Agent skill · data analytics · yonatangross
memory-fabric
Memory retrieval internals: knowledge graph orchestration with entity extraction, natural language query parsing, deduplication (>85% similarity), and cross-reference boosting over unified recency, relevance, and authority ranking. Use when designing or debugging how memory search itself works. Everyday lookups belong to memory; entry storage to remember; consolidation to dream.
Why this skill is useful
Adds a comprehensive memory retrieval system with entity extraction and deduplication capabilities that are not commonly found in public knowledge.
What it needs
Requires mcp__memory__search_nodes installed locally. About 3k tokens when loaded. Last updated 2026-08-06. 215 stars on the source repository.
What this skill does
Memory Fabric - Graph Orchestration Knowledge graph orchestration via mcpmemory for entity extraction, query parsing, deduplication, and cross-reference boosting. Overview Comprehensive memory retrieval from the knowledge graph Cross-referencing entities within graph storage Ensuring no relevant memories are missed Building unified context from graph queries Architecture Overview Unified Search Workflow Step 1: Parse Query Extract search intent and entity hints from natural language: Step 2: Execute Graph Query Query Graph (entity search): Step 3: Normalize Results Transform results to common format: Step 4: Deduplicate (>85% Similarity) When two results have >85% text similarity: 1. Keep the one with higher relevance score 2. Merge metadata 3. Mark as "cross-validated" for authority boost Step 5: Cross-Reference Boost If a result mentions an entity that exists elsewhere in the graph: Boost relevance score by 1.2x Add graph relationships to result metadata Step 6: Final Ranking Score = recencyfactor × relevance × sourceauthority Factor Weight Description ---------------- ------ ------------------------------------------- recency 0.3 Newer memories rank higher relevance 0.5 Semantic match quality sourceauthority 0.2 Graph entities boost, cross-validated boost Result Format Entity Extraction Memory Fabric extracts entities from natural language for graph storage: Load Read("references/entity-extraction.md") for detailed extraction patterns. Graph Relationship Traversal Memory Fabric supports multi-hop graph traversal for complex relationship queries. Example: Multi-Hop Query Integration with Graph Memory Memory Fabric uses the knowledge graph for entity relationships: 1. Graph search via mcpmemorysearchnodes finds matching entities 2. Graph traversal expands context via entity relationships 3. Cross-reference boosts relevance when entities match Integration Points With memory Skill When memory search runs, it can optionally use Memory Fabric for unified results. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills yonatangross/memory-fabric--f0b076