OpenClaw Onboarding: How to Train Your AI Agent Right
OpenClaw Onboarding: How to Train Your AI Agent Right
Onboarding is the most important stage of working with OpenClaw. How you "introduce" yourself to the agent determines the quality of all future work. This is a $30-50 token investment that pays off many times over.
Why This Is Critical
"Think of OpenClaw as cheap labor you must train — garbage instructions = garbage output"
The agent is not a ready product—it's a trainable system. The better you train it, the more useful it becomes.
Model for Onboarding
Use Claude Opus
- Gives the agent best "personality"
- Understands nuances
- Costs $30-50 in tokens
- After setup, switch to cheap model
"Not even close for anything else. It will give your bot the most personality."
What to Tell the Agent
About yourself:
- Profession and work area
- Work habits
- Personal habits
- Schedule (when you work, when you sleep)
- Time zone
About interests:
- What content you consume
- What news interests you
- Hobbies
- Sports
- Food
About goals:
- Why you need the agent
- What tasks to automate
- What takes most time
- What annoys you in routine
Q&A Session
Ask the agent to interview you:
"Ask me a very in-depth Q&A about myself, my work habits, my personal habits, what I want to use you for, what things I am interested in, what content I watch, what foods I like, what sports I follow."
Answer as detailed as possible.
Define Personality
Examples:
- Ziggy from Quantum Leap
- JARVIS from Iron Man
- Cortana from Halo
- Your own unique personality
What to define:
- How it addresses you
- Formal / informal
- Humor or seriousness
- Tone of voice
- Emoji usage
After Onboarding
- Run
/compact— clear context - Ask to commit to memory
- Check what was saved
- Switch to cheap model
Invest in onboarding—it pays dividends forever.
👀 See Also
Building a serverless AI agent platform on AWS for $0.01/month with Claude Code
A developer built a complete AWS serverless platform running AI agents for approximately $0.01/month using Claude Code over 29 hours, eliminating expensive components like NAT Gateway ($32/month) and ALB ($18/month). The project includes 233 unit tests, 35 E2E tests, and deploys with a single cdk deploy command.
Using the Dispatcher Pattern to Reduce Claude API Costs by 95%
A developer reduced their Claude API costs from $800-$2,000/month to about $215/month by implementing a dispatcher pattern that delegates heavy work to Claude Code CLI on a Claude Max subscription, while using minimal API tokens for orchestration.
Mastering Backup: Safeguarding Your OpenClaw Agent
In an era dominated by automation and AI, ensuring the safety of your OpenClaw agent through robust backup strategies is paramount. Learn the essential steps to secure your digital assistant.
Practical Lessons from Building On-Device AI in React Native
A developer shares specific technical details from building a React Native app with on-device LLMs, image generation, voice transcription, and vision AI, including memory management strategies, library choices, and performance benchmarks.