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

Short Leash AI Coding Method: Beat Fable by Keeping Control
Greg Slepak's short leash method for AI coding agents: plan, review every diff, deny bad changes, commit after subtasks. Beats Fable quality by keeping the developer in the loop.

Claude vs GPT for PhD Academic Writing: Preserving Technical Meaning in Methods Sections
A PhD candidate compares Claude and GPT for polishing computer vision / hardware co-design papers, finding Claude more reliable at preserving technical meaning and argument structure while GPT sometimes oversimplifies claims.

OpenClaw Fallback Chains Preserve Uptime but Can Quietly Lower Reliability
OpenClaw's model fallback mechanism can mask reliability issues. A smaller fallback model may pass simple tasks but fail complex ones, costing more in retries and review.

OpenClaw setup tips from a user's experience: Gmail MCP, profile flags, and networking issues
A user running OpenClaw on a Mac via UTM with Ubuntu VM shares specific configuration issues encountered: the Gmail MCP server requires html_body instead of body parameter, the --profile prod flag is needed to avoid a hardcoded dev identity, and API keys must be placed in auth-profiles.json via paste-token command.