How to Build Custom Skills
for OpenClaw Bots

This session guide distills Keith Motte's live walkthrough on building custom skills and agent teams for OpenClaw bots. Whether you are creating a single-purpose skill (like finding new businesses in your city) or a complex multi-agent system (like an expert X/Twitter posting engine), the principles covered here will save you tokens, improve output quality, and give you a repeatable workflow you can use every time.

What You Will Learn

Keith outlines two distinct approaches for creating OpenClaw skills. Both are valid, but they serve different purposes and produce different levels of quality. Understanding when to use each is the first decision you need to make.

This is Keith's preferred method. The core idea is to use external LLMs and research tools you already pay for (Manus, Genspark, ChatGPT, Perplexity, Cowork, etc.) to do the heavy lifting — then hand a polished, research-backed package to OpenClaw for final optimization.

Step-by-Step Workflow

What Keith's Example Produced

For the Expert X/Twitter Posting skill, the Cowork/Claude LLM generated a complete system with the following architecture:

The package also included 2,000+ lines of documentation, a 50-hook library, profile schemas, a scoring rubric, and a command reference guide.

For simpler skills or rapid prototyping, you can skip the external research phase entirely and just tell OpenClaw what you want in plain language. Keith demonstrated this by creating a business-finder skill on the fly.

Example: New Business Finder

OpenClaw processed this single prompt and produced a working skill that:

• Fetches data from public sources (Secretary of State filings, OpenCorporates)
• Categorizes businesses by type (food, retail, etc.) and city
• Identifies potential leads with contact information
• Scores leads based on configurable tiers
• Sets up a cron job for daily automated execution
• Delivers results via message (Telegram in Keith's setup)

Once you have built your skill package externally, the import process into OpenClaw follows a standard pattern. Here is the workflow Keith demonstrated:

Keith demonstrated several approaches for testing skills before and after importing them into OpenClaw. Testing is critical because it helps you baseline performance and catch issues early.

Testing Environments

Testing Workflow

• 1
  Test the skill in the external LLM first (Cowork, Claude, Manus)
• 2
  Time how long tasks take to establish a baseline
• 3
  Import into OpenClaw and run the same test
• 4
  Compare results and execution time
• 5
  Take screenshots of outputs and feed them back as feedback
• 6
  Iterate: "Hey, you ran this, you gave me this banger post — here's the result" + screenshot

One of Keith's "secret sauce" techniques is applying Google's E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) framework to validate skill outputs. If it is good enough for Google rankings, it is good enough to ensure your agents produce trustworthy content.

Keith mentioned several tools throughout the session. Here is a consolidated reference of what he uses and recommends for different stages of the skill-building process.

Your Action Checklist