Skill UI

A workflow for orchestrating Pushover operations through Composio’s Rube MCP connectors, covering tool discovery, connection validation, schema-aware execution, and recommended API calls for reliable automation.

Automates creation and validation of PWA manifest files for frontend projects, guiding developers through best practices and generating production-ready code snippets and configs for React, Vue, CSS, and modern web ecosystems.

This skill demonstrates the multi-model pattern for creating robust and clean API contracts using Pydantic. It guides the creation of separate models for creation requests (Create), updates (Update), responses (Response), and database documents (InDB), ensuring type safety and clear data validation for Python backends. It also covers best practices like handling camelCase aliases and optional fields.

This skill provides guidance and patterns for generating pairwise test cases. Pairwise testing is an advanced testing technique used in software quality assurance (QA) to ensure that every combination of two input parameters is tested. It is highly effective for complex systems with multiple interacting variables, helping developers improve test coverage efficiently while minimizing the overall number of required test cases.

Automated assistance for pyspark transformer work within data pipelines, activating when you request transformer patterns or best practices and covering ETL, data transformation, orchestration, and streaming scenarios.

This skill auto-activates when someone mentions pytest test generator, offering guided test automation workflows, producing ready-to-use code/configuration, and validating outputs for unit, integration, and mocking scenarios.

Showcases modern Python practices, covering strict 3.12 type hints, dataclasses vs Pydantic decisions, asyncio/httpx patterns, src-layout projects, pyproject tool configs, virtual env workflows, and pytest guidance for reliable codebases.

This skill acts as an expert architect to generate complete, production-ready Python project structures. It supports various modern architectures, including FastAPI, Django (full-stack), standalone libraries, and CLI tools. It ensures adherence to best practices, includes dependency management (uv, pip), type hinting, and comprehensive testing skeletons.

An end-to-end workflow for building robust, production-ready Python backend APIs. This guide covers all stages, including project scaffolding, SQLAlchemy database integration, Pydantic validation, implementing JWT/OAuth2 authentication, comprehensive error handling, unit testing, and Docker deployment for modern microservices.

A concise collection of idiomatic Python best practices—from readability and explicit configuration to type hints, error handling, and context managers—aimed at building robust, maintainable applications.

A comprehensive guide for profiling, analyzing, and optimizing Python code. It addresses identifying performance bottlenecks related to CPU usage, excessive memory consumption, I/O operations, and slow data processing. Use this skill to improve application speed and efficiency in production environments.

A comprehensive skill set for building robust, type-safe, and highly performant Python 3.11+ applications. This specialist enforces best practices by generating type-annotated code, utilizing async/await patterns, structured dataclasses, and comprehensive error handling. The workflow mandates rigorous validation using tools like mypy (strict mode), black, ruff, and extensive pytest test suites to ensure code quality and reliability.