How do I install the Baoyu Image Gen skill?

Run npx skillmds@latest add ulpi-io/baoyu-image-gen in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Baoyu Image Gen skill do?

AI image generation with OpenAI, Google, DashScope 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. It is listed under AI & ML on SkillMD.

Is Baoyu Image Gen safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Baoyu Image Gen?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Baoyu Image Gen free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Baoyu Image Gen?

ulpi-io (@ulpi-io) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Baoyu Image Gen by ulpi-io — Agent Skill

Name: Baoyu Image Gen
Author: ulpi-io

Image Generation (AI SDK)

Official API-based image generation. Supports OpenAI, Google, DashScope (阿里通义万象) and Replicate providers.

Script Directory

Agent Execution:

{baseDir} = this SKILL.md file's directory
Script path = {baseDir}/scripts/main.ts
Resolve ${BUN_X} runtime: if bun installed → bun; if npx available → npx -y bun; else suggest installing bun

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 multimodal or OpenAI edits)
${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

# 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

# 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)
`--provider google\|openai\|dashscope\|replicate`	Force provider (default: auto-detect)
`--model <id>`, `-m`	Model ID (Google: `gemini-3-pro-image-preview`, `gemini-3.1-flash-image-preview`; OpenAI: `gpt-image-1.5`, `gpt-image-1`)
`--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 (default: from quality)
`--ref <files...>`	Reference images. Supported by Google multimodal, OpenAI GPT Image edits, and Replicate
`--n <count>`	Number of images
`--json`	JSON output

Environment Variables

Variable	Description
`OPENAI_API_KEY`	OpenAI API key
`GOOGLE_API_KEY`	Google API key
`DASHSCOPE_API_KEY`	DashScope API key (阿里云)
`REPLICATE_API_TOKEN`	Replicate API token
`OPENAI_IMAGE_MODEL`	OpenAI model override
`GOOGLE_IMAGE_MODEL`	Google model override
`DASHSCOPE_IMAGE_MODEL`	DashScope model override (default: z-image-turbo)
`REPLICATE_IMAGE_MODEL`	Replicate model override (default: google/nano-banana-pro)
`OPENAI_BASE_URL`	Custom OpenAI endpoint
`GOOGLE_BASE_URL`	Custom Google endpoint
`DASHSCOPE_BASE_URL`	Custom DashScope endpoint
`REPLICATE_BASE_URL`	Custom Replicate endpoint
`BAOYU_IMAGE_GEN_MAX_WORKERS`	Override batch worker cap
`BAOYU_IMAGE_GEN_<PROVIDER>_CONCURRENCY`	Override provider concurrency, e.g. `BAOYU_IMAGE_GEN_REPLICATE_CONCURRENCY`
`BAOYU_IMAGE_GEN_<PROVIDER>_START_INTERVAL_MS`	Override provider start gap, e.g. `BAOYU_IMAGE_GEN_REPLICATE_START_INTERVAL_MS`

Load Priority: CLI args > EXTEND.md > env vars > <cwd>/.baoyu-skills/.env > ~/.baoyu-skills/.env

Model Resolution

Model priority (highest → lowest), applies to all providers:

CLI flag: --model <id>
EXTEND.md: default_model.[provider]
Env var: <PROVIDER>_IMAGE_MODEL (e.g., GOOGLE_IMAGE_MODEL)
Built-in default

EXTEND.md overrides env vars. If both EXTEND.md default_model.google: "gemini-3-pro-image-preview" and env var GOOGLE_IMAGE_MODEL=gemini-3.1-flash-image-preview exist, EXTEND.md wins.

Agent MUST display model info before each generation:

Show: Using [provider] / [model]
Show switch hint: Switch model: --model <id> | EXTEND.md default_model.[provider] | env <PROVIDER>_IMAGE_MODEL

Replicate Models

Supported model formats:

owner/name (recommended for official models), e.g. google/nano-banana-pro
owner/name:version (community models by version), e.g. stability-ai/sdxl:<version>

Examples:

# Use Replicate default model
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider replicate

# Override model explicitly
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider replicate --model google/nano-banana

Provider Selection

--ref provided + no --provider → auto-select Google first, then OpenAI, then Replicate
--provider specified → use it (if --ref, must be google, openai, or replicate)
Only one API key available → use that provider
Multiple available → default to Google

Quality Presets

Preset	Google imageSize	OpenAI Size	Replicate resolution	Use Case
`normal`	1K	1024px	1K	Quick previews
`2k` (default)	2K	2048px	2K	Covers, illustrations, infographics

Google imageSize: Can be overridden with --imageSize 1K|2K|4K

Aspect Ratios

Supported: 1:1, 16:9, 9:16, 4:3, 3:4, 2.35:1

Google multimodal: uses imageConfig.aspectRatio
OpenAI: maps to closest supported size
Replicate: passes aspect_ratio to model; when --ref is provided without --ar, defaults to match_input_image

Generation Mode

Default: Sequential generation.

Batch Parallel Generation: When --batchfile contains 2 or more pending tasks, the script automatically enables parallel generation.

Mode	When to Use
Sequential (default)	Normal usage, single images, small batches
Parallel batch	Batch mode with 2+ tasks

Execution choice:

Situation	Preferred approach	Why
One image, or 1-2 simple images	Sequential	Lower coordination overhead and easier debugging
Multiple images already have saved prompt files	Batch (`--batchfile`)	Reuses finalized prompts, applies shared throttling/retries, and gives predictable throughput
Each image still needs separate reasoning, prompt writing, or style exploration	Subagents	The work is still exploratory, so each image may need independent analysis before generation
Output comes from `baoyu-article-illustrator` with `outline.md` + `prompts/`	Batch (`build-batch.ts` -> `--batchfile`)	That workflow already produces prompt files, so direct batch execution is the intended path

Rule of thumb:

Prefer batch over subagents once prompt files are already saved and the task is "generate all of these"
Use subagents only when generation is coupled with per-image thinking, rewriting, or divergent creative exploration

Parallel behavior:

Default worker count is automatic, capped by config, built-in default 10
Provider-specific throttling is applied only in batch mode, and the built-in defaults are tuned for faster throughput while still avoiding obvious RPM bursts
You can override worker count with --jobs <count>
Each image retries automatically up to 3 attempts
Final output includes success count, failure count, and per-image failure reasons

Error Handling

Missing API key → error with setup instructions
Generation failure → auto-retry up to 3 attempts per image
Invalid aspect ratio → warning, proceed with default
Reference images with unsupported provider/model → error with fix hint

Extension Support

Custom configurations via EXTEND.md. See Preferences section for paths and supported options.

Baoyu Image Gen

File contents

Image Generation (AI SDK)

Script Directory

Step 0: Load Preferences ⛔ BLOCKING

Usage

Batch File Format

Options

Environment Variables

Model Resolution

Replicate Models

Provider Selection

Quality Presets

Aspect Ratios

Generation Mode

Error Handling

Extension Support

Frequently asked questions

Baoyu Image Gen

File contents

Image Generation (AI SDK)

Script Directory

Step 0: Load Preferences ⛔ BLOCKING

Usage

Batch File Format

Options

Environment Variables

Model Resolution

Replicate Models

Provider Selection

Quality Presets

Aspect Ratios

Generation Mode

Error Handling

Extension Support

Related skills

Frequently asked questions

How do I install the Baoyu Image Gen skill?

What does the Baoyu Image Gen skill do?

Is Baoyu Image Gen safe to use?

Which AI agents work with Baoyu Image Gen?

Is Baoyu Image Gen free to use?

Who published Baoyu Image Gen?