SiliconFlow Image Generation for Openclaw

A high-performance image generation skill for AI agents leveraging the SiliconFlow API for FLUX and Stable Diffusion models.

lilei0311
v1.0.0
Feb 15, 2026
3
1.7k
0

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install siliconflow-image-gen

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install siliconflow-image-gen using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).

Prefer to download?

Get the raw skill files in a ZIP archive.

What is SiliconFlow Image Generation?

The SiliconFlow Image Generation skill provides a robust interface for generating high-fidelity visual content directly from your AI agent environment. By tapping into the SiliconFlow API, it grants access to industry-leading models including FLUX.1-schnell, FLUX.1-dev, and Stable Diffusion 3.5 Large. This skill is a core component for developers building visual workflows within the Openclaw Skills ecosystem, offering a bridge between text-based reasoning and professional image synthesis.

Designed with developer experience in mind, it handles the complexities of API communication and file management automatically. Whether you are using the free-tier models for rapid prototyping or high-end models for production-grade assets, this skill ensures that your Openclaw Skills enabled agents can produce visual results with minimal latency and maximum configuration flexibility.

SiliconFlow Image Generation Use Cases

  • Automated generation of visual assets for web development and UI mockups.
  • Integration of image generation capabilities into multi-agent coding and design workflows.
  • Rapid prototyping of marketing materials and social media content via CLI.
  • Programmatic creation of dataset images for machine learning or testing environments.

How SiliconFlow Image Generation Works

  1. The skill initializes by detecting the SILICONFLOW_API_KEY from environment variables or the global Openclaw configuration.
  2. The user or agent invokes the script with a text prompt and optional parameters such as model selection or output file path.
  3. The script sends a secure request to the SiliconFlow API endpoint with the specified prompt and model metadata.
  4. Upon a successful response, the skill automatically downloads the generated image and saves it to the local file system for immediate use.

SiliconFlow Image Generation Setup

To get started, install the skill via the command line:

npx clawhub install siliconflow-image-gen

Next, set your API key as an environment variable:

export SILICONFLOW_API_KEY="your-api-key"

Alternatively, you can integrate it directly into your Openclaw Skills global configuration at ~/.openclaw/openclaw.json:

{
  "models": {
    "providers": {
      "siliconflow": {
        "apiKey": "your-api-key"
      }
    }
  }
}

SiliconFlow Image Generation Data Schema & Taxonomy

The skill manages configuration and output data through a structured approach:

  • Configuration: Reads from ~/.openclaw/openclaw.json to extract provider credentials.
  • Input Parameters: Supports CLI arguments including --model and --output.
  • Model Mapping:
    Model Identifier Tier Description
    black-forest-labs/FLUX.1-schnell Free Optimized for speed and cost-efficiency.
    black-forest-labs/FLUX.1-dev Paid High-fidelity model for professional use.
    stabilityai/stable-diffusion-3-5-large Paid Advanced composition and prompt adherence.

SiliconFlow Image Generation Advanced Features

  • Multi-model support allowing seamless switching between FLUX and Stable Diffusion variants.
  • Automatic API key resolution from environment variables or local Openclaw Skills config files.
  • Native CLI support for direct integration into shell scripts and automated build pipelines.
  • Local file persistence with customizable output paths for generated assets.
  • Lightweight Python-based execution suitable for containerized agent environments.

SKILL.md


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