context-optimizer

What This Skill Does

The context-optimizer is an advanced memory management engine specifically architected for DeepSeek's 64k context window. It functions as an intelligent middleware layer between your conversation history and the AI model, ensuring that vital context is preserved while eliminating noise. By utilizing a suite of strategies—including semantic merging, temporal summarization, and extractive compression—it effectively prevents context overflow. Unlike basic truncation, this skill employs a query-aware relevance scoring system that prioritizes information dynamically, ensuring that the model always has access to the most pertinent data for the task at hand. It also introduces a hierarchical memory structure that offloads less urgent, older interactions to a secondary archive, which can be retrieved on-demand.

Installation

To integrate this skill into your environment, run the following command in your terminal: clawhub install openclaw/skills/skills/ad2546/context-optimizer Once installed, import the createContextPruner module into your project and initialize it with your desired contextLimit to start managing your message flow programmatically.

Use Cases

• Long-term Project Management: Keep context on complex, week-long development tasks without losing track of early architectural decisions.
• Deep Research: Process vast amounts of documentation or literature reviews while maintaining focus on specific investigative queries.
• Support Chatbots: Enable bots to handle prolonged customer sessions by compressing older, resolved interaction data while retaining summary logs for future reference.

Example Prompts

1. "Optimize my current session history to prioritize technical specifications discussed earlier while condensing the general chit-chat."
2. "Search the context archive for any mentions of the database migration plan from last week and integrate them into the active context."
3. "Summarize the last two hours of our conversation and purge redundant messages to free up at least 20,000 tokens for the next coding task."

Tips & Limitations

• Configuration: Always set your contextLimit slightly below the hard limit (e.g., 60,000 for a 64k window) to allow room for system prompts and function call overhead.
• Performance: While dynamic compaction is powerful, it uses compute resources. For very high-frequency interactions, schedule manual compaction intervals rather than relying exclusively on per-message auto-compaction.
• Limitations: The effectiveness of the archive retrieval is dependent on the clarity of your queries. Ensure that archived data has distinct semantic markers to improve retrieval accuracy.