Skill UI

This skill provides an autonomous, repository-wide pipeline for auditing and fixing complex logical inconsistencies. It systematically executes stages including health checks, deep review, locating divergences, generating fixes, and performing diff-verification until the codebase is deemed clean. Ideal for maintaining high code quality and resolving deep, systemic bugs across entire projects.

This skill is designed to identify subtle and complex logic bugs within a single code file or function. It employs a semi-formal execution tracing methodology, systematically breaking down the suspected bug into five stages: Premises (expected state), Trace (execution path), Divergence (actual deviation), Trigger (reproduction case), and Remedy (fix). It is ideal for scenarios where users suspect code is wrong but cannot specify a concrete failure point.

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 applies rigorous, F1-inspired standards to software quality assurance. It executes comprehensive test plans, classifies failures across five critical tiers (Brake, Engine, Aero, Tyre, Pit), audits Continuous Integration (CI) signal integrity, and generates detailed WJTTC reports. Use it to validate functionality, reproduce complex bugs, or determine the readiness of a release.

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 acts as a critical reality-check layer before code generation. It ensures that every function call, import, method, and variable the agent uses is provably available in the user's specific installed environment and version. It prevents runtime bugs caused by hallucinated or improperly assumed APIs, making the generated code robust and reliable.

A comprehensive strategy for regression testing in AI-assisted development. It focuses on identifying systematic blind spots, such as schema inconsistencies between sandbox and production paths, and integrating automated bug checks into the development workflow. This guide teaches how to build robust, database-free API tests to ensure code quality when AI models contribute to the codebase.

This tool re-drives a recorded user session trajectory in a browser environment. It is essential for regression testing UI flows after updates, reproducing bugs, and comparing sequential runs. Crucially, it employs advanced selector embedding similarity to recover from common DOM drift, ensuring reliable replay even on complex or updated websites.

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 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.

This comprehensive tool analyzes git diffs to provide deep insights into code changes. It calculates quantified risk scores, classifies changes (e.g., feature, bugfix), recommends appropriate reviewers based on ownership, and provides detailed statistics. It is essential for assessing risk before submitting a PR, reviewing large merges, or managing cross-module complexity.