Find the perfect capability for your agent.
Convert Python code to idiomatic Roc. Use when migrating Python projects to Roc, translating Python patterns to idiomatic Roc, or refactoring Python codebases for type safety, functional purity, and native performance. Extends meta-convert-dev with Python-to-Roc specific patterns.
Builds enterprise Django applications with DRF, ORM optimization, async views, and Celery tasks. Use when creating Python web apps, REST APIs, or full-stack Django projects.
Build production-ready web applications with Django MVC, ORM, authentication, and REST APIs
Build robust backend systems with modern technologies (Node.js, Python, Go, Rust), frameworks (NestJS, FastAPI, Django), databases (PostgreSQL, MongoDB, Redis), APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), testing strategies, security best practices (OWASP Top 10), performance optimization, scalability patterns (microservices, caching, sharding), DevOps practices (Docker, Kubernetes, CI/CD), and monitoring. Use when designing APIs, implementing authentication, optimizing database queries, setting up CI/CD pipelines, handling security vulnerabilities, building microservices, or developing production-ready backend systems.
Expert-level Django development for robust Python web applications with ORM, admin, and authentication
Build, review, and refactor Python backend services. Use for tasks like FastAPI/Flask service setup, API design, request/response schemas, database access (PostgreSQL), migrations, background jobs, observability (logging/metrics/tracing), authentication, configuration, testing, and production hardening.
Deploy Python Flask applications to Railway.app with environment configuration and webhook setup
Build robust backend systems with modern technologies (Node.js, Python, Go, Rust), frameworks (NestJS, FastAPI, Django), databases (PostgreSQL, MongoDB, Redis), APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), testing strategies, security best practices (OWASP Top 10), performance optimization, scalability patterns (microservices, caching, sharding), DevOps practices (Docker, Kubernetes, CI/CD), and monitoring. Use when designing APIs, implementing authentication, optimizing database queries, setting up CI/CD pipelines, handling security vulnerabilities, building microservices, or developing production-ready backend systems.
Bootstrap Python projects with virtual environments and modern tooling. Use when creating Python applications, scripts, APIs, data science projects, or when the user wants a properly configured Python development environment.
Manage Python virtual environments and dependencies. Use when setting up projects or installing packages.
Manages HEC-HMS version differences (3.x vs 4.x), handles Python 2/3 compatibility, detects HMS installations, and generates version-appropriate Jython scripts. Use when working with legacy HMS 3.x projects, upgrading models from 3.x to 4.x, testing across multiple HMS versions, or troubleshooting version-specific issues. Handles 32-bit vs 64-bit architecture differences, memory limits, and script syntax variations. Trigger keywords: HMS version, HMS 3.x, HMS 4.x, legacy, upgrade, Python 2 compatible, 32-bit, 64-bit, version detection, multi-version testing.
Manage Python projects and dependencies using UV, the ultra-fast Rust-based package manager. Use when creating Python projects, managing dependencies, or running Python scripts.
Convert Python code to Elixir. Use when migrating Python projects to Elixir, translating Python patterns to idiomatic Elixir, refactoring Python codebases into BEAM/OTP, or building Elixir services from Python prototypes. Extends meta-convert-dev with Python-to-Elixir specific patterns covering all 10 pillars including FFI and Dev Workflow.
Python-specific library/package development patterns. Use when creating Python packages, designing public APIs with Python idioms, configuring pyproject.toml, managing dependencies with uv, publishing to PyPI, or writing package documentation. Extends meta-library-dev with Python tooling and ecosystem practices.
Generate production-ready Python project skeletons with Astral UV package manager, Hatchling build backend with dynamic versioning, bump-my-version for semantic version management, and modern tooling (ruff, mypy, pytest, bandit). Use when creating new Python projects, initializing Python packages, setting up src-layout projects, scaffolding Python libraries, or starting a new Python application. Supports Python 3.14+ by default with configurable version. Output to current directory, specified path, or tarball.
Manages Python environments, dependencies, and tools using uv and ruff. PROACTIVELY Use when setting up Python projects, adding dependencies, managing virtual environments, or enforcing code quality standards. MUST Use `uv` for all operations.
Set up Python project for PyPI publishing with pyproject.toml, src layout, and build scripts. Use when creating a new Python package, setting up for PyPI distribution, or initializing a Python library project.
