/baoyu-imagine

name: baoyu-imagine
description: AI image generation with OpenAI GPT Image 2, Azure OpenAI, Google, OpenRouter, DashScope, Z.AI GLM-Image, MiniMax, Jimeng, Seedream and Replicate APIs. Supports text-to-image, reference images, aspect ratios, and batch generation from saved prompt files. Sequential by default; use batch parallel generation when the user already has multiple prompts or wants stable multi-image throughput. Use when user asks to generate, create, or draw images.
version: 1.58.0
metadata:
  openclaw:
    homepage: https://github.com/JimLiu/baoyu-skills#baoyu-imagine
    requires:
      anyBins:
        - bun
        - npx

Image Generation (AI SDK)

Official API-based image generation. Supports OpenAI GPT Image 2, Azure OpenAI, Google, OpenRouter, DashScope (阿里通义万象), Z.AI GLM-Image, MiniMax, Jimeng (即梦), Seedream (豆包) and Replicate.

User Input Tools

When this skill prompts the user, follow this tool-selection rule (priority order):

1. **Prefer built-in user-input tools** exposed by the current agent runtime — e.g., `AskUserQuestion`, `request_user_input`, `clarify`, `ask_user`, or any equivalent. 2. **Fallback**: if no such tool exists, emit a numbered plain-text message and ask the user to reply with the chosen number/answer for each question. 3. **Batching**: if the tool supports multiple questions per call, combine all applicable questions into a single call; if only single-question, ask them one at a time in priority order.

Concrete `AskUserQuestion` references below are examples — substitute the local equivalent in other runtimes.

Script Directory

`{baseDir}` = this SKILL.md's directory. Main script: `{baseDir}/scripts/main.ts`. Resolve `${BUN_X}`: prefer `bun`; else `npx -y bun`; else suggest `brew install oven-sh/bun/bun`.

Step 0: Load Preferences ⛔ BLOCKING

This step MUST complete before any image generation — generation is blocked until EXTEND.md exists.

Check these paths in order; first hit wins:

| Path | Scope | |------|-------| | `.baoyu-skills/baoyu-imagine/EXTEND.md` | Project | | `${XDG_CONFIG_HOME:-$HOME/.config}/baoyu-skills/baoyu-imagine/EXTEND.md` | XDG | | `$HOME/.baoyu-skills/baoyu-imagine/EXTEND.md` | User home |

• **Found** → load, parse, apply. If `default_model.[provider]` is null → ask model only.
• **Not found** → run first-time setup (`references/config/first-time-setup.md`) using AskUserQuestion to collect provider + model + quality + save location. Save EXTEND.md, then continue. Do not generate images before this completes.

Legacy compatibility: if `.baoyu-skills/baoyu-image-gen/EXTEND.md` exists and the new path doesn't, the runtime renames it to `baoyu-imagine`. If both exist, the runtime leaves them alone and uses the new path.

**EXTEND.md keys**: default provider, default quality, default aspect ratio, default image size, OpenAI image API dialect, default models, batch worker cap, provider-specific batch limits. Schema: `references/config/preferences-schema.md`.

Usage

Minimum working examples — see `references/usage-examples.md` for the full set including per-provider invocations and batch mode.

Identity-preserving reference prompts

When the user wants a real person/character/object preserved from reference images, do **not** replace the reference with a long generic description. Prefer short, hard identity-preservation language:

• "Use the person/object in the reference image(s) as the same identity. Do not redesign it or create a similar-looking new subject."
• "Only change scene, clothing, pose, lighting, rendering style, and composition. Keep the face/proportions/hair/key accessories/overall identity from the references."
• If using multiple references, state that they are the same subject and should jointly define identity.

Pitfall: long descriptions like "young East Asian woman, oval face, clear eyes..." can cause the model to synthesize a new person matching the description instead of preserving the referenced person.

# Basic
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image cat.png

# With aspect ratio and high quality
${BUN_X} {baseDir}/scripts/main.ts --prompt "A landscape" --image out.png --ar 16:9 --quality 2k

# Prompt from files
${BUN_X} {baseDir}/scripts/main.ts --promptfiles system.md content.md --image out.png

# With reference image
${BUN_X} {baseDir}/scripts/main.ts --prompt "Make blue" --image out.png --ref source.png

# Specific provider
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider dashscope --model qwen-image-2.0-pro

# OpenAI GPT Image 2
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider openai --model gpt-image-2

# Batch mode
${BUN_X} {baseDir}/scripts/main.ts --batchfile batch.json --jobs 4

Reference-Image Identity Preservation

When the user wants a person/object preserved from reference images:

• Prefer a small curated set of existing source references (usually 2–4) over many images; large multi-megabyte refs can destabilize streaming providers.
• Make the prompt say the references are the same subject and the output must use that identity. Avoid long generic facial-feature descriptions that can cause the model to synthesize a new similar-looking person.
• Do not use newly generated outputs as references unless the user explicitly asks; generated refs compound drift.
• If results become too polished or influencer-like, reduce stylized refs and add explicit anti-beautification constraints (no face slimming, eye enlargement, heavy makeup, commercial travel shoot, over-smoothing).
• If the subject should look younger/older, preserve the face and express age through clothing, posture, scene, and styling; do not ask the model to change facial identity.

Options

| Option | Description | |--------|-------------| | `--prompt <text>`, `-p` | Prompt text | | `--promptfiles <files...>` | Read prompt from files (concatenated) | | `--image <path>` | Output image path (required in single-imag