Databases Skills

Designs database schemas, indexing strategies, query optimization, and migration patterns for SQL and NoSQL databases. Use when designing tables, optimizing queries, fixing N+1 problems, planning migrations, or when asked about database performance, normalization, ORMs, or data modeling.

Analyzes and optimizes application performance across frontend, backend, and database layers. Use when diagnosing slowness, improving load times, optimizing queries, reducing bundle size, or when asked about performance issues.

Pre-launch and pre-commit audit for vibe coding projects. Use when asked to check whether a project is ready to ship, deploy, merge, or commit, especially for common AI-built app mistakes: broken project structure, committed secrets or cache files, environment variable hygiene, database migrations, ORM/schema drift, unsafe raw SQL, unused legacy code, dead routes/components, weak auth, missing tests, build failures, and deployment footguns.

>- Corrects speech-to-text transcription errors using dictionary rules and Claude's built-in AI (no external API key required — Native AI Correction is the DEFAULT). Stage 3 API is a backup for automation without Claude Code. Builds personalized correction databases that learn from each fix, auto-loads person-name ASR variants from your people roster, and reads per-domain context files that prime the AI pass for context-dependent homophones. Triggers when working with ASR/STT output containing recognition errors, homophones, garbled technical terms, person-name errors, or Chinese/English mixed content. Also triggers on requests to clean up meeting notes, lecture transcripts, interview recordings, or any text produced by speech recognition. Use this skill even when the user just says "fix this transcript", "clean up these meeting notes", or mentions garbled names without invoking ASR specifically.

>- recon (find unsafe SQL construction sites), batched verify (trace user input to those sites in parallel subagents, 3 sites each), and merge (consolidate batch results). Covers string concat, f-strings, unsafe ORM methods, and dynamic identifiers. Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/sqli-results.md. Use when asked to find SQLi or database injection bugs.

Backend specialist for APIs, databases, authentication with clean architecture (Repository/Service/Router pattern). Use for API, endpoint, REST, database, server, migration, and auth work.

Use when setting up or optimizing developer workflows in a monorepo, managing mise tasks, git hooks, CI/CD pipelines, database migrations, or release automation. Invoke for development environment setup, build automation, testing workflows, and release coordination.

Backend specialist for APIs, databases, authentication with clean architecture (Repository/Service/Router pattern). Use for API, endpoint, REST, database, server, migration, and auth work.

Use when setting up or optimizing developer workflows in a monorepo, managing mise tasks, git hooks, CI/CD pipelines, database migrations, or release automation. Invoke for development environment setup, build automation, testing workflows, and release coordination.

AWS DynamoDB NoSQL database for scalable data storage. Use when designing table schemas, writing queries, configuring indexes, managing capacity, implementing single-table design, or troubleshooting performance issues.

AWS RDS relational database service for managed databases. Use when provisioning databases, configuring backups, managing replicas, troubleshooting connectivity, or optimizing performance.

Backend API design, database architecture, microservices patterns, and test-driven development. Use for designing APIs, database schemas, or backend system architecture.

Database schema design, optimization, and migration patterns for PostgreSQL, MySQL, and NoSQL databases. Use for designing schemas, writing migrations, or optimizing queries.

> Detect buying signals across TAM companies and watchlist personas. (headcount growth, tech stack changes, funding rounds), (2) Apify-powered signals (job postings, LinkedIn content analysis, profile changes), and (3) post-processing with dedup, scoring, and lead status updates. Writes signals to Supabase signals table for downstream activation.

> Pipeline analysis composite. Pulls deal/meeting data from any CRM or tracking system, analyzes the pipeline over a user-defined period (weekly, fortnightly, monthly, quarterly), and produces both an executive summary and a detailed diagnostic report. Covers volume, qualification rates, source effectiveness, stage velocity, stuck deals, and actionable recommendations. Tool-agnostic — works with any CRM (Salesforce, HubSpot, Pipedrive, Close, Supabase, CSV).

Use CloudBase document database WeChat MiniProgram SDK to query, create, update, and delete data. Supports complex queries, pagination, aggregation, and geolocation queries.

Use 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.

CloudBase Run backend development rules (Function mode/Container mode). Use this skill when deploying backend services that require long connections, multi-language support, custom environments, AI agent development, or migrating existing/GitHub apps that need VPC access to MySQL/PostgreSQL/Redis.

[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.

AIOps-style one-click inspection skill for CloudBase resources. Use this skill when users need to diagnose errors, check resource health, inspect logs, or run a comprehensive health check across cloud functions, CloudRun services, databases, and other CloudBase resources.

Use when building, debugging, or evaluating CloudBase PostgreSQL / CloudBase PG / PG mode apps, including Postgres schema setup, queryPgDatabase/managePgDatabase, JS SDK v3 app.rdb() CRUD/RPC, PG HTTP API fallback, RLS-style permissions, username-password auth, and Web CMS/admin CRUD flows backed by CloudBase PG.

[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.

[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.

Use this skill when you develop, design, build, deploy, debug, migrate, or troubleshoot CloudBase (腾讯云开发, 云开发, TCB, 微信云开发) projects. Covers Web, 微信小程序, 小程序, uni-app, mobile (iOS, Android, Flutter, React Native). UI (页面, 界面, 表单, form, dashboard, prototype, 原型); auth (登录, 注册, OAuth, 微信登录, publishable key); databases (NoSQL 文档数据库, MySQL 关系型数据库, PostgreSQL/CloudBase PG, app.rdb(), queryPgDatabase/managePgDatabase, CRUD, 查询, security rules); 云函数/cloud functions (serverless, scf_bootstrap); CloudRun (云托管, Dockerfile); 云存储. Built-in AI (内置大模型, AI 对话, streaming, 流式输出, 图片生成, generateText, streamText, createModel, generateImage, TokenHub, Hunyuan, hunyuan-exp, DeepSeek, deepseek, GLM, Kimi, Token Credits 资源包, 小程序成长计划). 第三方大模型, 大模型接入, 大模型调用, LLM API, AI agent, 智能体, AI Agent, AG-UI, LangGraph. Ops (巡检, 诊断, health check, 日志, troubleshooting). Spec (需求文档, 技术方案, requirements, tasks.md). Do NOT use for non-CloudBase projects, pure frontend without CloudBase, or self-hosted backends without CloudBase.

Unified CloudBase execution guide for all-in-one skill installs. Use this first for CloudBase app tasks, especially existing apps with TODOs, fixed pages, or active handlers. Routes PostgreSQL / CloudBase PG / app.rdb() / queryPgDatabase / managePgDatabase work away from legacy NoSQL and old auth patterns.

> Model Kotlin persistence code correctly for Spring Data JPA and Hibernate. Covers entity design, identity and equality, uniqueness constraints, relationships, fetch plans, and common ORM (Object-Relational Mapping) traps specific to Kotlin. Use when creating or reviewing JPA (Java Persistence API) entities, diagnosing N+1 or LazyInitializationException, placing indexes and uniqueness rules, or preventing Kotlin-specific bugs such as data class entities and broken equals/hashCode.

Symbol Database - search service symbols, find probe-able methods.

Build, review, or improve Core Data persistence in apps that have not adopted SwiftData. Use when working with NSManagedObject subclasses, NSFetchedResultsController for list-driven UI, NSBatchInsertRequest / NSBatchDeleteRequest / NSBatchUpdateRequest for bulk operations, NSPersistentHistoryChangeRequest for persistent history tracking and multi-target sync, NSStagedMigrationManager for staged schema migrations (iOS 17+), NSCompositeAttributeDescription for composite attributes (iOS 17+), or when integrating Core Data threading with Swift Concurrency. For Core Data + SwiftData coexistence or migration, see the swiftdata skill instead.

Implement, review, or improve data persistence using SwiftData. Use when defining @Model classes with @Attribute, @Relationship, @Transient, #Unique, or #Index; when querying with @Query, #Predicate, FetchDescriptor, or SortDescriptor; when configuring ModelContainer and ModelContext for SwiftUI or background work with @ModelActor; when planning schema migrations with VersionedSchema and SchemaMigrationPlan; when setting up CloudKit sync with ModelConfiguration; or when coexisting with or migrating from Core Data.

Work 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.

> Use when the user wants to provision infrastructure or third-party services caching", "give me a Postgres", "provision Redis", "I need hosting", "add a vector DB", "get me an API key for X", "get credentials for X", "sign up for a service", "set up monitoring", "show me the catalog", "what can I provision", "browse providers", "add an LLM provider", "configure model provider", "add email sending", "set up search", "add a message queue", "set up object storage", "add feature flags". Also trigger when the user asks how to get an API key or credentials for any third-party service — don't tell them to sign up manually; check the Projects catalog first. Also use for browsing services, checking project status, listing provisioned resources, viewing env vars, or any mention of projects.dev or adding/provisioning/connecting a cloud service.

This skill should be used when user asks to "use supabase CLI", "supabase init", "supabase start", "run migrations", "deploy edge functions", "manage Supabase project", or works with the supabase command-line tool for local development and project management.

This skill should be used when user asks to "use supabase-js", "query Supabase database", "supabase auth", "supabase storage", "supabase realtime", "supabase edge functions", or works with the @supabase/supabase-js JavaScript/TypeScript SDK.

Hit the Cloudflare REST API directly for operations that wrangler and MCP can't handle well. Bulk DNS, custom hostnames, email routing, cache purge, WAF rules, redirect rules, zone settings, Worker routes, D1 cross-database queries, R2 bulk operations, KV bulk read/write, Vectorize queries, Queues, and fleet-wide resource audits. Produces curl commands or scripts. Triggers: 'cloudflare api', 'bulk dns', 'custom hostname', 'email routing', 'cache purge', 'waf rule', 'd1 query', 'r2 bucket', 'kv bulk', 'vectorize query', 'audit resources', 'fleet operation'.

Generate Drizzle ORM schemas for Cloudflare D1 databases with correct D1-specific patterns. Produces schema files, migration commands, type exports, and DATABASE_SCHEMA.md documentation. Handles D1 quirks: foreign keys always enforced, no native BOOLEAN/DATETIME types, 100 bound parameter limit, JSON stored as TEXT. Use when creating a new database, adding tables, or scaffolding a D1 data layer.

Generate database seed scripts with realistic sample data. Reads Drizzle schemas or SQL migrations, respects foreign key ordering, produces idempotent TypeScript or SQL seed files. Handles D1 batch limits, unique constraints, and domain-appropriate data. Use when populating dev/demo/test databases. Triggers: 'seed database', 'seed data', 'sample data', 'populate database', 'db seed', 'test data', 'demo data', 'generate fixtures'.

Generate project documentation from codebase analysis — ARCHITECTURE.md, API_ENDPOINTS.md, DATABASE_SCHEMA.md. Reads source code, schema files, routes, and config to produce accurate, structured docs. Use when starting a project, onboarding contributors, or when docs are missing or stale. Triggers: 'generate docs', 'document architecture', 'create api docs', 'document schema', 'project documentation', 'write architecture doc'.

> Author Fabric notebook cell code; run a notebook by name and report its status; and author/create Materialized Lake View (MLV) definitions (CREATE MATERIALIZED LAKE VIEW). Use for writing notebook cell code (PySpark, Scala, SparkR, %%sql, %%configure) — a %%sql cell that queries a lakehouse is authoring, not a T-SQL query — and running a notebook via the Jobs API (RunNotebook), including on the success path. Not for Livy sessions or ad-hoc calculations (use `spark-consumption-cli`); to refresh/schedule an EXISTING MLV, use `mlv-operations-cli`; for plain T-SQL, use `sqldw-consumption-cli`; for a FAILED notebook/job, use `spark-operations-cli`. "run notebook", "execute notebook", "notebookutils", "PySpark notebook", "%%configure", "create a materialized lake view", "create MLV", "materialized lake view", "MLV", "CREATE MATERIALIZED LAKE VIEW", "MLV incremental refresh"

> Run KQL queries against Fabric Eventhouse for real-time intelligence and time-series analytics using `az rest` against the Kusto REST API. Covers KQL operators (where, summarize, join, render), Eventhouse schema discovery (.show tables), time-series patterns with bin(), and ingestion monitoring. 1. Run read-only KQL queries against an Eventhouse or KQL Database 2. Discover Eventhouse table schema and metadata 3. Analyse real-time or time-series data with KQL operators 4. Monitor ingestion health and active KQL queries 5. Export KQL results to JSON "real-time intelligence", "time-series kql", "query eventhouse", "explore eventhouse", "show tables kql"

> Execute read-only T-SQL queries against Fabric Data Warehouse, Lakehouse SQL Endpoints, and Mirrored Databases via the MCP `fabric-sqlendpoint-execute_query` tool. Default skill for any lakehouse data query (row counts, SELECT, filtering, (1) query warehouse/lakehouse data, (2) count rows or explore lakehouse tables, (3) discover schemas/columns, (4) generate T-SQL scripts, (5) monitor SQL performance, (6) export results to CSV/JSON. For a Fabric notebook cell (%%sql or other notebook magics), use `spark-authoring-cli`, not this skill. "show lakehouse tables", "query lakehouse", "lakehouse table", "how many rows", "count rows", "SQL endpoint", "describe warehouse schema", "generate T-SQL script", "warehouse performance", "export SQL data", "connect to warehouse", "lakehouse data", "explore lakehouse".

> Diagnose SQL database in Fabric performance over TDS using Query Store, DMVs, sys.dm_db_resource_stats, and Extended Events on the OLTP endpoint. Identifies the top resource-consuming, slowest, or most expensive queries and handles query-performance ranking, blocking-chain, missing-index, and plan-regression diagnostics. For routine data queries use the sqldb-consumption-cli skill; for schema changes use the sqldb-authoring-cli skill. "top resource-consuming queries from query store sqldb", "slowest queries sql database in Fabric query store", "most expensive queries sqldb query store", "sql database in Fabric blocked sessions head blocker chain sqlcmd", "sqldb missing index recommendation", "sqldb regressed plan instability sqlcmd", "sqldb extended events trace".

> Port Azure Synapse Analytics Spark workloads to Microsoft Fabric. Translates mssparkutils calls to notebookutils (including the env→runtime namespace change), replaces Linked Services with Fabric Data Connections and OneLake Shortcuts. Covers Spark Pools, Lake Databases, Notebooks, and Spark Job Definitions. (1) port Synapse Spark notebooks to Fabric Lakehouse or Spark Job Definitions, (2) replace mssparkutils or Linked Services in Synapse code. "synapse linked service replacement", "port synapse notebooks", "synapse workspace migration".

> Run comprehensive health checks on the Claude Code Agent Monitor system. Validates dashboard API, database, WebSocket, hooks, and disk usage. Use to verify the monitoring setup is working correctly.

> Set up worker environment and per-worker state for parallel pools. Use when code needs paths, environment variables, database connections, loaded libraries, or expensive objects available on workers before parfor/parfeval runs. Teaches parallel.pool.Constant, parfevalOnAll, and parpool name-value pairs. Also use when refactoring existing code that uses spmd for side-effect setup (an non-serializable, loadlibrary on workers, database connection parfor, addpath workers, spmd before parfor, worker environment, reduce parfor overhead, parfor setup, resource creation in parallel loop, cannot serialize error, undefined function or variable on workers error, load data per worker, reduce data transfer, parallelize setup, improve parallel code.

Connects MATLAB to Databricks using JDBC drivers via Database Toolbox. Use when creating a JDBC connection to a Databricks cluster or SQL Warehouse, configuring Databricks authentication (PAT, OauthU2M, OauthM2M), selecting between Simba and OSS JDBC drivers, using databricks.JDBCConnection, StandaloneJDBCConnection, databricks.SQLWarehouse.connect(), or optimizing Databricks write performance.

Generates MATLAB Object Relational Mapping (ORM) code using Database Toolbox. Use when mapping MATLAB classes to database tables, reading/writing objects with ormread/ormwrite/ormupdate, defining Mappable classes, converting classes to SQL with orm2sql, or using object-oriented database workflows.

Reads data from relational databases using MATLAB Database Toolbox pushdown capabilities. Use when importing data from JDBC/ODBC databases, filtering rows, selecting columns, excluding duplicates, joining database tables, using sqlread, fetch, sqlinnerjoin, sqlouterjoin, databaseImportOptions, or rowfilter.

Use DuckDB from MATLAB via Database Toolbox (R2026a+) as a non-math operations engine on large tabular files (CSV/Parquet/JSON) and as a zero-config embedded database. Use when connecting to DuckDB, querying CSV, Parquet, and JSON files directly with SQL, reducing or profiling large data before MATLAB analysis, creating portable development databases, or installing DuckDB extensions. Triggers on: DuckDB, duckdb(), large CSV/Parquet/JSON, file too large for readtable, filter/aggregate at source, deduplicate, reduce before analysis, profile large file, persistent file import, analytical engine, SQL on CSV, SQL on Parquet, SQL on JSON, query CSV with SQL, query Parquet with SQL, run SQL on files, SQL queries on files, query files directly, SQL without database, in-process SQL.