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Found 37 Skills
Comprehensive guide for writing modern Neo4j Cypher read queries. Essential for text2cypher MCP tools and LLMs generating Cypher queries. Covers removed/deprecated syntax, modern replacements, CALL subqueries for reads, COLLECT patterns, sorting best practices, and Quantified Path Patterns (QPP) for efficient graph traversal.
Neo4j Cypher Shell skill. Connect to instances, run Cypher queries, and retrieve full graph schemas via cypher-shell CLI.
Use when upgrading Neo4j 4.x and 5.x Cypher queries to 2025.x/2026.x versions
Auto-provision a virtual Visa card to complete an online purchase. Use when user asks to buy something and needs payment, or explicitly mentions cypher-pay/agent-pay. Handles onboarding (email OTP + KYC — once ever), token generation (once per device), funding, card creation, 3DS, and card lifecycle.
Translate Cypher and Neo4j-style queries into HelixDB Rust DSL stored queries. Use when the input contains Cypher, Neo4j, MATCH, OPTIONAL MATCH, WHERE, RETURN, ORDER BY, LIMIT, DISTINCT, MERGE, CASE, UNWIND, FOREACH, DETACH DELETE, IS NULL, or variable-length path patterns and the goal is to produce an equivalent Helix Rust query.
Expert in Spring Data Neo4j integration patterns for graph database development. Use when working with Neo4j graph databases, node entities, relationships, Cypher queries, reactive Neo4j operations, or Spring Data Neo4j repositories. Essential for graph data modeling, relationship mapping, custom queries, and Neo4j testing strategies.
Knowledge graph specialist for entity and causal relationship modelingUse when "knowledge graph, graph database, falkordb, neo4j, cypher query, entity resolution, causal relationships, graph traversal, graph-database, knowledge-graph, falkordb, neo4j, cypher, entity-resolution, causal-graph, ml-memory" mentioned.
Work with ArcGIS Knowledge graphs for storing and querying connected data. Use for graph databases, relationship visualization, and openCypher queries.
Graph database implementation for relationship-heavy data models. Use when building social networks, recommendation engines, knowledge graphs, or fraud detection. Covers Neo4j (primary), ArangoDB, Amazon Neptune, Cypher query patterns, and graph data modeling.
Use when working with Neo4j command-line tools including neo4j-admin, cypher-shell, aura-cli, and neo4j-mcp
Create and manage Neo4j vector indexes, run vector similarity search (ANN/kNN), store embeddings on nodes or relationships, use SEARCH clause (Neo4j 2026.01+, preferred) or db.index.vector.queryNodes() procedure (deprecated 2026.04, still works on 2025.x), configure HNSW and quantization options, pick similarity function and embedding provider dimensions, and batch-update embeddings. Use when tasks involve CREATE VECTOR INDEX, vector.dimensions, cosine/euclidean search, embedding ingestion pipelines, or semantic nearest-neighbor lookup. Does NOT handle GraphRAG retrieval_query graph traversal — use neo4j-graphrag-skill. Does NOT handle fulltext/keyword indexes (FULLTEXT INDEX, db.index.fulltext) — use neo4j-cypher-skill. Does NOT handle GDS graph embeddings (FastRP, Node2Vec) — use neo4j-gds-skill.
Build GraphRAG retrieval pipelines on Neo4j using the neo4j-graphrag Python package (formerly neo4j-genai). Covers retriever selection (VectorRetriever, HybridRetriever, VectorCypherRetriever, HybridCypherRetriever, Text2CypherRetriever), retrieval_query Cypher fragments, query_params, pipeline wiring (GraphRAG + LLM), embedder setup, index creation, and LangChain/LlamaIndex integration. Does NOT handle KG construction from documents — use neo4j-document-import-skill. Does NOT handle plain vector search — use neo4j-vector-index-skill. Does NOT handle GDS analytics — use neo4j-gds-skill. Does NOT handle agent memory — use neo4j-agent-memory-skill.