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memory-fabric Knowledge graph orchestration layer with entity extraction, natural language query parsing, deduplication (>85% similarity), and cross-reference boosting. Unifies search results ranked by recency, relevance, and authority. Use when designing memory retrieval, building entity graphs, or optimizing knowledge graph queries.
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عرض تفاصيل Prompt يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/yonatangross/orchestkit --skill memory-fabricيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
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تحميل Zip جاري التحميل... المزيد من هذا المستودع Chain patterns for multi-phase pipelines: MCP detection, handoff files, checkpoint-resume, worktree agents, CronCreate monitoring. Use when building or debugging a pipeline skill.
OrchestKit doctor for health diagnostics across manifest integrity, hook configuration, skill validation, agent frontmatter, MCP server connectivity, CC version compatibility, and permission rules. Reports issues with severity levels and auto-remediation suggestions. Validates component counts, detects orphaned entries, and checks CC version matrix compliance. Use when diagnosing plugin health, troubleshooting configuration issues, or running pre-release checks.
Inspects the OrchestKit telemetry pipeline for the current project — lists all known telemetry files with write counts, sizes, schema status, growth trend, and orphan detection. Use when verifying the observability pipeline is healthy, debugging a missing writer, or auditing which files have schema locks vs. which are drift-vulnerable. Read-only — never modifies telemetry files.
name memory-fabric license MIT compatibility Claude Code 2.1.220+. Requires memory MCP server. description Knowledge graph orchestration layer with entity extraction, natural language query parsing, deduplication (>85% similarity), and cross-reference boosting. Unifies search results ranked by recency, relevance, and authority. Use when designing memory retrieval, building entity graphs, or optimizing knowledge graph queries. context fork version 2.1.0 author OrchestKit tags ["memory","orchestration","graph-first","graph","unified-search","deduplication","cross-reference"] user-invocable false disable-model-invocation true allowed-tools ["Read","Bash","mcp__memory__search_nodes"] complexity high persuasion-type collaborative effort high metadata {"category":"mcp-enhancement","mcp-server":"memory"}
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):
mcp__memory__search_nodes ({
query : "pagination database-engineer"
})
Step 3: Normalize Results Transform results to common format:
{
"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:
Keep the one with higher relevance score
Merge metadata
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 {
"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_PLUGIN_ROOT}/skills/memory-fabric/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:
Graph search via mcp__memory__search_nodes finds matching entitiesGraph traversal expands context via entity relationshipsCross-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 startstop/memory-fabric-sync.sh - Sync entities to graph at session end
Configuration
MEMORY_FABRIC_DEDUP_THRESHOLD=0.85
MEMORY_FABRIC_BOOST_FACTOR=1.2
MEMORY_FABRIC_MAX_RESULTS=20
MCP Requirements Required: Knowledge graph MCP server:
{
"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 storagecaching - 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
