conversation-summary

What This Skill Does

The Conversation Summary skill for OpenClaw is designed to parse long-form conversation logs and distill them into concise, meaningful summaries. By leveraging the dedicated OpenClaw API endpoint, this skill facilitates efficient information retrieval, ensuring that users can maintain context across extensive chat sessions without needing to manually scroll through message history. The skill supports incremental updates, allowing it to ingest previous summaries alongside new conversation turns to create an evolving record of the interaction.

Installation

You can integrate this capability into your OpenClaw environment by executing the following command in your terminal:

clawhub install openclaw/skills/skills/dadaliu0121/conversation-summary-api

This command retrieves the necessary binaries and configuration files from the openclaw/skills repository, enabling the conversation-summary command immediately after setup.

Use Cases

• Meeting Recaps: Quickly generate a summary of a long brainstorm session to extract action items.
• Knowledge Retrieval: Use the summary to get a quick 'TL;DR' on complex topics discussed during a deep-dive session.
• Context Restoration: Perfect for returning to an AI agent after an extended period of inactivity to catch up on previous conclusions.
• Progress Tracking: Document the evolution of a project or decision-making process over multiple dialogue iterations.

Example Prompts

1. "Can you give me a quick recap of everything we've covered so far regarding the project roadmap?"
2. "Summarize our recent discussion about the architectural changes for the API."
3. "What did we conclude in our last conversation? Please provide a brief summary."

Tips & Limitations

• Data Formatting: Ensure that your chat history is correctly stringified before submission. The API strictly requires the chatList to be a string-encoded JSON array.
• Incremental Updates: To save processing time and maintain consistency, always include the previous summary in the historySummary parameter if one exists.
• Error Handling: Always monitor the code field in the API response. If the code is non-zero, the API failed to process the request, and you should log the error message provided.
• Token Constraints: While the skill handles large inputs, extremely long conversations should be chunked or managed periodically to ensure the model output remains focused and accurate.