baoyu-imagine
by JimLiubaoyu-imagine is a multi-provider image generation skill with a typed CLI, mandatory EXTEND.md setup, reference image support, aspect ratio controls, and batch runs across OpenAI, Azure OpenAI, Google, OpenRouter, DashScope, MiniMax, Jimeng, Seedream, and Replicate.
This skill scores 84/100, which means it is a solid directory listing candidate: agents get a clear trigger, a real execution path, and enough repository evidence to use it with meaningfully less guesswork than a generic image-generation prompt. Directory users should still expect some setup overhead around Bun, provider credentials, and preferences before first successful use.
- Strong triggerability: the frontmatter description clearly says when to use it and what it supports, including text-to-image, reference images, aspect ratios, and batch generation.
- High operational substance: `SKILL.md` points to a concrete executable path (`scripts/main.ts`), defines a blocking Step 0 preference-loading flow, and the repo includes 21 scripts plus provider-specific implementations and tests.
- Good install-decision value: support spans multiple real providers (OpenAI, Azure, Google, OpenRouter, DashScope, MiniMax, Jimeng, Seedream, Replicate) with preference schema and first-time setup docs that show this is more than a placeholder wrapper.
- Adoption is not one-command simple: `SKILL.md` has no install command, and successful use depends on Bun or `npx bun`, provider environment setup, and EXTEND.md preferences.
- The skill is comprehensive but dense: long documentation and many provider paths may slow quick understanding for users who only want a minimal first-run example.
Overview of baoyu-imagine skill
What baoyu-imagine does
The baoyu-imagine skill is an API-driven image generation workflow for agents that need to create images reliably, not just suggest prompts. It supports multiple providers including OpenAI, Azure OpenAI, Google, OpenRouter, DashScope, MiniMax, Jimeng, Seedream, and Replicate, with options for text-to-image, reference images, aspect ratios, image size, and batch runs.
Who should install baoyu-imagine skill
baoyu-imagine is best for users who want a reusable, script-backed image pipeline with provider choice and repeatable defaults. It fits teams that already have API keys, need more control than a one-off chat prompt, or want to generate several images from saved prompt files without manually re-entering settings every time.
Why users pick it over ordinary prompts
The main differentiator is execution discipline. The skill forces a preference-loading step through EXTEND.md, then runs a typed CLI with provider-specific handling, retries, output naming, and batch controls. That makes baoyu-imagine for Image Generation more predictable than asking a general assistant to “make an image” and hoping it chooses the right model and parameters.
Biggest adoption constraints
The biggest blocker is setup: you need bun or npx access, provider credentials, and a valid EXTEND.md preference file or first-run setup flow. This is not the best fit if you only want occasional casual image generation inside a chat UI, or if you do not want to manage provider APIs and model defaults.
How to Use baoyu-imagine skill
Install context and first files to read
For baoyu-imagine install, add the skill from the JimLiu/baoyu-skills repository in your skills environment, then read SKILL.md first. After that, the most useful files are references/config/first-time-setup.md, references/config/preferences-schema.md, scripts/main.ts, and scripts/main.test.ts. Those files explain the blocking preference step, config schema, CLI arguments, and expected execution behavior better than a quick repository skim.
Required inputs before your first run
Before using the baoyu-imagine skill, complete the mandatory preference load. The skill looks for .baoyu-skills/baoyu-imagine/EXTEND.md in project or user config locations. In practice, you need:
- a default provider
- a provider-specific default model
- API credentials for that provider
- optional defaults like aspect ratio, quality, image size, and batch worker limits
Without that, image generation should stop and ask for setup rather than guess.
How to call baoyu-imagine well
Strong baoyu-imagine usage starts with a complete request, not a vague idea. Good inputs usually include:
- subject: “a ceramic teapot on a wooden table”
- style: “clean product photography” or “anime concept art”
- composition: “three-quarter view, centered”
- background: “soft gray studio backdrop”
- output constraints:
16:9,1:1,2k, or4K - references: one or more image paths if consistency matters
A weak goal is “draw a teapot.” A stronger goal is: “Generate a 1:1 hero image of a matte white ceramic teapot, minimal studio lighting, soft shadow, premium ecommerce style, no text, no extra props.” That gives the provider enough structure to produce usable output on the first pass.
Practical workflow and batch guidance
Use single-image sequential generation for exploratory work and batch mode when you already have finalized prompts. The codebase supports promptFiles, referenceImages, batchFile, and jobs, with provider rate limits built in. A practical baoyu-imagine guide is:
- Set defaults in
EXTEND.md. - Test one prompt with one provider.
- Add aspect ratio and image-size constraints.
- Introduce reference images only when you need consistency.
- Move to batch files when generating a series of approved concepts.
This workflow avoids wasting tokens on parallel low-quality drafts.
baoyu-imagine skill FAQ
Is baoyu-imagine good for beginners?
Yes, if you are comfortable with API keys and config files. The skill is organized, tested, and explicit about setup, which helps beginners avoid hidden defaults. But it is not “zero-config”; the blocking EXTEND.md step means first-time users need a few minutes of setup before the first image.
When is baoyu-imagine a better fit than normal chat prompting?
Use baoyu-imagine when you need provider control, repeatability, saved preferences, reference image support, or batch generation. A normal prompt is fine for casual experimentation. The baoyu-imagine skill is better when output quality depends on consistent models, sizes, and reusable workflow settings.
Does baoyu-imagine support multiple image providers well?
Yes. The repository has separate provider modules and tests for Azure, OpenAI, Google, OpenRouter, DashScope, MiniMax, Jimeng, Seedream, and Replicate. That matters because provider behavior and argument validation differ. The skill’s structure reduces trial-and-error when switching providers or debugging environment issues.
When should you not install baoyu-imagine?
Skip baoyu-imagine install if you only generate images occasionally in a hosted chat app, do not want to manage credentials, or do not need batch files or structured defaults. It is also a poor fit if your workflow depends on heavy manual visual editing rather than prompt-driven generation.
How to Improve baoyu-imagine skill
Give baoyu-imagine richer creative constraints
The fastest way to improve baoyu-imagine for Image Generation results is to specify intent, framing, and exclusions up front. Include medium, lighting, camera angle, mood, and what to avoid. If you want consistency across images, repeat the non-negotiable attributes exactly instead of paraphrasing them between runs.
Use reference images selectively
Reference images help when matching character identity, product shape, palette, or composition, but they can also overconstrain results. Start with one clear reference image before adding several. If outputs become stiff or too derivative, remove weaker references and strengthen the textual brief instead.
Fix common failure modes after the first output
If the first image is close but wrong, change one variable at a time:
- wrong composition: rewrite framing and camera angle
- wrong style: name the target style more directly
- wrong proportions: add subject scale and layout cues
- too generic: add material, era, environment, and mood
- unstable batch results: reduce jobs or keep provider/model fixed
This is usually better than rewriting the whole prompt from scratch.
Tune config and throughput for real workloads
For repeated baoyu-imagine usage, improve defaults in EXTEND.md instead of restating them every time. Set your default provider, default model, and preferred aspect ratio once. For batch workloads, review batch.max_workers and provider_limits in references/config/preferences-schema.md; aggressive parallelism can hurt reliability faster than it improves speed.