inference-sh

What This Skill Does

The inference-sh skill integrates the powerful inference.sh CLI into OpenClaw, allowing the agent to execute over 150 different AI applications in the cloud without needing a local GPU. This skill abstracts away the complexity of managing infrastructure, enabling your agent to generate high-quality images, create videos, perform advanced web searches, interact with cutting-edge LLMs like Claude and Gemini, and even automate social media tasks on Twitter. By leveraging the inference.sh serverless platform, the agent can call specialized models such as FLUX for image synthesis, Veo for video production, or Tavily for accurate web research, making it an all-in-one powerhouse for AI-driven workflows.

Installation

To enable this skill, use the clawhub command within your OpenClaw environment:

clawhub install openclaw/skills/skills/okaris/inference-sh

After installation, you must configure the environment by running infsh login to authenticate your account and link your workspace, ensuring your agent has the necessary API access to run the hosted models.

Use Cases

This skill is designed for agents that require multi-modal output. Use cases include:

• Content Creation: Automating the generation of blog visuals or social media videos using models like FLUX or Veo.
• Research: Performing deep web research via Tavily or Exa to synthesize information from the live internet.
• Knowledge Access: Connecting the agent to top-tier LLMs like Claude Sonnet or Opus for complex reasoning tasks that require more power than standard local models.
• Social Media Management: Streamlining Twitter operations, such as automated posting, liking, or engaging with content.

Example Prompts

1. "Generate a professional 3D product rendering of a sleek desk lamp using the Rodin generator and save it for me."
2. "Search the web for the latest advancements in quantum computing and summarize the findings in a concise report."
3. "Create a short, engaging video showing a drone flying over a mountain range and post it to my Twitter with a relevant caption."

Tips & Limitations

• Efficiency: Always use the --no-wait flag for long-running processes like video generation to keep the agent responsive for other tasks.
• Input Validation: Use infsh app sample <app> to generate a JSON template. This ensures your inputs are correctly formatted and prevents runtime errors.
• Monitoring: Utilize infsh task get <task-id> to check the status of jobs, as complex media rendering may take time. Keep in mind that heavy usage is subject to your inference.sh account quotas.