Skill UI

This skill validates the correctness and completeness of any API request. It rigorously checks the HTTP method, URL structure, required headers, body format, authentication details, and query parameters. Use it to debug cURL commands, verify endpoint calls, or ensure API structures adhere to best practices, providing immediate, actionable feedback on required fixes.

A comprehensive, four-phase methodology for debugging complex software issues. It guides users through root cause investigation, pattern analysis, hypothesis testing, and controlled implementation. The core principle emphasizes finding the underlying cause rather than applying quick, symptomatic fixes, ensuring robust and reliable code improvements across various failure scenarios.

This powerful skill provides an interactive debugger interface, allowing developers to set breakpoints, step through source code line by line, inspect live variable states, and trace the entire call stack. It is essential for diagnosing complex bugs, understanding unexpected program flow, or determining the root cause when standard logging fails. It supports various languages via the DAP protocol.

This skill manages the end-to-end lifecycle of cloud test execution using HyperExecute for platforms like LambdaTest/TestMu AI. It provides comprehensive functionality to analyze projects, generate and validate complex YAML configurations, run CLI jobs, and debug failures. Ideal for integrating robust, scalable, and matrix-based testing into CI/CD pipelines.

This utility detects and redacts sensitive personally identifiable information (PII) from screenshots and images. It can mask various data types, including IDs, credit card numbers, API keys, phone numbers, and emails. Crucially, all processing is conducted locally and offline, ensuring that sensitive data never leaves the user's machine. It is essential for secure debugging, data sharing, and analysis workflows.

A meta-skill designed to capture hard-won, non-obvious knowledge—such as complex debugging steps, multi-step operational workflows (e.g., deployment, migrations), or project-specific facts—and convert them into reusable Agent Skills. It ensures that future sessions, regardless of the user or team member, start with proven procedures, preventing the need to rediscover solutions from scratch.

This skill analyzes raw WordPress Site Health reports (text, screenshots, or debug info). It converts diagnostic data into a prioritized, risk-tiered fix plan, identifying critical issues and recommended improvements. It provides exact, safe commands (WP-CLI/PHP snippets) necessary to address the site and strictly enforces safety protocols like backups, lint checks, and dry-runs.

This guide provides a comprehensive reference on Agent Skills, a standardized and powerful method for providing structured context, workflows, and tools to Large Language Models (LLMs). It details the anatomy of the SKILL.md file, the three-stage progressive disclosure loading process, and best practices for creating both capability (tool-based) and process (workflow-based) primitives. Use this resource when designing, reviewing, or debugging agent-driven applications to ensure reliable and context-efficient performance.

This skill provides a systematic, four-phase workflow for diagnosing and recovering from AI agent failures. It guides the agent to first capture the failure state, diagnose the root cause (e.g., loop, context overflow, state drift), apply minimal containment recovery actions, and finally generate a detailed introspection report, minimizing the need for human intervention.

This technique audits user interaction flows (like button clicks) to detect subtle bugs that static analysis often misses. It specifically tracks the full sequence of state changes, identifying instances where later actions unintentionally cancel, contradict, or leave the UI in an inconsistent state due to state store side effects or race conditions. Ideal for debugging complex state management architectures (Redux, Zustand).

This skill analyzes completed code sessions to automatically extract reusable patterns, transforming raw interaction data into structured, learned skills. It allows the AI to continuously improve its domain knowledge by capturing techniques like error resolution, debugging methods, and workarounds, making it effective for advanced coding assistance and knowledge curation.

This guide outlines a comprehensive, agent-focused testing workflow for modern C++ (C++17/20) using GoogleTest and CMake/CTest. It teaches key concepts like the TDD cycle (Red-Green-Refactor), utilizing mocking (gmock) and fixtures for isolation, setting up robust CI/CD integration, and debugging complex issues using sanitizers (ASAN, TSAN) and coverage analysis. Use this when writing, updating, or ensuring the functional robustness of C++ codebases.