Skill UI

This comprehensive guide covers idiomatic Go patterns, essential best practices, and advanced techniques for building robust, efficient, and maintainable Go applications. Topics include error wrapping, custom error types, context management for timeouts, concurrency patterns (Worker Pools, errgroup), and ensuring graceful shutdown.

A comprehensive guide to writing reliable and maintainable tests in Go. This skill covers the TDD methodology (Red-Green-Refactor cycle), advanced patterns like table-driven tests, subtests, benchmarks, and fuzzing. Learn how to ensure high code quality and test coverage in idiomatic Go projects.

Compute weighted similarity between two harness fingerprints using cosine, categorical agreement, and topology Jaccard. Use it to rank templates, detect drift, decide reuse vs scaffold, and generate confidence scores for recommender workflows.

This skill generates an enterprise-grade threat model report using the `harness threat-model` command. It categorizes threats across the MCP surface, providing a detailed report including the overall worst severity (clean, low, medium, high) and specific findings. Ideal for pre-launch security reviews and periodic audits when sharing findings with an InfoSec team.

This guide details the Hexagonal Architecture (Ports and Adapters) pattern, a robust method for decoupling core business logic from external infrastructure concerns. It teaches how to structure applications to make the domain model independent of databases, UI frameworks, or message queues. Implementing this pattern drastically improves testability, maintainability, and the ability to swap out external services without rewriting business rules.

This skill guides the design and rigorous review of goal-oriented agent feedback loops. It helps ensure that complex autonomous multi-step agents are robust, preventing failures such as infinite looping, goal-gaming, or running to completion with incorrect assumptions. It covers defining machine-decidable goals, setting boundaries, and selecting the correct loop type (servo, regulator).

This file is currently undergoing Japanese localization for MySQL patterns. It serves as a placeholder indicating that the Japanese translation is in progress. Users should refer to the original English skill file for implementation details.

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.

This engine serves as a shared, comprehensive orchestration layer for the entire development lifecycle (orch-* skills). It defines a rigorous, gated pipeline—including Research, Plan, TDD Implementation, Review, and Commit—ensuring consistency regardless of the specific operation (e.g., feature addition, defect fixing). It also includes a size classifier to accurately scope the required effort and gates for mandatory human review.

This skill orchestrates complex, behavior-preserving code refactoring. It is designed to improve code structure—such as extracting modules, eliminating duplication, or reducing nesting—without changing the core functionality. The process follows a rigorous, multi-phase gate system, ensuring existing tests remain green throughout the entire development lifecycle, culminating in a behavior-neutral commit.

A comprehensive guide detailing advanced performance optimization patterns for React 18/19 and Next.js. Adapted from Vercel Engineering's best practices, this skill organizes 70+ critical rules across eight categories—including eliminating waterfalls, optimizing bundle size, improving server-side rendering, and managing client-side re-renders. Use this when developing, reviewing, or refactoring high-performance React applications.

Skill Scout is a crucial pre-creation workflow designed to prevent the duplication of effort. Before building a new skill, it systematically searches local, marketplace, GitHub, and web sources. It not only finds potential matches but also rigorously vets them for security, maintenance status, and relevance, providing the user with clear options: Use Existing, Fork/Extend, or Create New.