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tools February 06, 2026 4 min read

The ultimate 'lazy' developer hack: Letting OpenClaw write its own skills

Stop writing boilerplate. Discover how OpenClaw can generate, test, and integrate its own skills, letting you build custom tools without leaving the chat.

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I’m going to be honest: I am lazier than the average developer. And I say that with pride.

Laziness is the mother of all good software engineering. It’s why we write scripts, why we build CI/CD pipelines, and why we spend six hours automating a five-minute task. But recently, I found a way to take that "virtuous laziness" to a level that feels almost like cheating.

I stopped writing my own tools. I started telling OpenClaw to write them for me—and not just write code, but test it, fix it, and install it.

This isn’t about generating a snippet of Python to copy-paste into VS Code. This is about an agent that can self-extend. It’s a workflow that has completely changed how I approach my terminal, and if you hate context switching as much as I do, you need to see this.

The problem with "quick" scripts

We’ve all been there. You need a simple tool—maybe something to check if a list of URLs is returning 200 OK, or a script to convert a batch of PNGs to WebP.

The logic is simple, but the friction is high. You have to:

  1. Create a new directory.
  2. npm init or pip install.
  3. Google the library name (is it requests or urllib?).
  4. Write the boilerplate.
  5. Debug the one typo you made.
  6. Run it.

By the time you’re done, twenty minutes have vanished. The "lazy" hack here is to remove steps 1 through 5 entirely.

The self-extending workflow

OpenClaw has a capability that I don’t think enough people talk about: Skill Generation. It’s not just a chatbot; it’s an agent with file system access and the ability to execute code.

Here is what happens when I need a new tool now. I don’t open an editor. I just type this into OpenClaw:

"Create a new skill called 'uptime-checker'. It should take a URL as input and return the status code and response time."

That’s it. Then I sit back and watch the magic happen.

Step 1: It writes the code

OpenClaw doesn't just hallucinate a response. It actually creates a file (usually in its own .opencode/skills directory). It picks the best tool for the job—maybe a Node.js script using fetch or a Python script using requests. It handles the imports and the argument parsing.

Step 2: It writes the test

This is the part that genuinely impressed me the first time I saw it. It doesn't just hope the code works. It generates a test file. It creates a mock scenario to ensure the function actually returns what it promised.

Step 3: It runs and fixes

It executes the test. If the test fails (maybe it forgot to install a dependency), it reads the error log, modifies the code, and runs the test again. It iterates until the little green checkmark appears.

Step 4: Integration

Once the test passes, the skill is "live." I can immediately use it in my next prompt.

"Check the uptime of smallai.in"

And it just works. I didn’t touch a single file.

A real example: The "JSON cleaner"

Last week I had a messy 50MB JSON file filled with duplicate entries. Normally, I’d sigh, open a Python script, import json, write a deduplication loop, etc.

Instead, I asked OpenClaw:
"Create a skill that takes a file path, removes duplicate objects based on the 'id' field, and saves the clean version."

It took about 45 seconds. It wrote the script, realized it needed to handle large file streams to avoid memory issues (smart!), tested it on a dummy file, and reported back: "Skill 'json-dedupe' is ready."

I typed: "Run json-dedupe on data.json". Done.

Why this changes the game

The value here isn't that the code is hard to write—it’s that the context switch is expensive.

Every time you leave your workflow to setup a new environment or write a utility script, you break your flow state. By delegating the creation of the tool to the agent, you stay focused on the goal (cleaning the data) rather than the process (writing the cleaner).

It feels like having a junior developer sitting next to you. You say, "Hey, write me a tool to do X," and they hand it to you a minute later, tested and ready to use.

Is it perfect?

No. Sometimes it tries to use a library that doesn't exist, or it misunderstands a complex requirement. But for the small, single-purpose tools that clutter up our "scripts" folder? It is flawless.

It’s a strange feeling, watching software write itself, test itself, and then offer itself up for you to use. But once you get used to it, going back to writing your own curl wrappers feels archaic.

If you are a developer, stop writing the boring stuff. Let the machine do it. That’s the ultimate lazy hack.

Conclusion

We are moving past the era where AI just answers questions. We are entering the era where AI builds the tools we use to find those answers.

Next time you find yourself about to write a "quick script" to parse a CSV or resize an image, stop. Ask OpenClaw to do it for you. Your future self will thank you for the saved time—and the uninterrupted flow state.