baoyu-image-gen

Multi-provider AI image generation with text-to-image, reference images, batch processing, and quality presets.

jimliu/baoyu-skills|Updated Jun 2, 2026

Works with

Claude CodeCursorClineWindsurfCodexGoose

What it does

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

Repository

jimliu/baoyu-skills

Last updated

Jun 2, 2026

Installation Guide

Select your AI agent

How to use baoyu-image-gen on Cursor

AI-first code editor with Composer

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

›Cursor installed and configured on your machine
›Node.js 16+ with npm — verify with node --version
›Active project directory where you want to add baoyu-image-gen

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/jimliu/baoyu-skills --skill baoyu-image-gen

Fetches baoyu-image-gen from jimliu/baoyu-skills and configures it for Cursor.

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?

│

│ ── Universal (.agents/skills) ────────────────

│ · Cline · Codex · Goose · Windsurf

│ ●Cursor(selected)

│ · Cursor · Aider · Continue

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/baoyu-image-gen

Restart Cursor to activate baoyu-image-gen. Access via /baoyu-image-gen in your agent's command palette.

⚠

Security Notice

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.

›View source on GitHub ›Skills CLI docs ›About Cursor ›What are agent skills?

Documentation

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Use Cases

User Story & Requirements Generation

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

Competitive Analysis

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

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Step 0: Load Preferences ⛔ BLOCKING

CRITICAL: 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

Schema: references/config/preferences-schema.md

Usage

# 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

Batch File Format

{ "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.

Options

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)

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

Environment Variables

Variable

Description

OPENAI_API_KEY

OpenAI API key

AZURE_OPENAI_API_KEY

baoyu-image-gen

What it does

Category

Repository

Last updated

Installation Guide

How to use baoyu-image-gen on Cursor

Prerequisites

Run the install command

Select Cursor when prompted

Verify installation

Security Notice

Documentation

List & Monetize Your Skill

Use Cases

User Story & Requirements Generation

Competitive Analysis

Roadmap Prioritization

Install Skill

Image Generation (AI SDK)

Script Directory

Step 0: Load Preferences ⛔ BLOCKING

Usage

Batch File Format

Options

Environment Variables

Stakeholder Communication

Implementation Guide

Best Practices

When to Use This

Learning Path

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Reviews

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