ichiro-mind

What This Skill Does

Ichiro-Mind serves as the cognitive backbone for OpenClaw AI agents, providing a sophisticated, multi-layered memory architecture designed for persistence, contextual awareness, and high-speed retrieval. Unlike simple conversational history, Ichiro-Mind organizes information across four distinct layers: HOT (immediate session states), WARM (associative neural graphs for causality), COLD (vector-based semantic search for facts), and ARCHIVE (long-term historical logs). By leveraging this structure, the agent automatically routes queries to the most efficient memory layer, ensuring it remembers complex user preferences, causal chains, and historical decisions without becoming overwhelmed by noise or data redundancy. The system includes an integrated Hygiene Engine for automated deduplication and token optimization, alongside a Learning Engine that evolves entity knowledge over time.

Installation

To integrate this memory system into your agent, run the following command in your terminal:

clawhub install openclaw/skills/skills/hudul/ichiro-mind

Ensure your agent has the necessary filesystem permissions to maintain the SESSION-STATE.md and repository-backed archive storage, as the skill periodically performs file operations to ensure persistence across sessions.

Use Cases

Ichiro-Mind is ideal for long-running AI agents that need to maintain context over days or weeks. Common use cases include:

• Personal Assistant Agents: Remembering your evolving project preferences, dietary restrictions, or schedule patterns over months.
• Software Development Agents: Maintaining context on legacy codebase architectural decisions and previous technical troubleshooting steps.
• Research Assistants: Tracking complex information across a vast repository of documents where semantic search is critical.
• Strategic Planning Agents: Using the WARM layer to map out causal dependencies in complex, multi-step business projects.

Example Prompts

• "Ichiro, based on our project history from last month, what were the main concerns we had about the API migration and how did we resolve them?"
• "Remember that I prefer to write my Python unit tests using the Pytest framework, never standard unittest."
• "What is the current status of the research project, and are there any unresolved contradictions in our recent notes?"

Tips & Limitations

To get the most out of Ichiro-Mind, ensure you allow the Hygiene Engine to run during low-activity periods to prevent memory bloat. While the system is highly efficient, remember that the COLD layer relies on semantic embeddings; thus, it excels at conceptual retrieval rather than exact regex matching. Avoid manually modifying the system-managed memory files while the agent is running to prevent race conditions. If you notice the agent 'forgetting' specific details, ensure the importance score threshold in your config is set appropriately to prevent important data from being archived too quickly.