Agent skill · research science · daffy0208
Knowledge Graph Builder
Design and build knowledge graphs. Use when modeling complex relationships, building semantic search, or creating knowledge bases. Covers schema design, entity relationships, and graph database selection.
Why this skill is useful
Provides domain-specific knowledge on knowledge graph design, schema, and database selection that the AI wouldn't reliably generate on its own.
What it needs
About 7k tokens when loaded. Last updated 2025-12-25. 33 stars on the source repository.
What this skill does
Knowledge Graph Builder Build structured knowledge graphs for enhanced AI system performance through relational knowledge. Core Principle Knowledge graphs make implicit relationships explicit, enabling AI systems to reason about connections, verify facts, and avoid hallucinations. When to Use Knowledge Graphs Use Knowledge Graphs When: ✅ Complex entity relationships are central to your domain ✅ Need to verify AI-generated facts against structured knowledge ✅ Semantic search and relationship traversal required ✅ Data has rich interconnections (people, organizations, products) ✅ Need to answer "how are X and Y related?" queries ✅ Building recommendation systems based on relationships ✅ Fraud detection or pattern recognition across connected data Don't Use Knowledge Graphs When: ❌ Simple tabular data (use relational DB) ❌ Purely document-based search (use RAG with vector DB) ❌ No significant relationships between entities ❌ Team lacks graph modeling expertise ❌ Read-heavy workload with no traversal (use traditional DB) --- 6-Phase Knowledge Graph Implementation Phase 1: Ontology Design Goal: Define entities, relationships, and properties for your domain Entity Types (Nodes): Person, Organization, Location, Product, Concept, Event, Document Relationship Types (Edges): Hierarchical: ISA, PARTOF, REPORTSTO Associative: WORKSFOR, LOCATEDIN, AUTHOREDBY, RELATEDTO Temporal: CREATEDON, OCCURREDBEFORE, OCCURREDAFTER Properties (Attributes): Node properties: id, name, type, createdat, metadata Edge properties: type, confidence, source, timestamp Example Ontology: Validation: [ ] Entities cover all domain concepts [ ] Relationships capture key connections [ ] Ontology reviewed with domain experts [ ] Classification hierarchy defined (is-a relationships) --- Phase 2: Graph Database Selection Decision Matrix: Neo4j (Recommended for most): Pros: Mature, Cypher query language, graph algorithms, excellent visualization Cons: Licensing costs for enterprise, scaling complexity Use when …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills daffy0208/knowledge-graph-builder