🎯Skills27
Generates, optimizes, and validates Cypher 25 queries for Neo4j 2025.x and 2026.x, covering graph pattern matching, vector and fulltext search, subqueries, batch writes, indexes, and query optimization.
Design, review, and refactor Neo4j graph data models — covers choosing node labels vs relationship types vs properties, migrating relational/document schemas to graph, detecting anti-patterns like supernodes, and enforcing schema with constraints and indexes.
An official Neo4j skill for building GraphRAG retrieval pipelines using the neo4j-graphrag Python package, covering retriever selection, Cypher query fragments, LLM pipeline wiring, and LangChain integration.
Ingests unstructured and semi-structured documents (PDFs, HTML, plain text, Markdown) into Neo4j as a knowledge graph — covers chunking, LLM entity/relationship extraction, Document-Chunk-Entity graph structures, and RAG pipeline integration.
Creates and manages Neo4j vector indexes for similarity search, supporting HNSW configuration, quantization, embedding storage on nodes and relationships, and both the SEARCH clause and legacy query procedures.
A skill that guides users or agents through 8 sequential stages to go from zero to a running Neo4j application, covering prerequisites, provisioning, data modeling, loading, exploration, querying, and app building.
A Claude Code skill for the Neo4j Python Driver v6, covering driver lifecycle, execute_query, managed and explicit transactions, async patterns, result handling, UNWIND batching, and connection pool tuning for Python applications connecting to Neo4j.
Authoritative reference for the neo4j-agent-memory Python package, a graph-native memory system for AI agents built on Neo4j providing three memory layers (short-term, long-term, reasoning) in a single knowledge graph. Supports integrations with LangChain, PydanticAI, CrewAI, AWS Strands, Google ADK, OpenAI Agents, LlamaIndex, and the hosted NAMS service.
Guides you through Neo4j Graph Data Science (GDS) workflows including graph projection, algorithm execution (PageRank, Louvain, WCC, FastRP, KNN), execution modes (stream/stats/mutate/write), memory estimation, and the GDS Python client for building recommendation pipelines and graph analytics.
Diagnoses and fixes slow Neo4j Cypher queries by interpreting EXPLAIN/PROFILE execution plans, identifying problematic operators like AllNodesScan and CartesianProduct, and prescribing fixes including indexes, query hints, rewrites, and runtime selection.
A comprehensive Neo4j data import skill covering LOAD CSV, CALL IN TRANSACTIONS, neo4j-admin bulk import, and APOC load procedures. It provides method selection guidance, type coercion, null handling, concurrent transactions, pre-import constraint setup, and post-import validation.
Uses Neo4j GenAI Plugin ai.text.* functions for in-Cypher embedding generation, text completion, structured output, chat, tokenization, and batch ingestion — supports OpenAI, Azure OpenAI, VertexAI, and Amazon Bedrock providers.
Guides building and configuring GraphQL APIs backed by Neo4j using @neo4j/graphql, covering type definitions, relationships, custom Cypher resolvers, JWT/JWKS authorization, auto-generated mutations, and Apollo Federation.
Guides programmatic security management in Neo4j including RBAC/ABAC, user and role lifecycle, privilege grants/denies, property-level access control, and auth provider configuration for LDAP and OIDC/SSO.
Provisions and manages Neo4j Aura graph database instances via CLI (aura-cli) or REST API. Covers creating, pausing, resuming, resizing, and deleting AuraDB instances across all tiers (Free/Professional/Business Critical/VDC), with OAuth2 auth setup and CI/CD pipeline support.
Provides guidance for running serverless Graph Data Science (GDS) sessions on Neo4j Aura Business Critical and VDC, covering authentication, memory estimation, remote graph projection, algorithm execution, and session lifecycle management via the graphdatascience Python client.
Guides reading from and writing to Neo4j with Apache Spark or Databricks using the Neo4j Connector, covering DataFrame operations, partition tuning, PySpark/Scala examples, and Delta Lake to Neo4j pipelines.
Covers the Neo4j Go Driver v6 including driver lifecycle, ExecuteQuery, managed and explicit transactions, session configuration, error handling, and data type mapping for Go applications connecting to Neo4j.
A skill for the Neo4j Java Driver v6, covering driver lifecycle management, Maven/Gradle setup, managed and explicit transactions, async/reactive patterns, error handling, data type mapping, connection pool tuning, and causal consistency with bookmarks for Java and Kotlin projects.
Configures and operates the Neo4j Connector for Kafka (sink + source) and the native Neo4j CDC API. Covers Cypher/Pattern/CUD sink strategies, CDC-based and query-based source, exactly-once semantics, DLQ error handling, Confluent Cloud managed connector, and schema registry.
Neo4j .NET Driver v6 skill covering IDriver lifecycle, dependency injection registration, ExecutableQuery fluent API, managed transactions with ExecuteReadAsync/ExecuteWriteAsync, IResultCursor, null safety, UNWIND batching, and temporal types.
Runs Neo4j Graph Analytics algorithms (PageRank, Louvain, WCC, Dijkstra, KNN, Node2Vec, FastRP, GraphSAGE) directly inside Snowflake without moving data, using the Neo4j Snowflake Native App's project-compute-write pattern.
Manages Neo4j Aura Agents via the v2beta1 REST API—create, list, update, delete, and invoke agents backed by AuraDB, configure tools like CypherTemplate, SimilaritySearch, and Text2Cypher, and deploy to REST or MCP endpoints.
Provides guidance for using the Neo4j Visualization Library (NVL) to render interactive graph visualizations in the browser. Covers the base library, interaction handlers for zoom/pan/drag/click/hover/lasso, and React wrappers for embedding Neo4j graph data into web applications.
A Claude Code skill for installing and configuring the official Neo4j MCP server, enabling AI agents to connect to Neo4j databases via MCP-compatible clients including Claude Code, Cursor, and VS Code with support for stdio and HTTP transport.
Provides comprehensive guidance on Neo4j command-line tools including neo4j-admin for backup/restore/import, cypher-shell for ad-hoc queries and CI/CD scripting, aura-cli for cloud provisioning, and neo4j-mcp for MCP server installation.
Migrates Neo4j driver code and Cypher queries from older versions (4.x, 5.x) to current releases (2025.x/2026.x, Cypher 25), covering Python, JavaScript, Java, .NET, and Go drivers with diff-ready fixes for package renames, removed APIs, and syntax changes.