Databases
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neo4j-genai-plugin-skillneo4j-contribUse Neo4j GenAI Plugin ai.text.* functions and procedures for in-Cypher embedding generation, text completion, structured output, chat, tokenization, and batch ingestion. Covers ai.text.embed(), ai.text.embedBatch(), ai.text.completion(), ai.text.structuredCompletion(), ai.text.aggregateCompletion(), ai.text.chat(), ai.text.tokenCount(), ai.text.chunkByTokenLimit(), and provider configuration for OpenAI, Azure OpenAI, VertexAI, and Amazon Bedrock. Requires CYPHER 25. Replaces deprecated genai.veneo4j-getting-started-skillneo4j-contribOrchestrates zero-to-running-app in 8 stages — prerequisites → context → provision → model → load → explore → query → build. Each stage reads its own reference file. Supports HITL and fully autonomous operation. Use when starting a new Neo4j project from scratch, provisioning Aura, generating synthetic data, building a notebook or app, or running the full onboarding pipeline. Time budget ≤15 min autonomous, ≤90 min HITL. Does NOT cover Cypher query authoring — use neo4j-cypher-skill. Does NOT coneo4j-import-skillneo4j-contribImport structured data into Neo4j — LOAD CSV, CALL IN TRANSACTIONS, neo4j-admin database import full (offline bulk), apoc.load.csv/json, apoc.periodic.iterate, driver batch writes. Covers method selection, header file format, type coercion, null handling, ON ERROR modes, CONCURRENT TRANSACTIONS, pre-import constraint setup, and post-import validation. Use when importing CSV/JSON/Parquet files, migrating relational data to graph, or bulk-loading large datasets. Does NOT handle unstructured documeneo4j-kafka-skillneo4j-contribConfigure and operate 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, schema registry (Avro/JSON), and native db.cdc.query cursor-loop patterns (Neo4j 5.13+ Enterprise/Aura BC/VDC). Use when streaming Kafka events into Neo4j, streaming Neo4j changes to Kafka, or querying Neo4j change events without Kafka. Does NOT neo4j-modeling-skillneo4j-contribDesign, review, and refactor Neo4j graph data models. Use when choosing node labels vs relationship types vs properties, migrating relational/document schemas to graph, detecting anti-patterns (generic labels, supernodes, missing constraints), designing intermediate nodes for n-ary relationships, enforcing schema with constraints and indexes, or assessing an existing model against graph modeling best practices. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT handle Sprineo4j-query-tuning-skillneo4j-contribDiagnoses and fixes slow Neo4j Cypher queries by reading execution plans, identifying bad operators (AllNodesScan, CartesianProduct, Eager, NodeByLabelScan), and prescribing fixes (indexes, hints, query rewrites, runtime selection). Use when a query is slow, when EXPLAIN or PROFILE output needs interpretation, when dbHits or pageCacheHitRatio are poor, when cardinality estimation diverges from actuals, or when deciding between slotted/pipelined/parallel runtimes. Covers USING INDEX / USING SCAN neo4j-snowflake-graph-analytics-skillneo4j-contribRun Neo4j Graph Analytics algorithms (PageRank, Louvain, WCC, Dijkstra, KNN, Node2Vec, FastRP, GraphSAGE) directly inside Snowflake without moving data. Use when running graph algorithms against Snowflake tables via the Neo4j Snowflake Native App ("GDS Snowflake", "graph algorithms in Snowflake", "Neo4j Graph Analytics"). Covers the explore → prepare projection views → project-compute-write flow, the strict view/column type rules the graph engine requires, exact SQL CALL syntax, and privilege seneo4j-spark-skillneo4j-contribUse when reading from or writing to Neo4j with Apache Spark or Databricks using the Neo4j Connector for Apache Spark 6.0 (org.neo4j.connectors:spark) or 5.x (org.neo4j:neo4j-connector-apache-spark). Covers SparkSession setup, DataFrame reads via labels/Cypher/relationship scan, DataFrame writes with SaveMode, node.keys for MERGE, relationship write mapping, partition and batch tuning, PySpark and Scala examples, Databricks cluster config, Databricks secrets for credentials, Delta Lake to Neo4j pneo4j-vector-index-skillneo4j-contribCreate 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 pipelneon-drizzleneondatabaseCreates a fully functional Drizzle ORM setup with a provisioned Neon database. Installs dependencies, provisions database credentials, configures connections, generates schemas, and runs migrations. Results in working code that can immediately connect to and query the database. Use when creating new projects with Drizzle, adding ORM to existing applications, or modifying database schemas.opensearch-skillsopensearch-projectBuild search applications and query log analytics data with OpenSearch. Use this skill when the user mentions OpenSearch, search app, index setup, search architecture, semantic search, vector search, hybrid search, BM25, dense vector, sparse vector, agentic search, RAG, embeddings, KNN, PDF ingestion, document processing, or any related search topic. Also use for log analytics and observability — when the user wants to set up log ingestion, query logs with PPL, analyze error patterns, set up indchromaorchestra-researchOpen-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.faissorchestra-researchFacebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.pineconeorchestra-researchManaged vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.qdrant-vector-searchorchestra-researchHigh-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.index-at-creationparcadeiIndex at Creation Timepostgresql-expertpersonamanagmentlayerExpert-level PostgreSQL database administration, advanced queries, performance tuning, and production operations. Use when the user mentions database, SQL, or performance, or when the task involves Advanced Data Types, Full-Text Search, Advanced Indexes, or Advanced Queries.java-jpa-hibernatepluginagentmarketplaceMaster JPA/Hibernate - entity design, queries, transactions, performance optimizationmongoose-mongodbpluginagentmarketplaceWork with MongoDB in Node.js using Mongoose ODM for schema design, CRUD operations, relationships, and advanced queriesmysqlpluginagentmarketplaceMySQL database administration and developmentpowersyncpowersync-jaBest practices for building and maintaining applications with PowerSync: Cloud and self-hosted setup, sync configuration, client SDK usage, backend integration (Supabase, custom Postgres, MongoDB, Azure DocumentDB, MySQL, MSSQL), schema changes, watch and reactive queries (useQuery), attachments, the upload queue, and debugging. Use this skill whenever the user mentions PowerSync, a @powersync/* package (@powersync/web, @powersync/react-native, @powersync/node), the powersync Flutter/Dart packagprisma-8prismaUse when working in a project that depends on @prisma/orm-postgres, @prisma/orm-sqlite, or @prisma/orm-mongo (Prisma 8, formerly Prisma Next): editing contract.prisma or a contract.ts builder, running `prisma contract emit`, planning or applying migrations, editing migration.ts, writing db.orm / db.sql / db.query queries, wiring db.ts or middleware, integrating a build tool, using the Supabase extension or RLS, or reading a dotted error code such as MIGRATION.HASH_MISMATCH. Use when the user askprisma-next-extension-upgradeprismaUpgrade Prisma Next in your extension. Bumps every `@prisma-next/*` dependency to the requested target (or npm `latest`), runs the per-transition upgrade instructions for the extension SPI (middleware lifecycle, codec / migration-tools / framework-components churn, seed-migration on-disk shape), verifies the pins are correctly exact via `prisma-next-check-pins`, runs the extension's own typecheck and tests, and commits each minor step on its own. Use when the user asks to "upgrade Prisma Next" iprisma-next-supabaseprismaUse Prisma Next with a Supabase project via `@prisma-next/extension-supabase` — wire `extensions: [supabasePack]`, declare cross-space FKs to `supabase:auth.AuthUser`, author RLS policies (`policy_select` / `policy_update` / `@@rls`, `auth.uid()` predicates), build `db.ts` with the `supabase()` factory, bind roles per request (`asUser(jwt)` / `asAnon()` / `asServiceRole()`), query `auth.*` / `storage.*` via the `db.asServiceRole().supabase` admin root, and validate JWTs (`jwksUrl` for current prqdrant-advisorqdrantDiagnose, troubleshoot, and advise on any Qdrant deployment by loading the latest official Qdrant skills live from skills.qdrant.tech. Use this whenever someone raises a Qdrant problem or question — slow or degraded search, high or growing memory / OOM crashes, optimizer stuck or slow, indexing slowness, scaling and sharding decisions (node count, QPS, latency, multitenancy, vertical vs horizontal), poor or irrelevant search results, hybrid search and reranking, embedding-model migration, versioquestdbquestdbUse this skill whenever working with QuestDB — a high-performance time-series database. Trigger on any mention of QuestDB, time-series SQL with SAMPLE BY, LATEST ON, ASOF JOIN, ILP ingestion, or the questdb Python/Go/Java/Rust/.NET client libraries. Also trigger when writing Grafana queries against QuestDB, creating materialized views for time-series rollups, working with order book or financial market data in QuestDB, or any SQL that involves designated timestamps or time-partitioned tables. Qucreationix-rx-data-storereason-machinesExpert skill for using RX, an embedded data store for JSON-shaped data with random-access reads, no-parse lookups, and a text-safe binary encoding format.openduck-distributed-duckdbreason-machinesOpenDuck — open-source distributed DuckDB with differential storage, hybrid dual execution, and transparent remote database attachpgmicro-postgres-sqlitereason-machinesUse pgmicro — an in-process PostgreSQL reimplementation backed by SQLite-compatible storage, embeddable as a library or CLIxata-postgres-platformreason-machinesExpert skill for Xata open-source cloud-native Postgres platform with copy-on-write branching, scale-to-zero, and Kubernetes deploymentAgentDB Advanced FeaturesruvnetMaster advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.AgentDB Performance OptimizationruvnetOptimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.embeddingsruvnetVector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.market-ingestruvnetIngest and normalize market data into OHLCV vectors with HNSW indexingmigrate-createruvnetCreate a new sequentially numbered database migration with up/down SQL filesmigrate-validateruvnetValidate pending migrations for foreign key consistency, rollback safety, and best practicesvector-searchruvnetVector search via embeddings_* (large-scale HNSW) and ruvllm_hnsw_* (WASM router for ≤11 hot patterns), with RaBitQ 1-bit quantization for 32× memory reductionsql-jdbc-access-designsamberDesign customer-facing SQL access to a product's data - a JDBC/ODBC endpoint, warehouse share, or hosted query surface. Covers the architecture gate by scan frequency (zero-copy share, replicated copy, per-tenant compute isolation), engine-level tenant isolation with secure views and row-level security, an additive-only schema-stability contract enforced in CI, BI-tool connectivity and driver certification, query governance and cost caps, short-lived credential issuance, and the pricing shape. Umssqlsanjay3290Execute read-only SQL queries against multiple Microsoft SQL Server databases. Use when: (1) querying MSSQL/SQL Server databases, (2) exploring database schemas/tables, (3) running SELECT queries for data analysis, (4) checking database contents. Supports multiple database connections with descriptions for intelligent auto-selection. Blocks all write operations (INSERT, UPDATE, DELETE, DROP, etc.) for safety.postgressanjay3290Execute read-only SQL queries against multiple PostgreSQL databases. Use when: (1) querying PostgreSQL databases, (2) exploring database schemas/tables, (3) running SELECT queries for data analysis, (4) checking database contents. Supports multiple database connections with descriptions for intelligent auto-selection. Blocks all write operations (INSERT, UPDATE, DELETE, DROP, etc.) for safety.bun-sqlitesecondskyUse for bun:sqlite, SQLite operations, prepared statements, transactions, and queries.sap-abap-cdssecondskyComprehensive SAP ABAP CDS (Core Data Services) reference for data modeling, view development, and semantic enrichment. Use when creating CDS views or view entities, defining data models with annotations, working with associations and cardinality, implementing input parameters, using built-in functions, writing CASE expressions, implementing access control with DCL, handling CURR/QUAN data types, troubleshooting CDS errors, querying CDS views from ABAP, or displaying data with SALV IDA. Covers Asap-dataspheresecondskySAP Datasphere development skill with 3 specialized agents, 5 slash commands, and validation hooks. Use when building data warehouses on SAP BTP, creating analytic models, configuring data flows and replication flows, setting up connections, managing spaces and users, implementing data access controls, using the datasphere CLI, or inspecting authenticated Datasphere browser UI state with Microsoft Edge CDP. Covers Data Builder, Business Builder, analytic models, 40+ connection types, real-time rsap-hana-clisecondskyAssists with SAP HANA Developer CLI (hana-cli) for database development and administration. Use when: installing hana-cli, connecting to SAP HANA databases, inspecting database objects (tables, views, procedures, functions), managing HDI containers, executing SQL queries, converting metadata to CDS/EDMX/OpenAPI formats, managing SAP HANA Cloud instances, working with BTP CLI integration, or troubleshooting hana-cli commands. Covers: 91 commands, 17+ output formats, HDI container management, clousap-hana-mlsecondskySAP HANA Machine Learning Python Client (hana-ml) development skill. Use when: Building ML solutions with SAP HANA's in-database machine learning using Python hana-ml library for PAL/APL algorithms, DataFrame operations, AutoML, model persistence, and visualization. Keywords: hana-ml, SAP HANA, machine learning, PAL, APL, predictive analytics, HANA DataFrame, ConnectionContext, classification, regression, clustering, time series, ARIMA, gradient boosting, AutoML, SHAP, model storagesap-sqlscriptsecondskyThis skill should be used when the user asks to "write a SQLScript procedure", "create HANA stored procedure", "implement AMDP method", "optimize SQLScript performance", "handle SQLScript exceptions", "debug HANA procedure", "create table function", "inspect a browser-based Datasphere SQL editor with Microsoft Edge CDP", or mentions SQLScript, SAP HANA procedures, AMDP, EXIT HANDLER, or code-to-data paradigm. Comprehensive SQLScript development guidance for SAP HANA database programming includindata-engineersickn33Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.database-adminsickn33Expert database administrator specializing in modern cloud databases, automation, and reliability engineering.database-architectsickn33Expert database architect specializing in data layer design from scratch, technology selection, schema modeling, and scalable database architectures.database-migrationsickn33Master database schema and data migrations across ORMs (Sequelize, TypeORM, Prisma), including rollback strategies and zero-downtime deployments.