Skill UI

An advanced system that analyzes coding sessions via hooks to extract atomic, confidence-scored behaviors called "instincts." It tracks patterns, allowing users to evolve these instincts into comprehensive skills, commands, or agents. V2.1 introduces project-scoped learning, preventing knowledge contamination between different codebases and improving development workflow efficiency.

A high-performance utility for executing simple, deterministic code transformations (e.g., removing console logs, converting var to const). This engine leverages a WASM core to perform structural refactoring sub-millisecond, without calling any expensive LLM APIs. Ideal for CI/CD pipelines, automated bulk refactoring, and cost-sensitive development workflows where determinism and speed are paramount.

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 guides developers through the entire plugin ecosystem, helping them find and recommend the optimal plugins for any given workflow or task. It covers core coordination, multi-agent collaboration, advanced memory management (Vector DB, RAG), architectural design (ADR/DDD), security auditing, and modern development methodologies. Use it when starting a new project or optimizing existing development pipelines.

A formal framework designed for evaluating AI-assisted code generation sessions. It implements Development Oriented Evals (EDD) principles, allowing users to define capability and regression criteria. The system supports multiple evaluation types—code-based, model-based, and human review—and tracks reliability using advanced metrics like pass@k, ensuring robust quality control for AI agents.

A sophisticated multi-agent development harness inspired by GANs. It separates product planning, code generation, and rigorous evaluation into three distinct agents (Planner, Generator, Evaluator). This creates an adversarial feedback loop, allowing projects to iteratively reach professional, production-grade quality far beyond what a single-agent workflow can achieve. Ideal for complex, full-stack applications.

A comprehensive guide to professional Git version control. It covers best practices for branching strategies (GitHub Flow, GitFlow, Trunk-Based), writing standardized commit messages using Conventional Commits, managing merges vs. rebases, and defining effective Pull Request workflows. Ideal for any development team looking to improve code collaboration and maintain a clean, traceable history.

This tool simplifies the process of detecting structural drift by combining audit listing, fresh auditing, and trend diffing into a single command. It finds the most recent audit record, runs a current audit against the repository, and compares the two using structural similarity metrics. It alerts when the structural distance falls below a specified threshold, helping maintain code consistency over time.

A powerful tool designed to scaffold sophisticated, custom AI agent harnesses using the metaharness CLI. It supports various templates (e.g., coding, devops, marketing) and AI hosts (e.g., Claude, Codex). Crucially, it operates with maximum safety, defaulting to a dry-run mode and refusing to write to the calling repository root, ensuring controlled and predictable development workflows.

This comprehensive skill provides deep integration with Jira, enabling users to manage the entire issue lifecycle. Features include retrieving ticket details, extracting testable requirements and acceptance criteria, updating issue status (transitions), adding comments, and linking code artifacts (PR/MR). Supports both recommended MCP server approach and direct REST API calls for robust development workflow automation.

This comprehensive guide covers advanced Kotlin testing patterns using industry-standard tools like Kotest and MockK. It details the entire TDD workflow (Red-Green-Refactor), property-based testing, coroutines testing, and code coverage configuration with Kover. Ideal for developers looking to write robust, maintainable, and scalable Kotlin codebases.

This tool orchestrates the complete workflow for addressing defects, ensuring a robust fix process. It guides the user through reproducing a bug as a failing regression test, applying the necessary fix until the test passes (goes green), and managing the subsequent code review and gated commit phases. Ideal for systems where existing functionality is broken, incorrect, or exhibiting regression.