Skill UI

This advanced pipeline orchestrates complex feature development based on Request for Comments (RFCs). It breaks down monolithic changes into small, independently verifiable work units, managing dependencies through a Directed Acyclic Graph (DAG). It includes multiple quality gates, dedicated unit testing, and a safe merge queue to ensure robust integration and controlled deployment.

This skill guides the user through the critical later stages of the SPARC methodology, covering both Pseudocode Design (Phase 2) and detailed Architecture Design (Phase 3). It helps translate abstract specifications into concrete, actionable plans, defining algorithms, module boundaries, API contracts, and infrastructure needs. Use this when requirements are stable and the focus shifts from 'what' to 'how'.

This skill executes the final two phases (Refinement and Completion) of the structured SPARC development methodology. It systematically elevates an implemented feature to production-grade quality by performing detailed code reviews (verifying compliance against specifications, architecture, and quality standards), maximizing test coverage (unit, integration, and edge cases), validating performance, and generating comprehensive documentation and traceability matrices. It ensures the feature is fully ready for deployment.

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, anti-drift multi-agent swarm environment. It is designed for coordinating complex, multi-file software development tasks, such as implementing new features, performing cross-module refactoring, or conducting security audits, requiring coordination among three or more specialized agents. It supports various topologies (hierarchical, mesh, etc.) to manage safe and parallel workstreams.

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.

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.

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.