Databases

445 skills.

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database-migrations-sql-migrationssickn33SQL database migrations with zero-downtime strategies for PostgreSQL, MySQL, and SQL Server. Focus on data integrity and rollback plans.database-optimizersickn33Expert database optimizer specializing in modern performance tuning, query optimization, and scalable architectures.file-uploadssickn33Expert at handling file uploads and cloud storage. Covers S3, Cloudflare R2, presigned URLs, multipart uploads, and image optimization. Knows how to handle large files without blocking.neon-postgressickn33Guides and best practices for working with Neon Serverless Postgres. Covers setup, connection methods, branching, autoscaling, scale-to-zero, read replicas, connection pooling, Neon Auth, and the Neon CLI, MCP server, REST API, TypeScript SDK, and Python SDK.nosql-expertsickn33Expert guidance for distributed NoSQL databases (Cassandra, DynamoDB). Focuses on mental models, query-first modeling, single-table design, and avoiding hot partitions in high-scale systems.postgres-best-practicessickn33Postgres performance optimization and best practices from Supabase. Use this skill when writing, reviewing, or optimizing Postgres queries, schema designs, or database configurations.postgresqlsickn33Design a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features sql-prosickn33Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems.vector-database-engineersickn33Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar cortex-codesnowflake-labsRoutes Snowflake-related operations to Cortex Code CLI for specialized Snowflake expertise. Use when user asks about Snowflake databases, data warehouses, SQL queries on Snowflake, Cortex AI features, Snowpark, dynamic tables, data governance in Snowflake, Snowflake security, or mentions "Cortex" explicitly. Do NOT use for general programming, local file operations, non-Snowflake databases, web development, or infrastructure tasks unrelated to Snowflake.database-designspencerpaulyDesign database schemas — tables, relationships, indexes, constraints, and ORM setup. Covers relational design, normalization, and common patterns.surrealdb-vectorsurrealdbVector search with SurrealDB using HNSW indexes, KNN queries, and similarity scoring. Use when creating vector indexes, querying vectors with KNN distance operators, building semantic search or RAG pipelines, tuning HNSW parameters (EFC, M, M0, distance function, type), or implementing recommendation systems with SurrealDB. Triggers: HNSW, vector, embedding, KNN, cosine, euclidean, semantic search, RAG, vector::distance.surrealqlsurrealdbGenerate and modify SurrealQL queries to interact with SurrealDB databases. This includes creating and retrieving records, designing and managing schemas, establishing and querying graph relationships, performing live (real-time) queries, and leveraging all unique SurrealQL features for advanced database workflows. Use this skill whenever users need to write, adapt, or troubleshoot SurrealQL statements.cloud-storage-webtencentcloudbaseComplete guide for CloudBase cloud storage using Web SDK (@cloudbase/js-sdk) - upload, download, temporary URLs, file management, and best practices.cloudbase-document-database-web-sdktencentcloudbaseUse CloudBase document database Web SDK only for confirmed NoSQL collection work. Query, create, update, and delete document data; if the task mentions PostgreSQL / CloudBase PG / app.rdb(), route to postgresql-development instead.data-model-creationtencentcloudbase[Deprecated] Optional advanced tool for complex data modeling. For simple MySQL table creation, use relational-database-tool directly; for PostgreSQL / CloudBase PG schema work, use postgresql-development. New environments should use PostgreSQL DDL via queryPgDatabase/managePgDatabase — see postgresql-development skill instead.relational-database-mcp-cloudbasetencentcloudbase[Deprecated] This is the required documentation for agents operating on the CloudBase Relational Database through MCP. It defines the canonical SQL management flow with `queryMysqlDatabase`, `manageMysqlDatabase`, `queryPermissions`, and `managePermissions`, including MySQL provisioning, destroy flow, async status checks, safe query execution, schema initialization, and permission updates. New environments should use PostgreSQL — see postgresql-development skill instead.relational-database-web-cloudbasetencentcloudbase[Deprecated] Use when building frontend Web apps that talk to CloudBase Relational Database via @cloudbase/js-sdk – provides the canonical init pattern so you can then use Supabase-style queries from the browser. New environments should use PostgreSQL with app.rdb() — see postgresql-development skill instead.ae-dataopsthinkingaiagenticengineUse ae-cli for DataOps warehouse tables/views and fields, recycling, workflow configuration and release, execution troubleshooting, backfill, SQL queries, and data integration.ae-metadatathinkingaiagenticengineAE metadata capability-gateway CLI: list, inspect, create, update, delete and download metadata data tables; bind an existing dimension table to a property or create and bind a CSV dimension table. Local file upload and event/property discovery are separate prerequisite capabilities.tigris-snapshots-forkingtigrisdataUse when needing point-in-time recovery, version control for object storage, or creating isolated bucket copies for testing/experimentation design-postgres-tablestimescaleUse this skill for general PostgreSQL table design. **Trigger when user asks to:** - Design PostgreSQL tables, schemas, or data models when creating new tables and when modifying existing ones. - Choose data types, constraints, or indexes for PostgreSQL - Create user tables, order tables, reference tables, or JSONB schemas - Understand PostgreSQL best practices for normalization, constraints, or indexing - Design update-heavy, upsert-heavy, or OLTP-style tables **Keywords:** PostgreSQL schema, t pgvector-semantic-searchtimescaleUse this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets setup-timescaledb-hypertablestimescaleUse this skill when creating database schemas or tables for Timescale, TimescaleDB, TigerData, or Tiger Cloud, especially for time-series, IoT, metrics, events, or log data. Use this to improve the performance of any insert-heavy table. **Trigger when user asks to:** - Create or design SQL schemas/tables AND Timescale/TimescaleDB/TigerData/Tiger Cloud is available - Set up hypertables, compression, retention policies, or continuous aggregates - Configure partition columns, segment_by, order_by, turso-dbtursodatabaseTurso (Limbo) database helper — an in-process SQLite-compatible database written in Rust. Formerly known as libSQL / libsql. Replaces @libsql/client, libsql-experimental for Turso use cases. Works in Node.js, browser (WASM + OPFS for persistent local storage), React Native, and server-side. Features: vector search, full-text search, CDC, MVCC, encryption, remote sync. SDKs: JavaScript (@tursodatabase/database), Serverless (@tursodatabase/serverless), Browser/WASM (@tursodatabase/database-wasm), mvcctursodatabaseOverview of Experimental MVCC feature - snapshot isolation, versioning, limitations redis-jsupstashWork with the Upstash Redis JavaScript/TypeScript SDK for serverless Redis operations. Use for caching, session storage, rate limiting, leaderboards, full-text search (querying, filtering, aggregating with @upstash/redis search extension), and all Redis data structures. Supports automatic serialization/deserialization of JavaScript types. Search also available via @upstash/search-redis and @upstash/search-ioredis adapters for TCP clients.upstash-redis-startupstashProvision a zero-config, no-signup, temporary Upstash Redis database for an AI agent with a single POST to https://upstash.com/start-redis, with no account, API key, or SDK setup required. Use when an agent needs scratch Redis right now and the user has not provided credentials, for short-term memory across tool calls, conversation history, a sub-agent work queue, ranked recall, or a quick prototype or demo. Covers idempotent creation and re-fetching credentials, calling the database through the upstash-search-jsupstashWork with the @upstash/search TypeScript/JavaScript SDK, a serverless full-text and semantic search database with built-in reranking. Use when adding search to an app or site, creating a search index, upserting documents with searchable content and filterable metadata, running keyword, semantic, or hybrid search queries, reranking results, filtering with SQL-like or structured filter syntax, paginating with range, fetching or deleting documents, resetting an index, or checking index info. Also u upstash-vector-jsupstashWork with the @upstash/vector TypeScript/JavaScript SDK, a serverless vector database for embeddings, similarity search, semantic search, and RAG (retrieval-augmented generation). Use when upserting, querying, fetching, ranging, or deleting vectors, upserting raw text against an index with a built-in embedding model, choosing dense, sparse, or hybrid indexes, filtering by metadata, organizing data with namespaces, running resumable queries, or connecting Upstash Vector to an AI or LLM applicatio django-safe-migrationvintasoftwareWrite, review, and rewrite Django migrations for PostgreSQL with zero-downtime guarantees. Use this skill whenever the user mentions migrations, Django schema changes, or deployment safety — even if they don't say "zero downtime" explicitly. Trigger on: "review this migration", "is this migration safe?", "write a migration for...", "rewrite this migration", "how do I add a NOT NULL column / drop a column / add an index / rename a column / add a FK without downtime", "will this migration cause lo masterdata-storage-strategyvtexApply when deciding whether VTEX Master Data is the right storage for a given workload, designing JSON Schemas with v-indexed, v-cache, v-security, and v-triggers, planning entity capacity and lifecycle, or auditing existing Master Data usage. Covers when to use MD versus Catalog, OMS, VBase, or external databases, schema design best practices, indexing strategy, trigger patterns, and operational considerations. Use before creating any new Master Data entity.vtex-io-data-access-patternsvtexApply when deciding where and how a VTEX IO app should store and read data. Covers when to use app settings, configuration apps, Master Data, VBase, VTEX core APIs, or external stores, and how to avoid duplicating sources of truth or abusing configuration stores for operational data. Use for new data flows, caching decisions, refactors, or reviewing suspicious storage and access patterns in VTEX IO apps.vtex-io-masterdatavtexApply when working with MasterData v2 entities, schemas, or MasterDataClient in VTEX IO apps, or when anyone designing or implementing a solution must scrutinize whether Master Data is the correct storage. The skill prompts hard questions: native Catalog or other VTEX stores, OMS, or an external database may be better; do not default to MD because it is convenient. Covers JSON Schema, CRUD, triggers, search and scroll, schema lifecycle, purchase-path avoidance, single source of truth, and BFF ha vtex-io-masterdata-strategyvtexApply when deciding whether and how VTEX IO apps should use Master Data v2 for custom data. Covers entity boundaries, schema lifecycle, indexing strategy, and when Master Data is the right storage mechanism versus another data approach. Use for reviews, wishlists, forms, or other custom data modeling decisions in VTEX IO apps.db-migration-helperwu529778790Use when generating database migration SQL from model or schema changes — compares current vs desired schema, detects diffs, outputs reversible safe migrations.grepai-storage-gobyoanbernabeuConfigure GOB local file storage for GrepAI. Use this skill for simple, single-machine setups.grepai-storage-postgresyoanbernabeuConfigure PostgreSQL with pgvector for GrepAI. Use this skill for team environments and large codebases.grepai-storage-qdrantyoanbernabeuConfigure Qdrant vector database for GrepAI. Use this skill for high-performance vector search.spark-optimizationwshobson10KOptimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.wecomcli-diskwecomteam5.6K企业微信微盘（Disk / 网盘）文件操作技能。承接"微盘 / 网盘"里的文件列出、搜索、读取元信息、上传、下载、重命名、新建文件夹操作。用户明确提到"微盘"/"网盘"/"共享空间"时必须先读取本技能获取完整指引，不得凭记忆处理。用户说"上传到微盘"、"帮我在微盘里搜一下 xxx"、"微盘那个 PPT 在哪"、"下载微盘那个文件"、"把微盘那个文件重命名成 xxx"、或直接给出 `https://drive.weixin.qq.com/s?k=...` 形式的微盘文件链接时使用本技能。与 `wecomcli-doc` / `wecomcli-sheet` / `wecomcli-smartsheet` / `wecomcli-smartpage` 的区别：本技能处理微盘里所有文件（含在线文档）的搜索/列表/基础信息/位置/路径/重命名等文件级操作；在线文档（`doc/sheet/smartsheet/smartpage` 类型）的内容读写走对应文档技能，不由本技能接管。当用户问「这个文档在微盘哪里」或问某文件在微盘的位置时，由本技能用 get 返回空间名/文件夹名/路径等元信息。baidu-drivebaidu-netdisk1.4K百度网盘(Baidu Drive)文件管理 — 上传、下载、转存、分享、搜索、移动、复制、重命名、创建文件夹。同时支持 Agent 记忆备份/恢复（kimiclaw/maxclaw/qclaw/openclaw）。 TRIGGER: 用户提及"百度网盘/bdpan/网盘/云盘/baidu drive/Baidu Drive"并涉及文件操作；或用户提及"备份记忆"、"恢复记忆"、"查看记忆备份"等记忆相关操作。 DO NOT TRIGGER: 非文件存储操作，或使用其他云盘服务时；本地记忆整理/清理操作；PPT 生成操作（已独立为 baidu-wenku-aippt skill）。sf-datacloud-preparejaganproSalesforce Data Cloud Prepare phase. TRIGGER when: user creates or manages Data Cloud data streams, DLOs, transforms, or Document AI configurations, or asks about ingestion into Data Cloud. DO NOT TRIGGER when: the task is connection setup only (use sf-datacloud-connect), DMOs and identity resolution (use sf-datacloud-harmonize), or query/search work (use sf-datacloud-retrieve).ue-data-assets-tablesquodsoler data-engineering-data-pipelinesickn33You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.