A streamlined framework for building, training, and deploying context-aware AI chatbots across various communication platforms using custom data.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install chatbot-builder
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install chatbot-builder using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
Chatbot Builder is a comprehensive tool designed to simplify the lifecycle of AI agent development. By utilizing this component within the Openclaw Skills ecosystem, developers can transform static documents, websites, and PDFs into interactive conversational agents. The skill focuses on versatility, allowing for seamless integration with top-tier LLMs like OpenAI and Anthropic, or even locally hosted models for enhanced privacy.
Beyond simple responses, the framework emphasizes conversational memory and context awareness, ensuring that user interactions feel natural and coherent. Whether you are building for a website widget or a major messaging platform, this tool provides the infrastructure to manage training, deployment, and analytics from a single interface.
To begin using this tool within your Openclaw Skills workflow, ensure you have Python 3.8 or higher installed. Follow these configuration steps:
# Create a new chatbot project instance
./chatbot.sh create support-bot
# Train the agent on your local documentation folder
./chatbot.sh train support-bot --data ./docs
# Configure your API credentials
export OPENAI_API_KEY='your-api-key-here'
# Deploy the agent to your preferred channel
./chatbot.sh deploy support-bot --channel web
The skill organizes its data and configuration into a structured taxonomy to ensure reliable performance across Openclaw Skills deployments:
| Component | Data Type | Purpose |
|---|---|---|
| Training Data | PDFs, URLs, TXT | The knowledge base used for RAG (Retrieval-Augmented Generation). |
| Memory Store | JSON/Vector DB | Maintains conversational state and user context across sessions. |
| Analytics | Log Files | Tracks message volume, response accuracy, and user engagement metrics. |
| Channel Manifest | YAML/JSON | Defines credentials and webhook endpoints for Slack, Discord, and WhatsApp. |
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