OpenClaw Application Development: Custom Skills and Plugin Development Guide

So your OpenClaw agent is up and running — it can hold a conversation, answer questions, and generally behave like a helpful assistant. But now you want it to do things: check order status, pull data from your CRM, trigger deployments, send notifications. That's where custom skills and plugins come in.

This guide covers both systems, when to use which, and how to build them properly.

Skills vs. Plugins: What's the Difference?

This trips up a lot of newcomers, so let's clarify upfront.

Skills are capabilities the AI agent can invoke during a conversation. The LLM decides when to call a skill based on the user's intent. A skill has a name, a description, parameters, and a handler function. Think of skills as tools the AI uses.

Plugins are lifecycle hooks that run automatically at defined points in the message processing pipeline. They intercept, modify, or augment messages and responses without the LLM explicitly choosing to invoke them. Think of plugins as middleware.

Aspect	Skills	Plugins
Triggered by	LLM decision	Lifecycle events
User-facing	Yes (results shown to user)	Usually invisible
Use case	API calls, data lookup, actions	Logging, filtering, enrichment
Configuration	Manifest + handler	Hook registration

Setting Up Your Development Environment

You need a running OpenClaw instance. The path of least resistance: deploy on Tencent Cloud Lighthouse using the one-click deployment guide. The pre-configured image includes everything — runtime, database, reverse proxy — so you can jump straight into development.

Grab an instance from the Tencent Cloud Lighthouse Special Offer page. The simple setup, high performance, and cost-effective pricing make it ideal for both development and production.

Once your instance is running, SSH in and familiarize yourself with the directory structure. Your skills and plugins will live in designated directories within the OpenClaw installation.

Building a Custom Skill: Step by Step

1. Define the Manifest

The manifest tells the LLM what your skill does:

{
  "name": "order_status",
  "description": "Look up the current status of a customer order by order ID",
  "parameters": {
    "type": "object",
    "properties": {
      "order_id": {
        "type": "string",
        "description": "The order ID (e.g., ORD-12345)"
      }
    },
    "required": ["order_id"]
  }
}

Write the description like you're explaining to a colleague what this function does. The LLM uses it to decide relevance.

2. Implement the Handler

async function handleOrderStatus({ order_id }) {
  try {
    const res = await fetch(
      `${process.env.ORDER_API_URL}/orders/${encodeURIComponent(order_id)}`,
      { headers: { 'X-API-Key': process.env.ORDER_API_KEY } }
    );

    if (res.status === 404) {
      return { message: `Order ${order_id} not found. Please check the ID and try again.` };
    }

    const order = await res.json();
    return {
      message: `Order ${order.id}: ${order.status}\nShipped: ${order.shipped_at || 'Not yet'}\nETA: ${order.eta || 'N/A'}`
    };
  } catch (err) {
    return { message: 'Unable to retrieve order status right now. Please try again later.' };
  }
}

3. Register and Deploy

Follow the Skills installation guide for the exact registration process. Once registered, the skill is immediately available to your agent.

Building a Plugin: Step by Step

Let's build a content filter plugin that screens outgoing responses for sensitive information.

module.exports = {
  name: 'pii-filter',
  version: '1.0.0',

  onResponseGenerated(context, response) {
    // Redact email addresses
    const filtered = response.text.replace(
      /[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}/g,
      '[EMAIL REDACTED]'
    );

    // Redact phone numbers (simple pattern)
    const cleaned = filtered.replace(
      /\b\d{3}[-.]?\d{3}[-.]?\d{4}\b/g,
      '[PHONE REDACTED]'
    );

    return { ...response, text: cleaned };
  }
};

This plugin runs on every response without the LLM knowing about it. It's pure middleware — invisible to the user but critical for compliance.

Connecting to Channels

Skills and plugins work across all connected channels. Once deployed, they're available whether the user is on:

No channel-specific code needed — OpenClaw abstracts the channel layer.

Debugging Tips

Check the skill invocation logs. If your skill isn't being called, the issue is almost always the manifest description — the LLM doesn't understand when to use it.
Test plugins in isolation. Write unit tests for your onMessageReceived and onResponseGenerated hooks before deploying.
Use the OpenClaw admin panel to verify skill registration status.
Tail the logs in real time: tail -f /path/to/openclaw/logs/app.log

Best Practices

One skill, one responsibility. Don't build a "do everything" skill. Smaller, focused skills are easier to maintain and more reliably invoked.
Fail gracefully. Return helpful error messages, not stack traces.
Keep plugins lightweight. Heavy processing in a plugin blocks the entire response pipeline.
Version your code. Commit skills and plugins to Git. Use the Tencent Cloud Lighthouse Special Offer to spin up a staging instance for testing before deploying to production.
Document your skills — even a one-line comment per function helps the next developer (or future you).

Start Building

The skill and plugin systems are where OpenClaw transforms from a chatbot into a platform. Every skill you add makes your agent more capable. Every plugin you write makes it more robust. And with Lighthouse handling the infrastructure, you can focus entirely on the logic that matters to your users.

Pick a real problem. Build a skill for it. Ship it today.

OpenClaw Application Development - Custom Skills and Plugin Development Guide