Neo4j plugins

Apply 33 structured reasoning frameworks — Bayesian analysis, root cause investigation, decision matrices, pre-mortems, and strategic planning — directly inside Claude Code to improve forecasts, valuations, architectural decisions, and problem-solving rigor.

Provides agent skills for comprehensive Neo4j database management: querying, modeling, data ingestion, AI/ML pipelines (GraphRAG, embeddings), graph algorithms, provisioning, security, and performance tuning.

Develop and manage Memgraph graph databases: write Cypher queries with Memgraph-specific extensions, build custom query modules in C++, Python, or Rust, design graph data models, configure triggers and storage, run graph algorithms, and build GraphRAG systems with hybrid retrieval.

Consult a senior DBA for expert guidance on schema design, database selection from 230+ engines (SQL/NoSQL/vector/time-series/streaming), query optimization, performance troubleshooting, HA/replication setup, migrations, security hardening, monitoring/alerting, capacity planning, backups/recovery, and IaC with Terraform/Kubernetes across relational, document, graph, and cloud-native databases.

Persists deterministic, team-accessible memory across Claude Code sessions with bi-temporal facts, RBAC, and zero-LLM recall. Installs a local MCP server backed by Postgres, Neo4j, and Pinecone, and automatically records file changes, commands, and session summaries.

Run local MCP servers to query Neo4j graph databases with Cypher, traverse graphs, inspect schemas, manipulate data, manage Aura cloud instances, store and retrieve memory, and design graph schemas using your credentials.

Turn any topic into a compact expertise artifact through a structured research pipeline (question tree, source discovery, zero-context fetch, .mv2 indexing, REPL distillation) without loading raw content into LLM context. Execute Python code in isolated Docker sandboxes for data analysis, prototyping, and DSPy sub-agents. Query Neo4j graph databases for knowledge retrieval.