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
Getting Started with OpenCode for Local AI Coding Agent Setup
A beginner's guide walks through setting up OpenCode as a fully local AI coding agent using ByteShape's optimized models with LM Studio, llama.cpp, or Ollama across Mac, Linux, and Windows (WSL2).
Understanding the .claude/ folder structure for Claude Code configuration
The .claude/ folder contains two directories: project-level for team configuration and global ~/.claude/ for personal preferences. CLAUDE.md files provide instructions that Claude follows throughout sessions, with CLAUDE.local.md for personal overrides.
AGENTS.md Done Right: A 25% Correctness Boost — or a 30% Drop
Augment Code tested AGENTS.md files head-to-head: the best ones rival a model upgrade from Haiku to Opus; the worst ones hurt output. Decision tables, procedural workflows, and progressive disclosure win.
Four Methods to Transfer ChatGPT History to Claude's Memory
Claude now offers memory import for ChatGPT data, but there are four approaches with different trade-offs: built-in import for speed, curated abstraction for control, full export for preservation, or a hybrid method combining all three.