Skill UI

This skill outlines a systematic workflow for addressing review comments and issue feedback on GitHub Pull Requests (PRs) using the GitHub CLI (`gh`). It guides users through inspecting comments, planning fixes, applying code changes, and finally responding to the threads as resolved, ensuring a streamlined code review process.

Streamlines Agencyzoom operations through Composio’s toolkit by driving Rube MCP searches, connection checks, and multi-execute calls before any action, making it ideal for automation engineers embedding CRM tasks into composable workflows.

Automatically detects and loads specialized agent instructions from the `AGENTS.md` file in the current working directory. This skill seamlessly integrates project-specific rules and behavioral guidelines into the AI's session context, ensuring the AI operates according to project standards without manual intervention. It is crucial for maintaining specialized agent behaviors across different development tasks and directory changes.

This skill guides the definition of comprehensive API contracts, serving as the shared interface specification between backend and frontend services. It covers defining request/response schemas, endpoint routes, TypeScript interfaces, and standardized error handling. Use it when establishing, modifying, or documenting the interaction points between different system components to ensure seamless integration and type consistency.

This skill guides the definition of comprehensive API contracts, serving as the shared interface specification between backend and frontend services. It covers defining request/response schemas, endpoint routes, TypeScript interfaces, and standardized error handling. Use it when establishing, modifying, or documenting the interaction points between different system components to ensure seamless integration and type consistency.

Streamline Apitemplate IO tasks by connecting Composio’s toolkit through Rube MCP, always running RUBE_SEARCH_TOOLS to fetch current schemas, validating the Apitemplate IO connection, and executing workflows with RUBE_MULTI_EXECUTE_TOOL.

Automates SOC2 audit preparation by invoking the soc2-audit-helper plugin to gather evidence, assess controls, and flag compliance gaps, streamlining readiness reporting and remediation planning.

A comprehensive guide detailing best practices for creating modern, clean, and highly maintainable user interfaces using Avalonia and Zafiro.Avalonia. It emphasizes leveraging semantic containers, defining shared styles, and utilizing generic components to dramatically reduce XAML redundancy and improve code structure. Learn to adopt patterns like Behaviors over Converters, and prevent deep nesting for optimal developer experience.

A comprehensive SDK providing robust authentication credentials for Python applications interacting with Azure services. It simplifies secure access by supporting multiple credential types, including Managed Identity, Service Principal, and Azure CLI. Features like DefaultAzureCredential allow seamless authentication across local environments, CI/CD pipelines, and various Azure cloud deployments.

A comprehensive SDK providing robust authentication credentials for Python applications interacting with Azure services. It simplifies secure access by supporting multiple credential types, including Managed Identity, Service Principal, and Azure CLI. Features like DefaultAzureCredential allow seamless authentication across local environments, CI/CD pipelines, and various Azure cloud deployments.

This is the Azure Resource Manager SDK for .NET, designed to streamline the deployment and management of Weights & Biases (W&B) ML experiment tracking instances directly through Azure Marketplace. It allows developers to programmatically create, configure, and manage W&B environments, including setting up marketplace details, managing admin users, configuring Single Sign-On (SSO), and ensuring robust ML observability within an Azure ecosystem. Ideal for MLOps pipelines.

This is the Azure Resource Manager SDK for .NET, designed to streamline the deployment and management of Weights & Biases (W&B) ML experiment tracking instances directly through Azure Marketplace. It allows developers to programmatically create, configure, and manage W&B environments, including setting up marketplace details, managing admin users, configuring Single Sign-On (SSO), and ensuring robust ML observability within an Azure ecosystem. Ideal for MLOps pipelines.