Skill UI

The RunAPI CLI serves as the dedicated execution layer for interacting with RunAPI's suite of advanced AI models. It allows developers to programmatically generate multimodal content, including images, videos, and music/audio, directly from the terminal or CI/CD pipeline. Users can submit one-off jobs, manage asynchronous tasks, and pass complex JSON request bodies to various services like Suno, Claude, and Gemini, making it essential for robust AI automation workflows.

Configure the maximum allowed size of the Webiny API Lambda bundle using the Infra.Api.MaxBundleSize extension in webiny.config.tsx. Use when the API bundle exceeds the default 4.5 MB limit or to enforce a stricter limit. Covers extension syntax, byte calculations, and interpreting build error messages.

A powerful CLI tool designed for deep web interaction and data scraping. bb-browser simulates genuine user behavior within a real browser environment, allowing access to both public and private enterprise systems, even requiring active user login. Key features include a Site System to turn complex websites into simple command-line APIs, advanced network request handling (with session state), form automation, and element interaction, making it ideal for enterprise data gathering and workflow automation.

This skill provides comprehensive operational capabilities for managing FrontendExtension resources in a Kubernetes cluster. It covers the entire lifecycle, including resource creation, package generation, artifact inspection, publishing, unpublishing, deletion, and deep debugging of package and publish controller behaviors. Use it for troubleshooting FE status, managing deployment pipelines, or interacting with the FE API.

A comprehensive guide to using the GitHub CLI (`gh`) to automate development workflows. This skill enables users to programmatically manage issues (create, list, comment), review pull requests (check status, merge), monitor CI/CD runs, and query data via the GitHub API, significantly streamlining the entire development lifecycle from the terminal.

This comprehensive skill provides expert guidance for integrating with Google NotebookLM programmatically, covering both the `nlm` Command Line Interface (CLI) and Measurement/Control Plane (MCP) tools. Use it to automate workflows, manage notebooks, add diverse sources (URLs, YouTube, Drive), and generate various complex content types, such as reports, quizzes, mind maps, and videos. Essential for developers building upon NotebookLM's advanced research capabilities.

An internal tool designed to validate candidate meta-skills (SKILL.md) before they are registered in the system. It performs comprehensive linting checks, including structural validation (G1: parsing, reference checks, XML escaping) and a scheduler dry-run (G2) using stub executors, ensuring the skill is deterministic and functional. Output diagnostics in JSON format.

This utility provides an extension point to inject custom query parameters into all live preview URLs generated by the Website Builder. Developers can use this to automatically append necessary data, such as signed tokens, tenant identifiers, or feature flags, ensuring the preview environment is fully configured for specific use cases. It supports complex async operations like fetching tokens from remote APIs.

This module provides comprehensive abstractions, data types, and utilities for managing webhooks. It includes interfaces for defining payloads, tracking delivery status, dispatching events, and verifying incoming webhooks, enabling robust event-driven integration between services.

A comprehensive guide for using C# scripting within Tabular Editor to programmatically manipulate and automate Power BI semantic models. This skill supports bulk metadata operations, creating calculation groups, managing security roles, and advanced data model restructuring using the TOM API and LINQ.

This guide details the use of Deneb, a certified custom visual, to embed Vega and Vega-Lite declarative visualization specifications directly into Power BI reports (PBIR). It outlines the complete workflow, from data binding and spec authoring to injection and validation using the pbir CLI. It emphasizes best practices, such as preferring Vega-Lite and handling complex field name escaping for robust report mutation.

This guide establishes best practices for code instrumentation and system observability. Learn how to implement structured logging, metrics (RED), and distributed tracing to make production behavior fully diagnosable. It is essential for any feature deployed live, ensuring rapid incident response and deep system understanding.