Skill UI

This skill provides a comprehensive, production-grade reference architecture for building robust Notion integrations. It defines a multi-layered structure, including client singletons, repository patterns, service orchestration, and caching mechanisms. It is essential for migrating ad-hoc API calls to scalable, event-driven, and highly reliable enterprise applications that incorporate rate limiting, sophisticated error handling, and comprehensive testing strategies.

This skill provides minimal working examples for connecting to Salesforce, executing SOQL queries, and performing full CRUD (Create, Read, Update, Delete) operations on standard sObjects. Examples are provided in both TypeScript and Python. It is ideal for initial Salesforce integration testing, learning core API patterns, or performing basic data manipulation in a Salesforce environment.

This guide provides a minimal, working example of connecting to a Shopify store using the GraphQL Admin API. It demonstrates how to initialize the API client, authenticate the connection, and execute a query to retrieve product and variant details. Ideal for developers starting a new e-commerce integration, testing API connectivity, or learning basic Shopify data retrieval patterns.

An expert playbook for systematically testing Insecure Direct Object References (IDOR) and Broken Object/Function Level Authorization (BOLA/BFLA). This guide covers finding object identifiers across all request vectors (URL, body, headers, cookies), implementing A-B testing methodologies, identifying various ID patterns, and executing horizontal or vertical privilege escalation attacks. Essential for deep API security auditing.

Comprehensive patterns and tools for writing robust tests for F# applications within the modern .NET ecosystem. This guide covers unit testing (xUnit, FsUnit), property-based testing (FsCheck), mocking dependencies (NSubstitute), and full integration testing (WebApplicationFactory). Ideal for improving test coverage, validating complex business logic, and establishing a resilient testing infrastructure.

Expert skill for building Model Context Protocol (MCP) servers and tools from scratch. Covers the full development lifecycle—from defining tool schemas and resources to implementation, testing, and publishing to registries. Supports robust patterns using TypeScript and Python for integrating custom capabilities into AI agents.

This skill provides a demonstration of interacting with external APIs and services using various command-line tools (CLI). It showcases core automation patterns, including fetching API documentation details and listing messages using specific application CLIs, making it ideal for testing and learning API integration flows.

This skill generates a comprehensive and structured developer onboarding document for any service or codebase. It guides new engineers through the entire product lifecycle, covering architectural overview, local development setup, key coding patterns, testing strategies, CI/CD deployment processes, and team contacts, ensuring rapid productivity from day one.

Provides comprehensive guidelines and utilities for modern unit testing using Jest and React Native. Covers Test-Driven Development (TDD) workflows, Behavior-Driven Development (BDD) principles, advanced mocking strategies for modules and GraphQL hooks, and implementing reusable factory functions for props and data. Use this skill to write robust, maintainable, and highly structured unit tests.

This skill generates comprehensive unit and integration tests using Jest for JavaScript and TypeScript. It supports advanced testing patterns, including deep mocking, asynchronous handling, snapshot testing, and testing React components with Testing Library. Use it when the user needs to thoroughly validate application logic and behavior.

A comprehensive strategy for regression testing in AI-assisted development. It focuses on identifying systematic blind spots, such as schema inconsistencies between sandbox and production paths, and integrating automated bug checks into the development workflow. This guide teaches how to build robust, database-free API tests to ensure code quality when AI models contribute to the codebase.

This guide outlines how to build new API connectors or providers that strictly adhere to the existing integration architecture and patterns of a target repository. It is designed for adding integrations while maintaining architectural consistency (e.g., layout, configuration schema, authentication model, and testing style), preventing the creation of novel, unsupported patterns.