Multi-Provider Image Generation Skill for AI Agents

Where this fits

Your agent is in the middle of a design workflow. A user says, "Create a mood board for a coastal cabin retreat, three variations, one with a reference photo I uploaded." Without a dedicated skill, your agent either stalls, hardcodes a single provider, or burns tokens on trial-and-error API calls. baoyu-image-gen slots in as a ready-made execution layer: it wraps OpenAI, Google, DashScope, and Replicate behind one script, handles reference images and aspect ratios, and respects user preferences stored in EXTEND.md.

Why agents benefit

• Multi-provider routing without glue code – The skill picks a provider based on your configured defaults or the user's explicit choice, so you don't maintain separate integrations.
• Preference persistence – Step 0 loads EXTEND.md (project or user level) before any generation. That means your agent remembers model defaults, style tweaks, and API keys across sessions.
• Sequential by default, parallel on request – For batch tasks like "generate 4 logo concepts," the agent can ask for parallel execution and get results faster, but stays safe with sequential calls for dependent prompts.
• Reference image support – Handles image-to-image workflows (e.g., "recolor this sketch") without you writing custom multipart logic.
• First-time setup guard – The blocking setup step forces clarity: if no config exists, the agent asks the user for preferences instead of guessing, which reduces failed generations.

Practical scenarios

1. Brand asset exploration with a client reference

A user uploads a competitor's ad and says, "Generate three alternative hero images in our brand style." The skill loads the project's EXTEND.md (which sets default_model.google to Gemini 2.0 Flash), sends the reference image, and outputs three variations with matching aspect ratios. The agent can then iterate by tweaking prompts, all within the same skill call.

2. Batch social media visuals with parallel generation

A content manager asks for 12 square images for a campaign, one per product. The agent detects the independent prompts, requests parallel generation, and collects the results in one response. Without parallel support, this would take 12 sequential round-trips; with it, the wall-clock time drops dramatically.

3. Fallback provider when rate limits hit

Your default provider (OpenAI) returns a 429. The skill's provider abstraction lets the agent switch to Replicate or DashScope without rewriting the prompt or losing the reference image. This resilience matters in production where uptime is non-negotiable.

Add it to your agent workflow

Install the skill in your agent's environment (e.g., a Claude Code or OpenAgentSkill-compatible runner):

npx skills add szsip239/teamclaw --skill baoyu-image-gen

Then trigger it naturally. For example, in a conversation:

User: Draw a futuristic city skyline at dusk, wide 16:9.
Agent: (loads EXTEND.md, picks default provider, runs main.ts with the prompt and aspect ratio)

The script returns image URLs or base64 data, which your agent can present to the user or save to disk.

Compare before adopting

Before you commit, compare against:

• Quality signals – Does the skill handle edge cases like invalid aspect ratios or missing API keys gracefully? Check the GitHub repo for error handling and tests.
• Maintenance freshness – Last commit date and open issues. A stale skill may break with SDK updates.
• Alternatives – Other image-gen skills exist (e.g., single-provider wrappers). Weigh multi-provider flexibility vs. simplicity.
• Workflow fit – The EXTEND.md preference system is opinionated. Ensure your agent's config conventions align, or be ready to adapt.

You can also review the skill's public listing at OpenAgentSkill for community feedback and usage stats.

Why it is worth tracking

With 112 stars and an active repository, baoyu-image-gen shows early community traction. Its multi-provider approach and preference persistence solve a real pain point for agents that need to generate images consistently across sessions. The parallel option is a differentiator—most skills force sequential. Track it if you're building agents that handle visual content, and evaluate it when your current image generation pipeline feels brittle or provider-locked. The skill's design (blocking setup, explicit preference loading) also sets a good pattern for other agent skills to follow.