Skill UI

This module provides core use case abstractions for interacting with a standardized language catalog. It offers two key functionalities: retrieving a specific language entry using its unique code (GetLanguageByCodeUseCase), and listing all available language entries (ListLanguagesUseCase). These abstractions ensure consistent and structured data access across the application.

This module provides multiple standardized abstractions for interacting with the OpenSearch API. It includes dedicated abstractions for clients, factories, and operational contexts, allowing developers to implement robust, scalable, and type-safe interactions with OpenSearch within the webiny ecosystem. Use these patterns to ensure consistent data access regardless of the specific usage pattern.

This comprehensive catalog provides essential abstractions for securing web APIs. It covers the entire lifecycle of authentication, including handling roles, users, and teams. Features include robust mechanisms for managing API keys (before/after events) and dedicated use cases for user and role creation/deletion, ensuring highly scalable and secure backend development.

A comprehensive API catalog for managing the entire tenant lifecycle in multi-tenant applications. It provides structured abstractions, including Use Cases, Repositories, and various Event Handlers (before/after hooks), facilitating the creation of robust, scalable, and loosely coupled multi-tenant backend systems.

This module provides comprehensive API abstractions and use cases for managing multi-tenant systems. It covers the entire tenant lifecycle, including creation, installation, enabling, disabling, retrieval by ID, and model extension. Developers can use these structured patterns to ensure robust, scalable, and maintainable multi-tenancy implementation within their services.

This tool allows users to execute complex, multi-agent workflows defined in YAML format directly within the AI environment. It functions as an orchestration engine, sequentially invoking various specialized roles (agents) without requiring manual API key configuration. Ideal for structured tasks requiring collaboration between multiple personas.

This comprehensive checklist provides guidance for integrating Workhuman Social Recognition and its rewards API into production environments. It covers essential API concepts, including OAuth 2.0 authentication, peer-to-peer recognition workflows, configurable award levels, and bidirectional synchronization with major HRIS systems (e.g., Workday, SAP). Use this resource when building robust and scalable employee recognition or reward management systems.

This guide provides the reference architecture for integrating Workhuman's Social Recognition and rewards APIs. It details key concepts like OAuth 2.0, peer-to-peer recognition, and configurable awards. Use this skill when building complex employee recognition workflows, syncing data bi-directionally with HRIS systems (e.g., Workday, SAP), or integrating with collaboration tools like Slack and Microsoft Teams.

A comprehensive guide for building, reviewing, and optimizing GraphQL APIs in Golang. This skill covers best practices including robust schema design (nullability, pagination), proper resolver separation, and critical performance optimizations like using DataLoaders to prevent N+1 query issues. Ideal for engineers working with gqlgen or graphql-go.

This skill analyzes raw git diffs or change descriptions to generate clear, story-driven commit messages. It strictly adheres to the Conventional Commits specification, ensuring that messages thoroughly explain *why* a change was made, not just *what* changed. It is ideal for writing meaningful commit history, preparing pull requests, or summarizing complex code changes for team collaboration.

This skill guides the design and optimization of production-grade multi-agent systems. It specializes in using LangGraph, LangChain, and DeepAgents to create structured, complex AI workflows. It covers state management, agent routing, memory integration, and tool calling, making it ideal for building robust, scalable agents like supervisors, planners, and coders.

This guide outlines architectural patterns and best practices for building scalable, production-ready APIs with FastAPI. It covers proper project structure, separating concerns using routers, services, and CRUD layers, implementing dependency injection for database sessions and authentication, defining Pydantic schemas for request/response, handling asynchronous operations, and managing custom error responses.