Skill UI

Provides an expert Spring Boot 4 testing guide that explains the test pyramid, slice annotations, AssertJ-centric assertions, MockMvcTester, RestTestClient, Testcontainers, and practical coverage goals.

A comprehensive guide covering best practices for Terraform and OpenTofu. It provides structured advice on module hierarchy, directory organization, naming conventions, and advanced testing strategies (including native testing and policy as code). Use this skill when designing, refactoring, or implementing robust Infrastructure as Code (IaC) pipelines and multi-environment deployments.

Applies proven testing patterns (AAA, Given-When-Then, builders, spies, doubles) to help write maintainable unit, integration, and end-to-end suites, covering structuring, naming, TDD/BDD, and mocks across frameworks like Jest, Vitest, or pytest.

This guide provides comprehensive best practices for writing, structuring, and reviewing tests for Flutter and Dart applications. It covers unit tests, widget tests, golden tests, ensuring test validity, best practices for mocking, and file organization, helping developers write robust and meaningful tests that catch real regressions.

This skill systematically reviews test code to identify and correct common anti-patterns. It detects issues such as flaky tests, overly mocked dependencies (over-mocking), brittle assertions, excessive test size (The Giant), and structural dependencies. It explains the flaw, provides best practices, and suggests refactored, reliable implementations, helping maintain high test quality and stability.

A comprehensive guide for writing and reviewing robust, behavior-first unit tests for Vue 3, TypeScript, and Pinia stores. This skill covers advanced patterns for testing components, composables, and state management using Vitest and Vue Test Utils, emphasizing black-box assertions and minimizing implementation coupling.

This skill guides the systematic execution of complex development plans. It mandates a critical review of the plan for dependencies, ambiguous conditions, and environmental assumptions before execution. The process involves incremental task completion, rigorous testing, structured committing, and final reporting, ensuring adherence to established development best practices.

This guide enforces a mandatory, multi-stage process for debugging complex software issues. It moves developers beyond merely patching symptoms by forcing them through Root Cause Investigation, Pattern Analysis, Hypothesis Validation, and proper Implementation with testing. It is crucial for solving production bugs, failed tests, and performance issues in a structured manner.

This skill systematically analyzes an existing codebase to generate seven comprehensive technical documents. It covers the technology stack, architectural patterns, directory structure, coding conventions, external integrations, testing strategies, and technical concerns. It is invaluable for new team members onboarding, conducting project audits, or creating a complete, verifiable system knowledge base.

Hypogenic is an advanced framework that uses Large Language Models (LLMs) to systematically generate and test hypotheses on complex tabular datasets. It supports three powerful modes: data-driven generation (HypoGeniC), literature-grounded refinement (HypoRefine), and combined union methods. This tool is essential for accelerating scientific discovery in fields such as predictive modeling, content analysis, and deception detection by combining empirical data insights with theoretical knowledge.

Statsmodels is a robust Python library providing comprehensive tools for advanced statistical analysis. It supports a wide range of models, including Ordinary Least Squares (OLS), Generalized Linear Models (GLM like Poisson and Logistic), Weighted Least Squares (WLS), and complex time series forecasting (ARIMA). Use this skill for rigorous econometrics, detailed diagnostic testing, and generating publication-ready statistical inference.

A structured system for exploring uncertain futures by mapping the full possibility space. Instead of a single prediction, it generates 4-6 distinct branches (Best, Worst, Wild Card, etc.). Use this tool for contingency planning, stress-testing complex decisions, or analyzing speculative "what if" questions to derive actionable intelligence and map second-order consequences.