Multi-provider AI image generation with text-to-image, reference images, batch processing, and quality presets.
Works with
Supports seven providers: OpenAI, Google, OpenRouter, DashScope, Jimeng, Seedream, and Replicate with automatic provider selection or explicit override
Handles single images via CLI flags or batch parallel generation from JSON files with configurable worker counts and per-provider throttling
Supports reference images (Google, OpenAI, OpenRouter, Replicate, Seedream), custom
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionbaoyu-image-genExecute the skills CLI command in your project's root directory to begin installation:
Fetches baoyu-image-gen from jimliu/baoyu-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate baoyu-image-gen. Access via /baoyu-image-gen in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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⚠️ DEPRECATED: This skill has been migrated to
baoyu-imagine. Print a bold red warning to the user in the user's language (use ANSI escape\033[1;31m) stating thatbaoyu-image-genhas been migrated tobaoyu-imagineand they should usebaoyu-imagineinstead. Then STOP — do NOT continue executing this skill.
Official API-based image generation. Supports OpenAI, Azure OpenAI, Google, OpenRouter, DashScope (阿里通义万象), MiniMax, Jimeng (即梦), Seedream (豆包) and Replicate providers.
Agent Execution:
{baseDir} = this SKILL.md file's directory{baseDir}/scripts/main.ts${BUN_X} runtime: if bun installed → bun; if npx available → npx -y bun; else suggest installing bunCRITICAL: This step MUST complete BEFORE any image generation. Do NOT skip or defer.
Check EXTEND.md existence (priority: project → user):
# macOS, Linux, WSL, Git Bash
test -f .baoyu-skills/baoyu-image-gen/EXTEND.md && echo "project"
test -f "${XDG_CONFIG_HOME:-$HOME/.config}/baoyu-skills/baoyu-image-gen/EXTEND.md" && echo "xdg"
test -f "$HOME/.baoyu-skills/baoyu-image-gen/EXTEND.md" && echo "user"
# PowerShell (Windows)
if (Test-Path .baoyu-skills/baoyu-image-gen/EXTEND.md) { "project" }
$xdg = if ($env:XDG_CONFIG_HOME) { $env:XDG_CONFIG_HOME } else { "$HOME/.config" }
if (Test-Path "$xdg/baoyu-skills/baoyu-image-gen/EXTEND.md") { "xdg" }
if (Test-Path "$HOME/.baoyu-skills/baoyu-image-gen/EXTEND.md") { "user" }
| Result | Action |
|---|---|
| Found | Load, parse, apply settings. If default_model.[provider] is null → ask model only (Flow 2) |
| Not found | ⛔ Run first-time setup (references/config/first-time-setup.md) → Save EXTEND.md → Then continue |
CRITICAL: If not found, complete the full setup (provider + model + quality + save location) using AskUserQuestion BEFORE generating any images. Generation is BLOCKED until EXTEND.md is created.
| Path | Location |
|---|---|
.baoyu-skills/baoyu-image-gen/EXTEND.md |
Project directory |
$HOME/.baoyu-skills/baoyu-image-gen/EXTEND.md |
User home |
EXTEND.md Supports: Default provider | Default quality | Default aspect ratio | Default image size | Default models | Batch worker cap | Provider-specific batch limits
Schema: references/config/preferences-schema.md
# Basic
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image cat.png
# With aspect ratio
${BUN_X} {baseDir}/scripts/main.ts --prompt "A landscape" --image out.png --ar 16:9
# High quality
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --quality 2k
# From prompt files
${BUN_X} {baseDir}/scripts/main.ts --promptfiles system.md content.md --image out.png
# With reference images (Google, OpenAI, Azure OpenAI, OpenRouter, Replicate, MiniMax, or Seedream 4.0/4.5/5.0)
${BUN_X} {baseDir}/scripts/main.ts --prompt "Make blue" --image out.png --ref source.png
# With reference images (explicit provider/model)
${BUN_X} {baseDir}/scripts/main.ts --prompt "Make blue" --image out.png --provider google --model gemini-3-pro-image-preview --ref source.png
# Azure OpenAI (model means deployment name)
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider azure --model gpt-image-1.5
# OpenRouter (recommended default model)
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider openrouter
# OpenRouter with reference images
${BUN_X} {baseDir}/scripts/main.ts --prompt "Make blue" --image out.png --provider openrouter --model google/gemini-3.1-flash-image-preview --ref source.png
# Specific provider
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider openai
# DashScope (阿里通义万象)
${BUN_X} {baseDir}/scripts/main.ts --prompt "一只可爱的猫" --image out.png --provider dashscope
# DashScope Qwen-Image 2.0 Pro (recommended for custom sizes and text rendering)
${BUN_X} {baseDir}/scripts/main.ts --prompt "为咖啡品牌设计一张 21:9 横幅海报，包含清晰中文标题" --image out.png --provider dashscope --model qwen-image-2.0-pro --size 2048x872
# DashScope legacy Qwen fixed-size model
${BUN_X} {baseDir}/scripts/main.ts --prompt "一张电影感海报" --image out.png --provider dashscope --model qwen-image-max --size 1664x928
# MiniMax
${BUN_X} {baseDir}/scripts/main.ts --prompt "A fashion editorial portrait by a bright studio window" --image out.jpg --provider minimax
# MiniMax with subject reference (best for character/portrait consistency)
${BUN_X} {baseDir}/scripts/main.ts --prompt "A girl stands by the library window, cinematic lighting" --image out.jpg --provider minimax --model image-01 --ref portrait.png --ar 16:9
# MiniMax with custom size (documented for image-01)
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cinematic poster" --image out.jpg --provider minimax --model image-01 --size 1536x1024
# Replicate (google/nano-banana-pro)
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider replicate
# Replicate with specific model
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider replicate --model google/nano-banana
# Batch mode with saved prompt files
${BUN_X} {baseDir}/scripts/main.ts --batchfile batch.json
# Batch mode with explicit worker count
${BUN_X} {baseDir}/scripts/main.ts --batchfile batch.json --jobs 4 --json
{
"jobs": 4,
"tasks": [
{
"id": "hero",
"promptFiles": ["prompts/hero.md"],
"image": "out/hero.png",
"provider": "replicate",
"model": "google/nano-banana-pro",
"ar": "16:9",
"quality": "2k"
},
{
"id": "diagram",
"promptFiles": ["prompts/diagram.md"],
"image": "out/diagram.png",
"ref": ["references/original.png"]
}
]
}
Paths in promptFiles, image, and ref are resolved relative to the batch file's directory. jobs is optional (overridden by CLI --jobs). Top-level array format (without jobs wrapper) is also accepted.
| Option | Description |
|---|---|
--prompt <text>, -p |
Prompt text |
--promptfiles <files...> |
Read prompt from files (concatenated) |
--image <path> |
Output image path (required in single-image mode) |
--batchfile <path> |
JSON batch file for multi-image generation |
--jobs <count> |
Worker count for batch mode (default: auto, max from config, built-in default 10) |
--provider google|openai|azure|openrouter|dashscope|minimax|jimeng|seedream|replicate |
Force provider (default: auto-detect) |
--model <id>, -m |
Model ID (Google: gemini-3-pro-image-preview; OpenAI: gpt-image-1.5; Azure: deployment name such as gpt-image-1.5 or image-prod; OpenRouter: google/gemini-3.1-flash-image-preview; DashScope: qwen-image-2.0-pro; MiniMax: image-01) |
--ar <ratio> |
Aspect ratio (e.g., 16:9, 1:1, 4:3) |
--size <WxH> |
Size (e.g., 1024x1024) |
--quality normal|2k |
Quality preset (default: 2k) |
--imageSize 1K|2K|4K |
Image size for Google/OpenRouter (default: from quality) |
--ref <files...> |
Reference images. Supported by Google multimodal, OpenAI GPT Image edits, Azure OpenAI edits (PNG/JPG only), OpenRouter multimodal models, Replicate, MiniMax subject-reference, and Seedream 5.0/4.5/4.0. Not supported by Jimeng, Seedream 3.0, or removed SeedEdit 3.0 |
--n <count> |
Number of images |
--json |
JSON output |
| Variable | Description |
|---|---|
OPENAI_API_KEY |
OpenAI API key |
AZURE_OPENAI_API_KEY✓ Make data-driven prioritization decisions faster Stakeholder CommunicationDraft PRDs, status updates, and stakeholder presentations Example Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement ✓ Save 3-5 hours/week on communication overhead Implementation GuidePrerequisites
Time Estimate 30-60 minutes to see productivity improvements Steps
Common Pitfalls
Best Practices✓ Do
✗ Don't
💡 Pro Tips
When to Use This✓ Use when Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work. ✗ Avoid when Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed. Learning Path
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