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
Multi-Agent Architecture: Avoiding the Single-Agent Pitfall in AI Systems
A Reddit post identifies the common architectural mistake of using a single agent for multiple tasks, which leads to fragile systems requiring constant babysitting. The solution proposed is an orchestrator-specialist model where each agent has a narrow, specific role.
Vibe Coding Rules: Build Side Projects from Your Phone Using Claude Code Without Reading Code
A senior engineer shares their rules for building side projects entirely from a phone using Claude Code without reading code: start in plan mode, commit to git, write tests, use subagents for reviews, and auto-mode.
iOS Shortcut Workaround for Sending iPhone Photos to Cowork via iCloud Sync
A developer created an iOS Shortcut called "PhoPo" that converts iPhone photos to JPEG, resizes them, and saves them to an iCloud-synced folder that Cowork can access, enabling Claude to analyze screenshots and photos from mobile devices.
Components of a Coding Agent: How Tools, Memory, and Context Extend LLMs
Sebastian Raschka breaks down the six building blocks of coding agents like Claude Code and Codex CLI, explaining how agent harnesses combine models with tools, memory, and repository context to make LLMs more effective for software work.