Skill UI

Launches an interactive web dashboard designed to visualize the knowledge graph derived from a target codebase. This tool helps developers gain a high-level, structural understanding of complex projects by mapping out relationships, module dependencies, and the overall knowledge network of the code, making maintenance and onboarding significantly easier.

Turns messy workflows, docs, transcripts, or existing scripts into production-ready cross-platform agent skills by surfacing hidden requirements, generating specs, and validating exports for Claude Code, Copilot CLI, Cursor, and similar runtimes.

This agent facilitates comprehensive debugging and testing of iOS applications running on a simulated environment. It leverages XcodeBuildMCP to manage the entire lifecycle, including building, running, interacting with the UI (tap, type, gesture), taking screenshots, and capturing detailed runtime logs. Use it whenever you need to simulate real-world app testing or diagnose issues on a booted simulator.

A comprehensive toolset to scaffold, build, sign, and package SwiftPM-based macOS applications entirely without requiring an Xcode project. It guides users through the end-to-end process, including setting up the project, running the app, performing code signing, notarization, and generating update appcasts for robust, reproducible releases.

This skill provides a comprehensive, structured workflow for diagnosing performance bottlenecks in SwiftUI applications. It guides users through a code-first review to detect common issues like invalidation storms, layout thrash, or heavy main-thread work. When code review is insufficient, it provides detailed guidance on collecting runtime evidence using Instruments, enabling precise root cause analysis and targeted remediation strategies.

This guide enforces the 'spec-first' principle in software development, treating the specification as a non-negotiable contract. It outlines a rigorous, multi-stage workflow detailing mandatory sections (Functional/Non-Functional Requirements, Acceptance Criteria, API Contracts, Data Models) and bounded autonomy rules. Users learn how to define the system 'must do' before writing a single line of code, thereby minimizing rework and eliminating scope creep.

Keeps project-specific memory for Claude Code by auto-loading context on each session, tracking git activity, and running deterministic Node.js scripts so instructions remain consistent across models.

A library-agnostic Flutter/Dart code review checklist that walks through widget best practices, state management patterns, Dart idioms, performance, accessibility, security, and clean architecture, helping reviewers spot regressions in any app stack.

An advanced framework designed to transform standard Claude Code into a persistent, self-directing agent system. It integrates crucial components like structured persistent memory (MCP), scheduled operations (Crons), remote dispatching, and computer automation (browser/desktop control). This system is ideal for implementing complex, continuous, or event-driven automated workflows that require statefulness and long-term monitoring.

Extracts business domain knowledge from a codebase, optionally reusing an existing knowledge graph, and generates an interactive horizontal flow graph for dashboard visualization, with lightweight scanning and validation steps.

A comprehensive guide to structuring Flutter applications using a feature-first, layered architecture. It defines clear separation of concerns across UI, Logic (Domain), and Data layers, ensuring maintainability and testability. This approach guides developers on organizing code by feature modules, making it ideal for building complex, scalable, and long-lived applications while remaining agnostic to specific state management solutions.

This skill provides comprehensive guidance for setting up production-grade Continuous Integration/Continuous Deployment (CI/CD) pipelines for Go projects using GitHub Actions. It covers all critical stages, including rigorous unit testing with race detection, advanced linting, static application security testing (SAST) via tools like CodeQL and govulncheck, automated dependency management (Dependabot/Renovate), container image building, and automated binary release using GoReleaser. Use this when building a robust quality gate for a Go codebase.