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.

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.

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

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

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

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 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.

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

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.

Transform AI agents into experts on dbt testing strategies, providing guidance on implementing

dbt with TD Trino. Covers profiles.yml setup (method:none, user:TD_API_KEY), required override macros (no CREATE VIEW), TD_INTERVAL in models, and TD Workflow deployment.

Generate presentation layer components using routing-controllers with NestJS-style decorators, class-validator for validation, and automatic Swagger documentation.

Apply Domain-Driven Design, Clean Architecture, CQRS, and command/query patterns to code reviews and feature design. Use when analyzing or designing code in Application, Service, Infrastructure, DataAccess, Validation, Domain, or Functions projects, or when addressing architectural concerns, layering, mapping, entities, value objects, repositories, or validators in the Rome Repair Order Service.

Generate complete Domain-Driven Design bounded contexts with all 4 architectural layers (Domain, Application, Infrastructure, Presentation) for Bun.js + Express + routing-controllers backend applicati

Analyzes and refactors code using Domain-Driven Design principles. Use when refactoring domain models, identifying DDD anti-patterns, improving domain clarity, or applying tactical/strategic DDD patterns.

Créer des tests exhaustifs pour les bounded contexts DDD suivant une approche TDD (Test-Driven Development) avec des standards de coverage stricts.

Generate comprehensive investment memos for cyber•Fund investment committee decisions. Use when creating DD memos or investment analysis documents.

Conduct authorized denial of service testing to assess network resilience and configure intrusion detection systems (IDS) to detect and alert on various DoS attack patterns. This skill covers volume-b

Remove AI-generated artifacts from documents, including tool names, boilerplate patterns, over-structuring.

Build trading systems in the style of D.E. Shaw, the pioneering computational finance firm. Emphasizes systematic strategies, rigorous quantitative research, and world-class technology infrastructure. Use when building research platforms, systematic trading strategies, or quantitative finance infrastructure.

This skill should be used to remove AI-generated artifacts and unnecessary code before committing. It scans changed files for redundant comments, AI TODOs, excessive docstrings, and unnecessary markdown files. Git-only, no GitHub required.

Apply DE2 Factorization to separate multiplicative components to understand relative contribution of each factor.

Identify and remove unused code, commented blocks, unreachable code, and unused imports. This skill should be used during Phase 1 cleanup tasks to improve codebase maintainability.

Scoring system for opportunity hygiene, win likelihood, and inspection

Review active deals and surface risks

Win competitive rounds: run a clean process, deliver value previews before asking, coordinate partners, and manage timelines. Use when you're trying to close a 'must win' deal against other funds.