Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
v2.2.0: Auto-ingest daily notes during consolidation, fix argparse conflict in store command, fix tree prune crash
What This Skill Does
Three-tier memory system (hot/warm/cold) for persistent agent memory across conversations. Facts are distilled, scored with recency decay, and organized in a hierarchical tree index. Critical state syncs to Turso after each conversation for cloud-first recovery and multi-device continuity.
LLM-powered tree search achieves 98%+ retrieval accuracy versus 70-80% for vector embeddings, and context size stays constant at 8-15KB regardless of how long the agent has been running.
When to use it
- Retrieving an architectural decision made three weeks ago without scanning full history
- Automatically ingesting daily markdown notes into the tiered memory system
- Finding all stored URLs associated with a specific tool or project
- Keeping agent context under 15KB after years of accumulated conversations
- Restoring full agent memory to a new device in under two minutes
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: What did we decide about the BSC integration approach?
- 1Agent sends the query to the tree search module
- 2LLM reasons over the tree index and identifies relevant categories such as projects/evoclaw/bsc and active_context/events
- 3Warm memory entries from those categories are fetched and ranked by score
- 4Cold storage is queried for lower-scored archived facts in the same paths
- 5Top results are injected into the agent context before the response is generated
Fact returned: 'Decided to use raw JSON-RPC for BSC to avoid go-ethereum dependency' with category path, importance score, and any preserved URLs or commands
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.