Skill UI

This comprehensive skill guides users through the entire lifecycle of developing AI agent skills. From initial concept (Understand) to structured design, writing the core skill logic, rigorous testing, and final performance optimization. It ensures skills are robust, accurate, and highly triggerable across any domain, including engineering, content creation, and research.

This comprehensive benchmark suite evaluates the performance of Vision-Language Models (VLM) on complex video anomaly detection tasks using the SmartHome-Bench dataset. It covers a wide range of real-world smart home scenarios, including security breaches, elderly fall detection, and unusual pet behavior. Unlike single-frame analysis, this benchmark requires deep multi-frame video understanding, making it a robust tool for testing the advanced AI capabilities of video monitoring systems.

This skill provides Continuous Integration (CI) testing for applications built with WebContainer and StackBlitz SDK. It utilizes Playwright browser tests to ensure code reliability and functional correctness across various environments. Ideal for developers integrating WebContainers into CI pipelines or working within the StackBlitz development ecosystem.

This guide establishes a comprehensive development loop for automating TechSmith's screen capture and video editing processes (Snagit/Camtasia). It outlines the required project structure, includes PowerShell scripting examples, and details how to use Pester testing framework for robust COM API connection testing, ensuring reliable automation workflows.

Automate the Continuous Integration and Continuous Deployment (CI/CD) workflow for applications leveraging Together AI's OpenAI-compatible API. This skill sets up comprehensive GitHub Actions workflows to run unit tests (mocking completions and embeddings) on every Pull Request, and full integration tests (live API calls) upon merging to the main branch. It ensures robust validation of prompt formatting, model selection, and API connectivity for various LLMs.

This guide outlines best practices and structured workflows for automating complex tasks within the Unity Editor using the Model Context Protocol (MCP). It provides programmatic methods for managing GameObjects, applying script edits, analyzing the editor state, executing batch commands, and capturing detailed, multi-view screenshots. Ideal for developers needing robust, repeatable, and automated testing or operational tasks within Unity projects.

This comprehensive guide teaches how to use httpYac in VSCode to convert API documentation (like Swagger or Postman collections) into robust, executable .http files. It provides advanced workflow guidance for implementing complex authentication schemes (OAuth2, Bearer), managing multi-environment configurations, chaining requests using response data, and integrating testing workflows with CI/CD pipelines for professional-grade API automation.

This comprehensive guide serves as a decision flowchart for developers using Claude Code. It details when to use specific slash commands (/plan, /compact, /loop), select specialized agents (planner, code-reviewer, build-error-resolver), or invoke workflow skills (e2e-testing, tdd-workflow). It helps users select the optimal tool combination for complex tasks across the entire software development lifecycle, including feature planning, bug fixing, testing, and architectural design.

Captures complete, full-page screenshots of any modern web page using the Chrome DevTools Protocol (CDP). Unlike standard tools, this utility handles Single Page Applications (SPAs), scrolling content, lazy-loaded images, and complex scroll containers, ensuring the entire visible content is captured in a single, high-fidelity PNG file. Ideal for documenting complex web flows and testing.

KubeStellar Console is an open-source, multi-cluster Kubernetes dashboard (CNCF project) that provides advanced operational capabilities powered by an MCP server and built-in AI agent skills. It helps manage complex deployments across edge and cloud environments, offering AI-assisted troubleshooting, performance testing, and deep integration with major CNCF projects like Argo and Istio.

Squirrel is an advanced, full-cycle AI coding skill designed to guide software development from concept to production. It automatically detects the current project state (greenfield, in-progress, or mature) and adapts an 8-phase engineering pipeline. This comprehensive workflow covers planning, building, rigorous testing, bug hunting, code polishing, documentation, and deployment readiness, ensuring high-quality, maintainable results.

A systematic, multi-phase guide for tackling hard bugs and performance regressions. It mandates building a robust feedback loop (e.g., failing tests, harnesses), formulating falsifiable hypotheses, targeted instrumentation, and rigorous regression testing. This methodology ensures that debugging efforts are structured, efficient, and highly reproducible, making it ideal for complex software defects.