MongoDB database exploration and querying. Use when you need to understand database structure, view existing data, check collection schemas, count documents, or run queries to investigate the database state. (project)

Especialista Sênior em MongoDB, Segurança de Dados, Migrations, Backup/Recovery e Data Integrity. Guardian dos dados do Super Cartola Manager com foco em operações seguras, auditoria de schemas, otimização de queries e gestão de lifecycle de dados. Use para migrations, limpeza, manutenção, snapshots, índices, validações e qualquer operação crítica com banco de dados.

Initialize database infrastructure for new projects

Lint PostgreSQL functions against schema, analyze usage, and generate fix reports; use when detecting broken functions, validating schema contracts, or cleaning up unused database functions

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Detect dangerous operations in database migrations before deployment

Generator สำหรับสร้าง database migration files พร้อม up/down scripts, validation และ rollback safety

Monitor database performance and prevent regressions.

PostgreSQL database management with Drizzle ORM, versioned migrations, and type-safe queries. This skill should be used when setting up a new database, writing migrations, managing schemas, or troubleshooting database issues in PostgreSQL projects.

Comprehensive guide to optimizing database queries, understanding EXPLAIN plans, indexing strategies, and eliminating N+1 queries

db-schema-manager

This skill should be used when seeding databases with realistic fake data for development, testing, or staging environments. Supports PostgreSQL, MySQL, SQLite, MongoDB with ORM-based seeding (SQLAlchemy, Django, Prisma) and Faker library for generating realistic test data. Use when the user needs to populate databases with sample data, create test fixtures, or set up development/staging environments with realistic data.

SQLite database management with Prisma ORM, type-safe queries, and Railway deployment with Litestream backup. This skill should be used when creating database schemas, writing migrations, managing SQLite on Railway volumes, or troubleshooting database issues.

Guide for recovering data from SQLite Write-Ahead Log (WAL) files that may be corrupted, encrypted, or inaccessible through standard methods. This skill should be used when tasks involve SQLite database recovery, WAL file analysis, encrypted database files, or discrepancies between tool outputs and filesystem access.

Database migration workflow helper. Use when creating database migrations, modifying SQLAlchemy models, or managing Alembic migrations. Automatically handles model changes, migration creation, and database upgrades.

DBA Admin Agent. 백업, 복구, 보안, 권한 관리를 담당합니다.

DBA Architect Agent. 스키마 설계, 정규화, 데이터 모델링을 담당합니다.

DBA Tuner Agent. 쿼리 최적화, 인덱스 설계, 슬로우 쿼리 분석을 담당합니다.

Toolkit for analyzing Thai legacy DBF accounting databases using roonpoo, Parquet, and DuckDB.

Transform AI agents into experts on dbt project architecture and medallion layer patterns, providing

This skill enables AI agents to help users monitor dbt execution using the

dbt (data build tool) patterns for model organization, incremental strategies, and testing.

Transform AI agents into experts on dbt command-line operations, model selection patterns, Jinja

Guide AI agents through a systematic, user-choice-driven installation process for dbt-core on local

Complete guide for dbt data transformation including models, tests, documentation, incremental builds, macros, packages, and production workflows

PROACTIVE skill - STOP and invoke BEFORE writing dbt SQL. Validates models against coding conventions for staging, integration, and warehouse layers. Covers naming, SQL structure, field conventions, testing, and documentation. CRITICAL - When about to write .sql files in models/, invoke this skill first, write second. Supports project-specific convention overrides and sqlfluff integration.

dbt best practices for models, tests, documentation, and project organization.

Transform AI agents into experts on dbt materializations, providing guidance on choosing the right

Transform Google BigQuery DDL (views, tables, stored procedures) into production-quality dbt models

Transform IBM DB2 DDL (views, tables, stored procedures) into production-quality dbt models

Transform Hive/Spark/Databricks DDL (views, tables, UDFs) into production-quality dbt models

Transform SQL Server/Azure Synapse T-SQL DDL (views, tables, stored procedures) into

Transform Oracle DDL (views, tables, stored procedures, packages) into production-quality dbt models

Transform PostgreSQL/Greenplum/Netezza DDL (views, tables, stored procedures) into

Transform Amazon Redshift DDL (views, tables, stored procedures) into production-quality dbt models

Transform Snowflake DDL (views, tables, stored procedures) into production-quality dbt models,

Transform Sybase IQ DDL (views, tables, stored procedures) into production-quality dbt models

Transform Teradata DDL (views, tables, stored procedures) into production-quality dbt models

Define and enforce validation rules for dbt models during migration. This skill provides

Transform Vertica DDL (views, tables, stored procedures) into production-quality dbt models

Guide AI agents through the complete migration lifecycle from Snowflake or legacy database systems

Create dbt models following FF Analytics Kimball patterns and 2×2 stat model. This skill should be used when creating staging models, core facts/dimensions, or analytical marts. Guides through model creation with proper grain, tests, External Parquet configuration, and per-model YAML documentation using dbt 1.10+ syntax.

Writes, edits, and creates dbt models following best practices. Use when user needs to create new dbt SQL models, update existing models, or convert raw SQL to dbt format. Handles staging, intermediate, and mart models with proper config blocks, CTEs, and documentation.

Transform AI agents into experts on writing production-quality dbt models, providing guidance on CTE

Comprehensive guide to dbt (data build tool) patterns, modeling best practices, testing strategies, and production workflows for modern data transformation

Transform AI agents into experts on dbt and Snowflake performance optimization, providing guidance

Deploy, manage, and monitor dbt projects natively within Snowflake using web-based workspaces,

Complete step-by-step guide for setting up dbt Projects on Snowflake from beginning to end.

Provides expert-level assistance with dbt Semantic Layer, MetricFlow, semantic models, metrics, dimensions, entities, measures, and BI tool integrations. Use this skill when building semantic models, creating metrics (simple, ratio, cumulative, derived, conversion), debugging validation errors, or integrating with BI tools. Extracted from official dbt documentation and optimized for data practitioners.

ALWAYS USE when working with dbt models, SQL transformations, tests, snapshots, or macros. Use IMMEDIATELY when editing dbt_project.yml, profiles.yml, or creating SQL models. MUST be loaded before any transform-layer work. Enforces dbt owns SQL principle - never parse, validate, or transform SQL in Python.