Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
A drop-in replacement for OpenClaw's default memory search. Uses a 4-signal fusion pipeline combining vector similarity, keyword matching, header matching, and filepath scoring to retrieve memory chunks. Also includes salience scoring and a knowledge graph cross-reference builder.
Achieves 0.782 MRR versus approximately 0.45 for baseline vector-only search by fusing four signals and applying adaptive weighting when keyword overlap is low.
When to use it
- Finding relevant past context when vector-only search returns weak results
- Surfacing stale memory files during agent heartbeat cycles
- Building cross-reference links between related memory chunks
- Querying memories with date references like 'last Monday' or 'Feb 8'
- Indexing updated memory files after significant workspace changes
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: 'What did I decide about the auth system last week?'
- 1Detects temporal reference 'last week' and applies 3x boost to date-matching files
- 2Embeds the query using nomic-embed-text via Ollama
- 3Scores all memory chunks across vector similarity, keyword overlap, header match, and filepath
- 4Checks if top score exceeds PRF threshold; if not, expands query terms and re-scores
- 5Returns ranked results with path, score, and snippet
Ranked memory chunks referencing auth system decisions from the relevant timeframe
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.