Skill UI

A comprehensive multi-agent feedback loop inspired by Generative Adversarial Networks (GANs), designed to build complex, production-ready applications. It separates generation from evaluation, using a 'ruthlessly strict' evaluator (QA) to drive continuous iteration. This process ensures high design quality, robust functionality, and originality, overcoming the limitations of single-agent self-critique. Ideal for full-stack feature development and complex product specification implementation.

GateGuard is a mandatory pre-tool-use hook designed to significantly enhance the reliability and safety of LLM-generated code and actions. Instead of relying on self-evaluation, it forces the model to perform concrete, verifiable investigations—such as listing all imported files, checking data schemas, or detailing rollback procedures—before any write or destructive command execution. It establishes a three-stage gate (Deny, Force, Allow) to prevent hallucination and ensure context awareness in complex coding environments.

This expert skill guides the creation of commercial-grade, highly optimized, and professional Windows installers for Python applications. It leverages Nuitka for extreme compilation (LTO, slimming, 32/64-bit selection) and Inno Setup for robust packaging. The goal is to deliver the smallest size, fastest startup time, and a residue-free user experience, strictly avoiding basic single-file builds. Ideal for professional software deployment.

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 skill provides comprehensive automation for managing GitHub workflows across the entire development lifecycle. It covers detailed issue triage, PR review status checks, CI/CD failure debugging, official release management, and security monitoring using the GitHub CLI. Ideal for maintaining open-source project health and streamlining team development processes.

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.

A utility for scaffolding and verifying fixed evaluation corpora (bench suites). It generates deterministic test suites from existing repositories, decoupling the evaluation process from the repo's natural test suite. This ensures consistent fitness scoring when running evolution pipelines across different commits or forks.

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 comprehensive 7-section report providing a deep assessment of a repository's health and readiness level. It calculates key metrics including risk score, agent topology recommendations, MCP surface area, test confidence, and publish readiness. This tool is vital for pre-mint reviews, detecting architectural drift, and enforcing strict CI/CD quality gates.

Performs a pure, read-only static security scan of a harness's declared MCP surfaces. It analyzes policy, permission, and dependency issues by reading manifest files (e.g., .mcp/servers.json and .harness/claims.json) without executing any underlying tools. Designed for integration into CI/CD pipelines to enforce security policies and ensure compliance.

A comprehensive audit worker that integrates Open Infrastructure Architecture (OIA) manifest analysis, threat modeling, and MCP security scanning. It computes a composite worst-case severity score and persists time-stamped audit records in a memory namespace. Primarily designed for scheduled CI/CD pipelines to detect architectural drift and security regressions over time.