebP output is currently a preview capability.

Request it with background=transparent; merely asking for PNG does not create an alpha channel.

• output_compression applies only to JPEG or WebP.

For editing:

• Call client.images.edit and convert local/remote/data-URL bytes with the

SDK's toFile helper.

• Supply at most 16 input images and preserve their order.
• gpt-image-2 always processes image inputs at high fidelity, so omit

inputfidelity. Use inputfidelity=high only for earlier supported GPT Image models when identity preservation matters.

• A mask applies to the first input. It must be PNG, under 4 MB, and match the

first image dimensions. Fully transparent mask areas indicate what to edit.

• State the requested change first, then explicitly list everything that must

remain invariant.

The scripts accept URL output as a defensive fallback, verify returned image magic bytes, use timeouts/retries, and never overwrite an existing file.

5. Optimize and save the prompt

Search only the relevant cookbook entries; do not read either cookbook end to end:

rg -n '^### No\..*(poster|portrait|product|infographic)' \
  skills/zenmux-image-generation/references/awesome-gpt-image-2.md

Use awesome-gpt-image-2.md for OpenAI-oriented examples and awesome-nano-banana-pro-prompts.md for Gemini-oriented examples. Adapt their structure, not their subject.

Prompt order:

1. Scene/background and composition
2. Main subject and action
3. Materials, lighting, palette, lens/render style
4. Exact quoted copy, when required
5. Edit relationships between [Image #N] inputs
6. Constraints and invariants

For an edit, write surgical instructions such as: "Change only X. Preserve identity, pose, geometry, camera angle, lighting, framing, background, and all unmentioned details." Repeat invariants on every follow-up edit.

Save the prompt as:

.context/prompts/zenmux-image-generation/<YYYYMMDD-HHMMSS>-<short-slug>.md

Use this format:

# Optimized prompt — <summary>

- **Model:** openai/gpt-image-2
- **Size:** 1024x1536
- **Quality:** medium
- **Count:** 4
- **Output format:** png
- **Filename prefix:** launch-poster
- **References:** none
- **Created:** 2026-08-31 14:30 (Asia/Singapore)

---

<optimized prompt body>

Show the optimized prompt and parameters to the user. Do not call the paid generation API until the user confirms. Edit the same prompt file if they ask for small changes.

6. Generate with TypeScript

OpenAI generation:

npm --prefix skills/zenmux-image-generation run generate:openai -- \
  --model "openai/gpt-image-2" \
  --prompt-file ".context/prompts/zenmux-image-generation/<file>.md" \
  --filename-prefix "launch-poster" \
  --n 4 --size "1024x1536" --quality "medium" --output-format "png"

OpenAI edit with ordered references:

npm --prefix skills/zenmux-image-generation run generate:openai -- \
  --model "openai/gpt-image-2" \
  --prompt-file ".context/prompts/zenmux-image-generation/<file>.md" \
  --filename-prefix "outfit-edit" \
  --n 4 --size "1024x1536" --quality "high" \
  --reference-image "/absolute/path/person.png" \
  --reference-image "https://example.com/jacket.webp"

Add --mask-image "/absolute/path/mask.png" for a masked edit. For an earlier GPT Image model, add --input-fidelity high when supported. Add --background transparent --output-format png for a lossless transparent asset, or use WebP plus --compression 85 for a smaller transparent asset.

Gemini/Vertex-compatible model:

npm --prefix skills/zenmux-image-generation run generate:gemini -- \
  --model "google/gemini-3.1-flash-image" \
  --prompt-file ".context/prompts/zenmux-image-generation/<file>.md" \
  --filename-prefix "campaign-poster" \
  --n 4 --aspect-ratio "2:3" --image-size "1K"

The TypeScript Gemini helper covers both ZenMux protocol shapes:

• Google Gemini image models use generateContent or --stream for

generateContentStream. It always requests TEXT and IMAGE, supports up to 14 ordered references, and uses --aspect-ratio / --image-size to build imageConfig.

• Non-Google models use generateImages, or editImage when references are

present. Supported CLI mappings include --negative-prompt, --aspect-ratio, --image-size, --seed, --enhance-prompt, --person-generation, --safety-filter-level, --include-rai-reason, --add-watermark, and --guidance-scale.

• --size and --quality are OpenAI passthrough fields when an OpenAI model

is deliberately called through Gemini protocol. --image-size becomes sampleImageSize for other generateImages providers where supported.

• ZenMux Vertex protocol does not map GPT Image's background field. Use

generate-openai.ts for transparent GPT Image output.

• Provider defaults vary when --aspect-ratio and --image-size are omitted.

Some providers may also return a different MIME type than requested; the helper detects PNG/JPEG/WebP magic bytes and saves the matching extension.

--output-dir is optional. Pass it only when the user requests a different location.

7. Object-storage-safe filenames

Every saved image uses this deterministic shape:

<semantic-prefix>-<model-slug>-<utc-millisecond-timestamp>-<8-hex-run-id>-<index>.<ext>

Example:

launch-poster-openai-gpt-image-2-20260831t143012345z-a1b2c3d4-01.png

This is intentionally conservative for Supabase Storage, S3-compatible APIs, CDNs, and signed URLs:

• Lowercase ASCII only in the stem: a-z, 0-9, and -
• One final . before png, jpg, or webp
• No spaces, Unicode, slashes, backslashes, control characters, URL-reserved

punctuation, leading dots, or .. segments

• UTC millisecond timestamp for lexical ordering
• Random run ID plus output index to prevent collisions during concurrent runs
• Exclusive file creation so a collision cannot overwrite an existing object

Always pass a concise semantic --filename-prefix. If omitted, the script derives it from the prompt filename and sanitizes it with the same rules.

8. Report and iterate

On success, the scripts print SUCCESS, OUTPUTDIR, IMAGEPATHS, and a single-line RESULTJSON. Parse RESULTJSON when another tool needs stable machine-readable output. Return clickable absolute paths to the user.

For follow-up edits, preserve the selected image as [Image #1], save a new prompt that restates invariants, confirm it, and run the appropriate edit path.

If generation fails:

• Missing dependency: run npm ci --prefix skills/zenmux-image-generation.
• Model unavailable: rerun list_models.sh; use the exact live ID.
• Invalid size: choose the nearest valid dimensions that preserve aspect ratio.
• Reference rejected: convert it to PNG/JPEG/WebP and keep it under the model's

input limit.

• Provider rejects multiple outputs: loop calls with --n 1 and retain the

same semantic prefix; each run ID keeps filenames unique.