Enable seamless conversational interactions and structured Q&A capabilities within your AI agent ecosystem using Openclaw Skills.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install chat-ask
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install chat-ask using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Chat/Ask Skill?
The Chat/Ask skill provides a robust framework for managing dialogue between users and AI agents. It serves as the primary interface for initiating conversations, asking targeted questions, and maintaining a structured history of interactions. By utilizing this within the suite of Openclaw Skills, developers can build more interactive and context-aware applications.
This skill includes built-in support for context-aware messaging and detailed response toggling, making it ideal for both quick queries and deep-dive analysis. It is a fundamental component for anyone looking to streamline human-to-agent communication while ensuring data is handled in a structured, developer-friendly format.
Chat/Ask Skill Use Cases
- Initiating multi-turn conversations with specific context to guide AI responses.
- Fetching detailed answers to complex technical questions using the detailed flag.
- Summarizing previous interactions to maintain context window efficiency.
- Clearing session data to ensure privacy or reset agent states during testing.
- Retrieving historical logs for debugging and interaction auditing within Openclaw Skills.
How Chat/Ask Skill Works
- The user sends a message via the chat tool, optionally providing context to guide the agent response.
- For specific inquiries, the ask tool processes questions and returns either concise or detailed answers based on the provided parameters.
- All interactions are processed and can be logged via the chat history tool, which allows for retrieval, clearing, or summarization of the dialogue.
- The system returns standardized JSON responses including success status and timestamps to ensure compatibility across the ecosystem of Openclaw Skills.
Chat/Ask Skill Setup
To use this skill, ensure python3 is installed on your environment. Since this is a core part of Openclaw Skills, it works out of the box with minimal configuration. You can invoke tools using the provided python scripts:
# Start a chat conversation
python3 scripts/chat_tool.py 'Hello, how are you?' 'Just checking in'
# Ask a specific, detailed question
python3 scripts/ask_tool.py 'What is OpenClaw?' true
Chat/Ask Skill Data Schema & Taxonomy
The skill returns data in a consistent JSON format to maintain interoperability with other Openclaw Skills.
| Field | Type | Description |
|---|---|---|
| status | string | Indicates success or error |
| timestamp | string | ISO format timestamp of the interaction |
| tool | string | The specific tool name that was executed |
| answer/response | string | The AI-generated content |
| history | array | List of previous messages (for history tool) |
Chat/Ask Skill Advanced Features
- Contextual Injection: Pass additional context strings to the chat tool to ground the AI's response in specific data or scenarios.
- Granular History Control: Use the 'summary' action within chat history to compress long conversations without losing essential information.
- Detailed Toggle: Switch between quick answers and comprehensive explanations to balance speed and depth in Openclaw Skills.
- Standardized Response Format: All tools output a unified JSON schema, making it easy to pipe data into other automation workflows.
SKILL.md
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