Modern Python development with 3.12+, async patterns, FastAPI/Django scaffolding, testing, packaging, and production best practices including observability, resilience, and code quality tooling

Develop production-ready Python backends with async patterns, modern frameworks (FastAPI, Django), and best practices for architecture, testing, and performance.

Build production-ready Python backends with async patterns, FastAPI/Django architecture, testing strategies, and modern tooling (uv, ruff, pydantic). Guides framework selection, project structure, and performance optimization for Python 3.12+ applications. Includes async web APIs, microservices, real-time systems, and TDD workflows.

Write correct, Pythonic code using patterns for syntax, concurrency, networking, databases, ML/LLM, and HPC, with automatic style enforcement for readability.

Audit database connections, calculate optimal pool sizes, and configure or deploy PgBouncer/ProxySQL for PostgreSQL and MySQL to prevent connection exhaustion and improve throughput.

Run database tests with fixture loading, automatic transaction rollback, schema validation, and realistic test data generation across PostgreSQL, MySQL, MongoDB, SQLite, and Redis, including ORM support for Prisma, TypeORM, SQLAlchemy, Knex, and Drizzle.

Generate ORM models, migrations, and relationships from database schemas or natural language requirements across Prisma, TypeORM, SQLAlchemy, Django ORM, Drizzle, and more.

Provides 70+ slash commands organized across 12 development phases, with specialized agents for Rust, Python, Go, JavaScript, Bash, FastAPI, documentation, performance analysis, real-time systems, and subagent creation, plus a Dagger-based safety system and multi-dimensional validation.

Manage every aspect of PostHog: run product analytics, feature flags, A/B experiments, session replays, error tracking, and LLM/APM observability. Build dashboards, alerts, and automated monitors; review code for correctness and performance; connect external data sources via REST/SQL connectors. Includes a MCP server and Claude Code session capture.

Manage the full data engineering lifecycle: design database schemas, generate zero-downtime migrations, build ETL/ELT pipelines, optimize slow queries, and monitor data freshness, schema drift, and pipeline status across PostgreSQL, BigQuery, Snowflake, and dbt/Airflow.

Generate zero-downtime database migrations with forward SQL, rollback SQL, and deployment sequences for schema changes like adding columns, renaming tables, or dropping columns.

Perform a full database health check and schema reconnaissance. Inventory schemas, migrations, data volumes, backups, connection pools, and query patterns across projects using Prisma, Drizzle, or SQLAlchemy.

Design and build database schemas from natural-language domain descriptions. Input a description of your data model and receive DDL, ORM configs, or migration files tailored to your stack (Prisma, Drizzle, SQLAlchemy, or raw PostgreSQL).

Provides a disciplined workflow for Python application development: from requirements discovery and domain modeling through implementation, TDD, code review, and PR management. Comes with specialized skills for FastAPI, SQLAlchemy, Temporal, Python 3.10+ patterns, and monorepo configuration.

Review Python/FastAPI backend code with automated checks for SQLAlchemy ORM patterns, PostgreSQL query optimization, pytest test quality, and type safety, using a multi-step verification protocol to eliminate false positives

Build, validate, and migrate data pipelines with ETL reliability patterns, data profiling, quality checks, schema inference, and synthetic test data generation for CSV, Parquet, JSON, and SQL databases.

Streamlines Python development with automated workflows for building Django and FastAPI apps, managing SQLAlchemy databases with Alembic, writing pytest tests, debugging errors, and enforcing code quality.

Standardize Python project setup, modern coding conventions, SQLAlchemy 2.0 ORM patterns, and pytest testing practices. Use shared skills for dependency management, linting/typing with ruff and mypy, writing type-hinted Python with dataclasses and match statements, plus fixture-based test suites with coverage.

Scaffold and manage Flask applications with app factory, Blueprints, views, authentication, validation, and Jinja2 templates or JSON APIs. Automate database migrations with Flask-Migrate, review SQL, and enforce data governance through automated classification and lineage checks after file edits.

Generates FastAPI backends with Pydantic v2 schemas, SQLAlchemy async sessions, JWT auth, and OpenAPI contracts, then manages Alembic migrations and data lineage checks.

Develop, test, and debug InterSystems IRIS applications with ObjectScript and Python — compile code, run SQL queries, manage interoperability productions, configure Docker containers, measure test coverage, and navigate codebases via MCP tools.

Build and configure Litestar web applications with opinionated patterns for routing, DTOs, authentication, dependency injection, HTMX, Inertia.js, real-time features, API endpoints exposed as MCP tools, background task queues (SAQ, Litestar QueuePlugin), and production deployment on Docker, Kubernetes, or Cloud Run

Generate ORM models and database schemas bidirectionally between models and databases, supporting multiple frameworks like Prisma, SQLAlchemy, Django, Rails, Laravel, TypeORM, and Entity Framework across Python and TypeScript.

Implement and optimize database connection pooling across languages (Python, Java, Node.js) with guidance on pool size, timeouts, and lifecycle settings for improved performance.

Enforces Clean Architecture in Python/FastAPI projects with layered scaffolding, design rule scanning, code smell detection, and automated refactoring plans. Scaffolds endpoints, reviews REST APIs, generates stub-based tests, and teaches Pythonic patterns.

Generate backend API projects with FastAPI or Django, including async routes, Pydantic schemas, GraphQL schemas, and microservices architecture. Get expert guidance for API design, federation, and performance optimization.

Accelerate Python backend development with production-ready patterns for FastAPI, SQLAlchemy, Redis, and Pydantic, plus code quality (Ruff, ty), observability, error handling, git commit generation, and Excalidraw diagramming.