Skill UI

This skill guides users through Phases 2 (Pseudocode Design) and 3 (Architecture Design) of the SPARC methodology. It translates high-level specifications into detailed technical blueprints, covering algorithm pseudocode, module boundaries, API contracts (using DDD patterns), and infrastructure planning, ensuring a robust plan before writing production code.

This skill acts as an expert guide for creating comprehensive, implementation-ready design system documentation. It structures guidelines by defining foundational tokens (color, typography, spacing), detailing component anatomy, interaction states, and adhering to strict accessibility standards (WCAG 2.2 AA). Use it to ensure your design standards are consistent, technically sound, and actionable for engineering and design teams.

This tool initializes a structured multi-agent swarm with built-in anti-drift configuration. It is designed for orchestrating complex, multi-file tasks that require coordinated efforts from three or more specialized AI agents, such as feature implementation, cross-module refactoring, or comprehensive security audits. It supports various topologies (hierarchical, mesh, etc.) to ensure robust and manageable parallel work execution across large teams.

A guide to the Swift 6.2 concurrency model, which mandates default single-threaded execution to eliminate accidental data races. Learn how to use MainActor for isolated consistency and utilize the @concurrent attribute for explicitly offloading CPU-intensive tasks to background threads, ensuring safe and predictable application architecture.

This workflow guides developers through Test-Driven Development (TDD) principles. It mandates writing tests (unit, integration, E2E) before writing implementation code, ensuring minimum 80% test coverage. The structured process (Red -> Green -> Refactor) guarantees robust code quality, effective bug fixing, and safe feature addition, while integrating Git checkpoint commits for full traceability.

This skill provides a structured framework for managing complex, multi-agent development tasks. It treats agents as a collaborative team, utilizing structured work items, defined ownership, Agent Kanban boards, and explicit merge gates. This ensures that large-scale, multi-tool, or cross-branch development efforts are predictable, highly visible, and result in reliably mergeable, production-ready artifacts.

An interactive tool designed to discover, select, and assemble specialized teams of AI agents. It supports structured agent collections across different domains (e.g., Engineering, Marketing). Users select multiple roles for a specific task, and the skill launches them in parallel, synthesizing their independent findings into a unified report, including consensus and conflict analysis.

This skill guides users through evidence-based terminal operations. It is designed for scenarios requiring direct command execution, Git status checks, debugging CI/build failures, and pushing narrow, verified fixes. It emphasizes following a structured, minimal-scope workflow and accurately reporting the repository's current state (e.g., local changes vs. pushed code).

This guide details the architectural and implementation patterns for building modular applications or microservices using the tinystruct Java framework. It covers core concepts like using the @Action annotation for dual routing (CLI/HTTP), managing request state with Context, and using native Builder for JSON serialization, ensuring a lightweight and structured development environment.

A comprehensive tool for analyzing the current market regime (bull, bear, ranging, volatile) using advanced neural models. It detects the prevailing market structure, provides a confidence score, and recommends suitable trading strategies. Ideal for market analysis, backtesting preparation, or general market condition inquiries.

Tree Ring Memory is a local-first, framework-agnostic layer for managing durable knowledge in AI agents. It provides structured methods for recalling past decisions, recording evidence, auditing project state, and deliberately forgetting information, ensuring the core project memory remains clean and actionable without relying on raw conversation transcripts.

This tool automatically converts design screenshots or visual exports into structured, functional Vue 3 Composition API components. It supports popular UI libraries like Vant, Element Plus, and Ant Design Vue. By batch converting screenshots, it streamlines the development process from raw design assets to reusable, production-ready code, significantly boosting frontend development efficiency.