Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
- Hardened exclusion rules for memory indexing by skipping secret directories such as .ssh, .aws, and .env - Improved compliance with ClawHub security audit requirements - Documentation and install script updated to reflect new exclusion logic - No changes to functional code; update focused on enhanced security configuration
What This Skill Does
Manages local vector embeddings for OpenClaw agents using the Gemma-300M model via node-llama-cpp. All embeddings and searches run on-device with no external API calls. Includes recursive loop protection to prevent the agent from indexing its own vector database.
Removes dependency on external embedding APIs, keeping all data local and eliminating per-request costs or privacy exposure.
When to use it
- Searching past agent sessions without sending data to external servers
- Indexing a local codebase for semantic retrieval across agent runs
- Maintaining project context in offline or air-gapped environments
- Preventing memory bloat by excluding credential files and node_modules from indexing
- Monitoring vector memory health with a periodic heartbeat check
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Set up local memory indexing for my OpenClaw environment
- 1Checks for existing node-llama-cpp install and adds it if missing
- 2Configures the local memory provider with the Gemma-300M model path from Hugging Face
- 3Sets exclusion rules to skip the vector DB directory, node_modules, and credential files
- 4Restarts the OpenClaw gateway to apply the new configuration
- 5Runs initial memory index if chunk count is zero, otherwise reports existing chunk count
Memory is active with the indexed chunk count confirmed via `openclaw memory status`