Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
🚀 ONE-COMMAND SETUP: Eliminates installation friction with unified_setup.sh that automatically handles 3-layer memory system creation, Python dependencies, OpenClaw memorySearch integration, and initial indexing. No more manual configuration steps.
What This Skill Does
Three-layer memory system using Markdown files, ChromaDB vector embeddings, and a NetworkX knowledge graph that persists agent knowledge across sessions. One command installs all dependencies and configures OpenClaw to index directive files like SOUL.md and AGENTS.md that its built-in memory search ignores by default.
Combining vector search with a knowledge graph lets the agent surface semantically related facts and traverse concept relationships, while the OpenClaw integration closes a gap where agents silently ignore their own directive files.
When to use it
- Recalling an architectural decision made weeks ago without fabricating details
- Looking up an exact repo URL or contact handle before taking an action
- Reviewing what the agent logged during a previous work session
- Keeping agent behavior consistent with workspace rules after restarts
- Building up an institutional knowledge base of people, repos, and infrastructure over time
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: 'What did we decide about the authentication approach last month?'
- 1Runs memory_search or auto_retrieve.py with the query before responding
- 2Vector search returns top-3 semantically similar chunks from MEMORY.md and daily logs
- 3Graph traversal surfaces related concepts linked to the matching chunks
- 4Agent cites the source file and section in its answer
Agent returns the recorded decision with the date it was logged and the file it came from, without fabricating details