context-management

What This Skill Does

The context-management methodology provides a structured, file-based framework for AI Agents to maintain state across different sessions. Rather than relying on volatile model memory, this skill dictates a hierarchical file system architecture consisting of five distinct layers: SOUL.md (Identity), USER.md (User Context), AGENTS.md (Routing/Protocol), TOOLS.md (Environment), and a dual-layer memory system (daily logs and long-term MEMORY.md). By implementing this skill, your agent effectively moves its brain onto the disk, ensuring that it carries historical context, learned lessons, and specific user preferences into every new conversation. It transforms your agent from a stateless chatbot into an evolving autonomous assistant that gets smarter and more efficient over time.

Installation

Run the following command in your terminal to initialize the context management framework within your agent directory: clawhub install openclaw/skills/skills/349840432m-dev/agent-context-memory

Use Cases

• Long-Running Agents: Perfect for agents managing multi-day projects, research tasks, or repetitive daily automation workflows.
• Agent Workspace Initialization: Use this to standardize the structure of a new project, ensuring clear boundaries between agent identity, user preferences, and system environment configurations.
• Knowledge Capitalization: Use this to capture debugging insights, error fixes, and operational "tribal knowledge" that would otherwise be lost when the agent session expires.

Example Prompts

1. "I am starting a new project on market research. Please initialize the standard context management directory structure, starting with a SOUL.md file that defines my role as a professional analyst."
2. "I just encountered an error with the web-search skill. Please analyze the current daily memory log, identify the failure pattern, and summarize the fix into the long-term MEMORY.md file for future reference."
3. "Summarize the current project status and user preferences based on the content in USER.md and the latest memory logs to prepare for the next session."

Tips & Limitations

• Write, Don't Remember: The core principle is that explicit file documentation beats model inference. If it isn't in a file, assume the agent doesn't know it.
• Maintenance: Periodically review the memory/ folder and purge outdated logs to prevent the context window from becoming bloated with irrelevant historical data.
• Consistency: Ensure the agent has read/write access to the root of its workspace to effectively manage the file hierarchy.
• Granularity: When updating MEMORY.md, focus only on non-obvious, non-repeating insights. If an experience can be derived from existing code documentation, it does not belong in the long-term memory layer.