Agent skill

memory-fabric

Knowledge graph memory orchestration - entity extraction, query parsing, deduplication, and cross-reference boosting. Use when designing memory orchestration.

View SKILL.md on GitHub Repository
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npx add-skill https://github.com/yonatangross/orchestkit/tree/main/plugins/ork/skills/memory-fabric

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Additional technical details for this skill

category
mcp-enhancement
mcp server
memory

SKILL.md

Memory Fabric - Graph Orchestration

Knowledge graph orchestration via mcp__memory__* 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

┌─────────────────────────────────────────────────────────────┐
│                    Memory Fabric Layer                      │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│   ┌─────────────┐              ┌─────────────┐              │
│   │   Query     │              │   Query     │              │
│   │   Parser    │              │   Executor  │              │
│   └──────┬──────┘              └──────┬──────┘              │
│          │                            │                     │
│          ▼                            ▼                     │
│   ┌──────────────────────────────────────────────┐          │
│   │            Graph Query Dispatch              │          │
│   └──────────────────────┬───────────────────────┘          │
│                          │                                  │
│                ┌─────────▼──────────┐                       │
│                │  mcp__memory__*    │                       │
│                │  (Knowledge Graph) │                       │
│                └─────────┬──────────┘                       │
│                          │                                  │
│                          ▼                                  │
│        ┌─────────────────────────────────────────┐          │
│        │        Result Normalizer                │          │
│        └─────────────────────┬───────────────────┘          │
│                              │                              │
│                              ▼                              │
│        ┌─────────────────────────────────────────┐          │
│        │     Deduplication Engine (>85% sim)     │          │
│        └─────────────────────┬───────────────────┘          │
│                              │                              │
│                              ▼                              │
│        ┌─────────────────────────────────────────┐          │
│        │  Cross-Reference Booster                │          │
│        └─────────────────────┬───────────────────┘          │
│                              │                              │
│                              ▼                              │
│        ┌─────────────────────────────────────────┐          │
│        │  Final Ranking: recency × relevance     │          │
│        │                 × source_authority      │          │
│        └─────────────────────────────────────────┘          │
│                                                             │
└─────────────────────────────────────────────────────────────┘

Unified Search Workflow

Step 1: Parse Query

Extract search intent and entity hints from natural language:

Input: "What pagination approach did database-engineer recommend?"

Parsed:
- query: "pagination approach recommend"
- entity_hints: ["database-engineer", "pagination"]
- intent: "decision" or "pattern"

Step 2: Execute Graph Query

Query Graph (entity search):

javascript
mcp__memory__search_nodes({
  query: "pagination database-engineer"
})

Step 3: Normalize Results

Transform results to common format:

json
{
  "id": "graph:original_id",
  "text": "content text",
  "source": "graph",
  "timestamp": "ISO8601",
  "relevance": 0.0-1.0,
  "entities": ["entity1", "entity2"],
  "metadata": {}
}

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 = recency_factor × relevance × source_authority

Factor Weight Description
recency 0.3 Newer memories rank higher
relevance 0.5 Semantic match quality
source_authority 0.2 Graph entities boost, cross-validated boost

Result Format

json
{
  "query": "original query",
  "total_results": 4,
  "sources": {
    "graph": 4
  },
  "results": [
    {
      "id": "graph:cursor-pagination",
      "text": "Use cursor-based pagination for scalability",
      "score": 0.92,
      "source": "graph",
      "timestamp": "2026-01-15T10:00:00Z",
      "entities": ["cursor-pagination", "database-engineer"],
      "graph_relations": [
        { "from": "database-engineer", "relation": "recommends", "to": "cursor-pagination" }
      ]
    }
  ]
}

Entity Extraction

Memory Fabric extracts entities from natural language for graph storage:

Input: "database-engineer uses pgvector for RAG applications"

Extracted:
- Entities:
  - { name: "database-engineer", type: "agent" }
  - { name: "pgvector", type: "technology" }
  - { name: "RAG", type: "pattern" }
- Relations:
  - { from: "database-engineer", relation: "uses", to: "pgvector" }
  - { from: "pgvector", relation: "used_for", to: "RAG" }

Load Read("${CLAUDE_SKILL_DIR}/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

Query: "What did database-engineer recommend about pagination?"

1. Search for "database-engineer pagination"
   → Find entity: "database-engineer recommends cursor-pagination"

2. Traverse related entities (depth 2)
   → Traverse: database-engineer → recommends → cursor-pagination
   → Find: "cursor-pagination uses offset-based approach"

3. Return results with relationship context

Integration with Graph Memory

Memory Fabric uses the knowledge graph for entity relationships:

  1. Graph search via mcp__memory__search_nodes 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.

With Hooks

  • prompt/memory-fabric-context.sh - Inject unified context at session start
  • stop/memory-fabric-sync.sh - Sync entities to graph at session end

Configuration

bash
# Environment variables
MEMORY_FABRIC_DEDUP_THRESHOLD=0.85    # Similarity threshold for merging
MEMORY_FABRIC_BOOST_FACTOR=1.2        # Cross-reference boost multiplier
MEMORY_FABRIC_MAX_RESULTS=20          # Max results per source

MCP Requirements

Required: Knowledge graph MCP server:

json
{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": ["-y", "@anthropic/memory-mcp-server"]
    }
  }
}

Error Handling

Scenario Behavior
graph unavailable Error - graph is required
Query empty Return recent memories from graph

Related Skills

  • ork:memory - User-facing memory operations (search, load, sync, viz)
  • ork:remember - User-facing memory storage
  • caching - Caching layer that can use fabric

Key Decisions

Decision Choice Rationale
Dedup threshold 85% Balances catching duplicates vs. preserving nuance
Parallel queries Always Reduces latency, both sources are independent
Cross-ref boost 1.2x Validated info more trustworthy but not dominant
Ranking weights 0.3/0.5/0.2 Relevance most important, recency secondary

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