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Found 705 Skills
Python backend implementation patterns for FastAPI applications with SQLAlchemy 2.0, Pydantic v2, and async patterns. Use during the implementation phase when creating or modifying FastAPI endpoints, Pydantic models, SQLAlchemy models, service layers, or repository classes. Covers async session management, dependency injection via Depends(), layered error handling, and Alembic migrations. Does NOT cover testing (use pytest-patterns), deployment (use deployment-pipeline), or FastAPI framework mechanics like middleware and WebSockets (use fastapi-patterns).
Manage serverless MySQL databases and branches on PlanetScale
JOOQ type-safe SQL patterns - use for database queries, repositories, complex SQL operations, and PostgreSQL-specific features
Alembic migration patterns for SQLAlchemy 2.0 async. Use when creating database migrations, managing schema versions, handling zero-downtime deployments, or implementing reversible database changes.
Guides the agent through async database integration with SQLAlchemy and Alembic migrations for FastAPI applications. Triggered when users ask to "set up a database", "create database models", "add SQLAlchemy", "create migrations", "run Alembic", "connect to PostgreSQL", "add a database layer", "create CRUD operations", "set up async database", or mention SQLAlchemy, Alembic, ORM, database models, async database, connection pool, or database migrations.
Creates dbt models following project conventions. Use when working with dbt models for: (1) Creating new models (any layer - discovers project's naming conventions first) (2) Task mentions "create", "build", "add", "write", "new", or "implement" with model, table, or SQL (3) Modifying existing model logic, columns, joins, or transformations (4) Implementing a model from schema.yml specs or expected output requirements Discovers project conventions before writing. Runs dbt build (not just compile) to verify.
Optimizes Snowflake query performance using query ID from history. Use when optimizing Snowflake queries for: (1) User provides a Snowflake query_id (UUID format) to analyze or optimize (2) Task mentions "slow query", "optimize", "query history", or "query profile" with a query ID (3) Analyzing query performance metrics - bytes scanned, spillage, partition pruning (4) User references a previously run query that needs optimization Fetches query profile, identifies bottlenecks, returns optimized SQL with expected improvements.
Optimizes Snowflake SQL query performance from provided query text. Use when optimizing Snowflake SQL for: (1) User provides or pastes a SQL query and asks to optimize, tune, or improve it (2) Task mentions "slow query", "make faster", "improve performance", "optimize SQL", or "query tuning" (3) Reviewing SQL for performance anti-patterns (function on filter column, implicit joins, etc.) (4) User asks why a query is slow or how to speed it up
Python full-stack with FastAPI, React, PostgreSQL, and Docker.
Creates custom Docker-based State Transition Functions (STFs) for D6E platform workflows. Use when building containerized business logic for D6E, implementing data processing steps, or creating workflow functions that need database access. Handles JSON input/output, SQL API integration, and multi-language implementations (Python, Node.js, Go).
Develops resources for FiveM using the QBCore Framework. Covers resource creation, Core Object usage, Player management, Callbacks, Events, Items, Jobs, Gangs, Database (oxmysql), and best practices. Use when the user works with FiveM, QBCore, Lua scripts for QBCore servers, or mentions `QBCore.Functions`, `GetCoreObject`, `CitizenID`, or any system of the QBCore Framework.
This skill should be used when working with Bun runtime, bun:sqlite, Bun.serve, bun:test, or when "Bun", "bun:test", or Bun-specific patterns are mentioned.