AI image generation with OpenAI GPT Image 2.5, Azure OpenAI, Google, OpenRouter, DashScope, Z.AI GLM-Image, MiniMax, Jimeng, Seedream, Replicate and Agnes 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.
name: baoyu-image-gen
description: AI image generation with OpenAI GPT Image 2.5, Azure OpenAI, Google, OpenRouter, DashScope, Z.AI GLM-Image, MiniMax, Jimeng, Seedream, Replicate and Agnes 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: 2.2.0
metadata:
openclaw:
homepage: https://github.com/JimLiu/baoyu-skills#baoyu-image-gen
requires:
anyBins:
- bun
- npx
When this skill prompts the user, follow this tool-selection rule (priority order):
Prefer built-in user-input tools exposed by the current agent runtime — e.g., AskUserQuestion, request_user_input, clarify, ask_user, or any equivalent.
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.
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. All scripts/... paths below are relative to {baseDir}. Main script: {baseDir}/scripts/main.ts. Batch payload helper: {baseDir}/scripts/build-batch.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.
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-imagine/EXTEND.md exists and the new path doesn't, the runtime renames it to baoyu-image-gen. If both exist, the runtime leaves them alone and uses the new path.
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-2.5-flare
# Codex CLI (uses logged-in Codex subscription — no OPENAI_API_KEY required; requires `codex` on PATH)
${BUN_X} {baseDir}/scripts/main.ts --prompt "A cat" --image out.png --provider codex-cli --ar 16:9
# Batch mode
${BUN_X} {baseDir}/scripts/main.ts --batchfile batch.json --jobs 4
# Build a batch file from outline.md + prompts/ (e.g. baoyu-article-illustrator output)
${BUN_X} {baseDir}/scripts/build-batch.ts --outline outline.md --prompts prompts --output batch.json --images-dir attachments
${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-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; codex-cli is never auto-selected — must be pinned via CLI or EXTEND.md)
--model <id>, -m
Model ID — see provider references for defaults and allowed values
--ar <ratio>
Aspect ratio (16:9, 1:1, 4:3, …)
--size <WxH>
Explicit size (e.g., 1024x1024; for gpt-image-2.5-* and gpt-image-2, width/height must be multiples of 16, max edge 3840px, ratio no wider than 3:1)
--quality normal|2k
Quality preset (default: 2k)
--imageSize 1K|2K|4K
Image size for Google/OpenRouter (default: from quality)
--imageApiDialect openai-native|ratio-metadata
OpenAI-compatible endpoint dialect — use ratio-metadata for gateways that expect aspect-ratio size plus metadata.resolution
--ref <files...>
Reference images. Supported by Google multimodal, OpenAI GPT Image edits, Azure OpenAI edits (PNG/JPG only), OpenRouter multimodal models, Replicate supported families, MiniMax subject-reference, Seedream 5.0/4.5/4.0, DashScope wan2.7-image-pro/wan2.7-image. Not supported by Jimeng, Seedream 3.0, SeedEdit 3.0, or any DashScope model outside the wan2.7-image* family
--n <count>
Number of images. Replicate requires --n 1 (single-output save semantics)
Azure image API version (default 2025-04-01-preview)
JIMENG_REGION
Jimeng region (default cn-north-1)
OPENAI_IMAGE_API_DIALECT
openai-native | ratio-metadata
OPENROUTER_HTTP_REFERER, OPENROUTER_TITLE
Optional OpenRouter attribution
BAOYU_IMAGE_GEN_MAX_WORKERS
Override batch worker cap
BAOYU_IMAGE_GEN_<PROVIDER>_CONCURRENCY
Per-provider concurrency (e.g., BAOYU_IMAGE_GEN_REPLICATE_CONCURRENCY; for codex-cli use BAOYU_IMAGE_GEN_CODEX_CLI_CONCURRENCY)
BAOYU_IMAGE_GEN_<PROVIDER>_START_INTERVAL_MS
Per-provider start-gap
BAOYU_CODEX_IMAGEGEN_BIN
Override the codex-imagegen wrapper path for the codex-cli provider (default: bundled scripts/codex-imagegen/main.ts; accepts .ts or legacy .sh/binary)
BAOYU_CODEX_IMAGEGEN_CACHE_DIR
Enable idempotency cache for the codex-cli provider (off by default)
BAOYU_CODEX_IMAGEGEN_TIMEOUT_MS
Per-attempt codex exec timeout for the codex-cli provider (default: 300000 ms)
BAOYU_CODEX_IMAGEGEN_RETRIES
Wrapper-side retry attempts on retryable errors for the codex-cli provider (default: 2)
BAOYU_CODEX_IMAGEGEN_LOG_FILE
Append JSONL diagnostic log for the codex-cli provider
--provider openai --model gpt-image-2.5-flare uses the standard OpenAI Images API (/v1/images/generations or /v1/images/edits) and requires OPENAI_API_KEY. A Codex or ChatGPT desktop login is a different entitlement and is not a drop-in replacement for OPENAI_API_KEY; do not paste a Codex OAuth token into OPENAI_API_KEY or only set OPENAI_BASE_URL to a Codex backend.
If the user wants to use their Codex subscription / GPT Image 2 entitlement without an OpenAI API key, route through a Codex-native backend instead of this skill's openai provider:
In Codex runtime: use the native imagegen skill/tool.
In non-Codex runtimes with codex CLI installed and logged in: use baoyu-image-gen --provider codex-cli (preferred — it gives you the same retry / cache / batch flow as every other provider). The provider spawns the bundled scripts/codex-imagegen/main.ts; the same code lives upstream at packages/baoyu-codex-imagegen/src/main.ts for standalone callers.
In Hermes runtimes with a native image_generate tool: use that tool as a fallback, and state whether reference images were passed directly or reconstructed from extracted traits.
Do not modify the existing openai provider to silently consume Codex OAuth. The first-class Codex-CLI path is the dedicated codex-cli provider, which has its own auth (Codex login), route (codex exec), request shape, and tests. See references/codex-oauth-vs-openai-api-key.md.
Model Resolution
Priority (highest → lowest) applies to every provider:
CLI flag --model <id>
EXTEND.md default_model.[provider]
Env var <PROVIDER>_IMAGE_MODEL
Built-in default
For OpenAI, the built-in default is gpt-image-2.5-flare (fast, lowest latency). gpt-image-2.5-sunburst is the most capable variant for complex scenes and precise edits; gpt-image-2, gpt-image-1.5, gpt-image-1, and dated GPT Image snapshots (e.g. gpt-image-2.5-flare-2026-09-08, gpt-image-2-2026-04-21) remain selectable with --model or OPENAI_IMAGE_MODEL.
For Google, the built-in default is gemini-3-pro-image. gemini-3.1-flash-image is the faster low-cost option, and gemini-3.1-flash-lite-image is the cheapest — it only produces 1K output, so --quality 2k / --imageSize 2K|4K is clamped to 1K with a warning.
For DashScope, the built-in default is qwen-image-2.0-pro; qwen-image-3.0-pro is the newest flagship and uses the same sizing rules.
For Azure, --model / default_model.azure is the Azure deployment name. AZURE_OPENAI_DEPLOYMENT is the preferred env var; AZURE_OPENAI_IMAGE_MODEL is kept as a backward-compatible alias. If your Azure deployment is named after the underlying model, use gpt-image-2.5-flare; otherwise use the exact custom deployment name.
EXTEND.md overrides env vars: if EXTEND.md sets default_model.google: "gemini-3-pro-image" and the env var sets GOOGLE_IMAGE_MODEL=gemini-3.1-flash-image, EXTEND.md wins.
provider=openai means the auth and routing entrypoint is OpenAI-compatible. It does not guarantee the upstream image API uses OpenAI native semantics. When a gateway expects a different wire format, set default_image_api_dialect in EXTEND.md, OPENAI_IMAGE_API_DIALECT, or --imageApiDialect:
openai-native: pixel size (1536x1024) and native OpenAI quality fields
ratio-metadata: aspect-ratio size (16:9) plus metadata.resolution (1K|2K|4K) and metadata.orientation
Use openai-native for the OpenAI native API or strict clones; try ratio-metadata for compatibility gateways in front of Gemini or similar models. Current limitation: ratio-metadata applies only to text-to-image; reference-image edits still need openai-native or a provider with first-class edit support.
Provider-Specific Guides
Each provider has its own quirks (model families, size rules, ref support, limits). Read these when the user picks that provider or asks for non-default behavior:
codex-cli is never auto-selected — set default_provider: codex-cli in EXTEND.md or pass --provider codex-cli. It spawns codex exec via the bundled scripts/codex-imagegen/main.ts TS entrypoint (run with bun) and uses the user's Codex subscription (no OPENAI_API_KEY). Requires codex on PATH with an active codex login.
Quality Presets
Preset
Google imageSize
OpenAI size
OpenRouter size
Replicate resolution
Use case
normal
1K
1024px target
1K
1K
Quick previews
2k (default)
2K
2048px target
2K
2K
Covers, illustrations, infographics
Google/OpenRouter imageSize can be overridden with --imageSize 1K|2K|4K.
For OpenAI native gpt-image-2.5-* and gpt-image-2, normal maps to quality=medium and a low-latency valid size near the requested aspect ratio; 2k maps to quality=high and 2048px-class sizes such as 2048x2048, 2048x1152, or 1152x2048. Use explicit --size for valid custom or 4K outputs, e.g. 3840x2160.
Aspect Ratios
Supported: 1:1, 16:9, 9:16, 4:3, 3:4, 2.35:1.
Google multimodal: imageConfig.aspectRatio
OpenAI: gpt-image-2.5-* and gpt-image-2 use the closest valid custom size for the requested ratio; older GPT Image and DALL·E models use their closest supported fixed size
OpenRouter: imageGenerationOptions.aspect_ratio; if only --size <WxH> is given, the ratio is inferred
Replicate: behavior is model-specific — google/nano-banana* uses aspect_ratio, bytedance/seedream-* uses documented Replicate ratios, Wan 2.7 maps --ar to a concrete size
MiniMax: official aspect_ratio values; if --size <WxH> is given without --ar, sends width/height for image-01
Each image still needs its own reasoning / prompt writing / style exploration
Subagents
Work is still exploratory, each needs independent analysis
Input is outline.md + prompts/ (e.g. from baoyu-article-illustrator)
Batch — use {baseDir}/scripts/build-batch.ts to assemble the payload
The outline + prompt files already contain everything needed
Rule of thumb: once prompt files are saved and the task is "generate all of these", prefer batch over subagents. Use subagents only when generation is coupled with per-image thinking or divergent creative exploration.
Parallel behavior:
Default worker count is automatic, capped by config, built-in default 10
Provider-specific throttling applies only in batch mode; defaults are tuned for throughput while avoiding RPM bursts
Override with --jobs <count>
Each image retries 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
Codex image2 fallback
If --provider openai --model gpt-image-2.5-flare fails because OPENAI_API_KEY is missing but the current runtime has a native image-generation backend or the repo-level codex-imagegen wrapper is available, use that path rather than leaving the user waiting. Be explicit about whether the fallback is true reference-image generation or only a text-prompt reconstruction from extracted visual traits. See references/codex-image2-fallback.md.
References
File
Content
references/usage-examples.md
Extended CLI examples across providers and batch mode
references/codex-oauth-vs-openai-api-key.md
Why Codex/ChatGPT OAuth image2 entitlement is not usable through baoyu-image-gen's standard OpenAI API-key provider
references/codex-image2-fallback.md
Practical fallback behavior when OpenAI API credentials are absent but Codex/native image generation is available
references/providers/dashscope.md
DashScope families, sizes, limits
references/providers/zai.md
Z.AI GLM-image / cogview-4
references/providers/minimax.md
MiniMax image-01 + subject reference
references/providers/openrouter.md
OpenRouter multimodal flow
references/providers/replicate.md
Replicate supported families + guardrails
references/providers/agnes.md
Agnes (agnes-image-2.5-flash) sizing, refs, and limits
references/config/preferences-schema.md
EXTEND.md schema
references/config/first-time-setup.md
First-time setup flow
Extension Support
Custom configurations via EXTEND.md. See Step 0 for paths and schema.
How do I install baoyu-image-gen?
Download skills/baoyu-image-gen/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does baoyu-image-gen live?
The publisher keeps it at https://github.com/JimLiu/baoyu-skills.
Which agents can use baoyu-image-gen?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.