OpenClaw Is the Most Powerful AI Tool Ever Created — But You Might Not Feel That Way
If you’ve tried OpenClaw and walked away thinking, “This just feels like ChatGPT inside Telegram,” you’re not wrong — but you’re also barely scratching the surface.
OpenClaw isn’t meant to feel impressive out of the box. Its real power only appears once you stop treating it like a chatbot and start using it like what it actually is: an autonomous AI employee that can think, delegate, act, and improve on its own.
Below are the five most powerful techniques and workflows that transform OpenClaw from a simple chat interface into a true agentic system.
1. Sub-Agents: The Secret to Real Power
Most users make the same mistake: they use one single agent for everything.
That works — but it’s inefficient and quickly becomes messy.
The real breakthrough comes when you create sub-agents, each designed for a specific role.
How This Works in Practice
Think of your main agent as a manager, not a worker.
In my setup:
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My main agent (Mono) acts as a thinking partner — strategy, decisions, brainstorming.
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Actual work is delegated to specialized sub-agents.
For example:
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A coding sub-agent handles all development tasks.
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It has its own model, context window, and memory.
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This prevents overload and keeps each agent sharp and focused.
When I ask Mono to build something, she doesn’t write code herself — she hands it off to the coding sub-agent, who does the execution.
Creating a Sub-Agent (Simple Prompt)
You can create one instantly with a single instruction:
Create a new sub-agent named Samantha and set her up as my dedicated coding assistant. Use a coding-optimized model as her primary model. Route all coding-related tasks to her. Leave my main agent unchanged and notify me when Samantha is ready.
Refresh your OpenClaw dashboard — your new agent will appear and be used automatically.
2. Skills: Turning Your Agent Into a Power Tool
OpenClaw becomes exponentially more capable once you start installing skills.
Skills are essentially new abilities you give your agent — think of them as plugins, but far more powerful.
ClawHub: The Skill Marketplace
One essential skill to install early is ClawHub.
ClawHub acts like an App Store for AI agents, allowing your agent to:
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Discover new skills
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Install them autonomously
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Update them when needed
One standout example is a skill that searches Reddit, X, YouTube, and the web from the last 30 days, giving you fresh trends instead of stale information.
Even better, you don’t need to manually install anything:
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Copy the skill link
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Paste it into your chat
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Ask your agent to install it for you
Important Security Note
ClawHub is community-driven. Anyone can publish a skill.
That means:
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Some skills may be poorly written
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A few may be malicious
Only install skills from trusted sources — or ask your agent to rebuild the functionality itself, which avoids external code entirely.
3. Proactive Agents: When AI Works Without Prompts
This is where OpenClaw stops being a chatbot and starts behaving like an employee.
OpenClaw can act proactively — meaning it doesn’t need you to ask before it does useful work.
Example: Autonomous Morning Briefs
Every morning at 8 AM, my agent sends me:
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The latest AI news
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Content ideas tailored to my interests
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A prioritized task list
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Suggestions for tasks the agent can complete for me
No prompt. No interaction. Just results.
Making It Even More Autonomous
You can push this further by instructing your agent to:
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Work daily on improving workflows
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Identify weak points
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Deliver completed projects or optimizations automatically
At that point, you’re no longer managing tasks — you’re reviewing outcomes.
4. Memory: Fixing the Biggest OpenClaw Pain Point
One of the most common complaints about OpenClaw is memory loss.
This happens because OpenClaw:
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Uses a limited context window
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Automatically compresses older conversations to save tokens
During compression, important details often get summarized away.
The Fix
You can solve this with a single pre-compaction instruction that forces the agent to:
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Identify critical information
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Save it to persistent memory files
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Preserve long-term context before compression occurs
With the right setup, memory loss becomes minimal — and in some cases, nearly permanent.
(There’s far more depth here, but this alone dramatically improves reliability.)
5. Self-Improving Agents: The Ultimate Upgrade
The most powerful OpenClaw setups don’t just execute tasks — they learn from correction.
Self-Improving Workflows
You can create a loop where your agent:
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Logs mistakes
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Stores corrections
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Adjusts future behavior automatically
There’s a popular community skill that sets this up using:
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Hot memory
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Correction logs
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Pattern tracking
Every time you correct the agent, it gets better — permanently.
Prefer Safety? Let the Agent Build It
If you don’t want to install community code, you can simply instruct your agent to:
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Create its own self-improvement system
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Store learning locally
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Avoid external dependencies
The result is nearly identical — with zero security risk.
Why OpenClaw Feels Underwhelming (Until It Doesn’t)
OpenClaw doesn’t impress through flashy UI or instant wow-moments.
It impresses when you:
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Delegate roles
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Install abilities
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Enable autonomy
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Preserve memory
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Let it improve itself
At that point, it stops feeling like software and starts feeling like infrastructure.
Most people never reach this stage — which is exactly why the ones who do gain an unfair advantage.
Final Thought
If you’re still prompting OpenClaw like a chatbot, you’re using maybe 10% of its power.
Used correctly, OpenClaw isn’t just a tool.
It’s a system that thinks, acts, improves — and works while you don’t.
