Generates Python scripts to perform daily ETL updates on MySQL databases, combining insert and update operations using UNION ALL logic and parameterized queries for security.
将数据库表定义（如TSV）转换为C#实体类，属性名采用保留下划线的PascalCase命名，自动处理可空类型及[Column]特性注释。
Создает SQL-запросы для базы данных с фиксированной схемой (таблицы QRY_QUEUE, ST_ABONENTS, QRY_TYPE) для анализа статистики обработки сообщений, процента ошибок и данных абонентов.
Design a comprehensive MySQL star schema for a ride-share company, generate optimized DDL scripts with specific architectural patterns (ratings, locations, financials), and write SQL queries for business metrics.
使用火山引擎 RDS MySQL MCP Server，帮助用户完成 RDS MySQL 相关的实例管理、数据库操作、账号管理和运维任务，可直接调用 uv run ./scripts/call_rds_mysql.py 脚本获取实时结果。
Inspect the ServerKit SQLite database — show tables, schemas, row counts, and run queries. Runs via WSL for local dev or SSH MCP for production. Use when debugging data issues or understanding the current database state.
Help users write, validate, and troubleshoot osquery SQL queries using provided osquery table schemas as the authoritative source.
Guides DBeaver usage for database connection management, SQL development, data import/export, and ER diagram generation across MySQL, PostgreSQL, Oracle, MongoDB, and other databases. Use when the user needs to configure DBeaver connections, write queries in the SQL editor, export data, or generate ER diagrams.
Guides PostgreSQL development including table design, indexing, constraints, PL/pgSQL, JSONB, full-text search, window functions, CTEs, EXPLAIN ANALYZE tuning, backup/restore, replication, and extensions like pgvector. Use when the user needs to write or optimize PostgreSQL queries, design schemas, or manage PostgreSQL databases.
Guides Oracle database development including SQL, PL/SQL stored procedures, triggers, EXPLAIN PLAN optimization, AWR analysis, RMAN backup, RAC clustering, and Data Guard. Use when the user needs to write Oracle SQL, create PL/SQL procedures, tune query performance, or manage Oracle database administration.
Use this skill when the user says 'add schema', 'schema markup', 'JSON-LD', 'structured data', 'rich results', 'rich snippets', or is adding or fixing schema.org structured data for better search result appearance. Do NOT use for meta tag optimization or full SEO audits.
Use this skill when the user says 'design database', 'create tables', 'database schema', 'add table', 'database architect', 'ERD', 'data model', or is designing Supabase/Postgres table structures, relationships, RLS policies, or migrations. Do NOT use for schema migration of existing tables (use migration-planner) or code refactoring.
PostgreSQL and Drizzle ORM best practices. Triggers on: PostgreSQL, Postgres, Drizzle, database, schema, tables, columns, indexes, queries, migrations, ORM, relations, joins, transactions, SQL, drizzle-kit, connection pooling, N+1, JSONB, RLS. Use when: writing database schemas, queries, migrations, or any database-related code. Proactively apply when creating APIs, backends, or data models.
Expert guide for Drizzle ORM best practices, including schema definitions, queries, mutations, transactions, migrations, and performance optimization. Use when working with Drizzle ORM, database schemas, queries, or migrations.
Validate NoSQL injection vulnerabilities across MongoDB, Cassandra, CouchDB, Redis, and other NoSQL databases. Test operator injection, JavaScript injection, and query manipulation patterns. Use when testing CWE-943 (Improper Neutralization of Special Elements in Data Query Logic) and related NoSQL injection classes.
Validate SQL injection vulnerabilities (including blind SQLi) across time-based, error-based, boolean-based, UNION-based, stacked-query, and out-of-band patterns. Use when testing CWE-89 (SQL Injection), CWE-564 (Hibernate SQL Injection), and related SQL injection classes across MySQL, PostgreSQL, MSSQL, Oracle, and SQLite targets.
Execute read-only T-SQL queries against Fabric Data Warehouse, Lakehouse SQL Endpoints, and Mirrored Databases via CLI. Default skill for any lakehouse data query (row counts, SELECT, filtering, aggregation) unless the user explicitly requests PySpark or Spark DataFrames. Use when the user wants to: (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. Triggers: "warehouse", "SQL query", "T-SQL", "query warehouse", "show warehouse tables", "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".
Use when designing database schemas, implementing repository patterns, writing optimized queries, managing migrations, or working with indexes and transactions for SQL/NoSQL databases.
