Skill UI

The Santa Method is a multi-agent adversarial verification framework designed to improve output quality by requiring two independent reviewers (Agent B and Agent C) to pass the content before publication. It eliminates single-agent bias and systematically improves outputs through iterative refinement loops until convergence or manual escalation.

A guide for security researchers focused on finding exploitable, remote-reachable vulnerabilities for bug bounties or responsible disclosure. It emphasizes high-signal flaws such as SSRF, RCE, and SQL injection, while guiding users through the professional triage and PoC creation process for penetration testing.

A comprehensive security auditing tool designed for Claude Code environments. It scans the entire configuration directory for security vulnerabilities, misconfigurations, injection risks, hardcoded secrets, and overly permissive settings. Ideal for initial project setup, pre-commit checks, or routine security hygiene.

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.

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 executes the final two phases of the SPARC methodology: Refinement and Completion. It systematically reviews the implemented feature through rigorous code reviews, expands test coverage (unit, integration, edge cases), validates performance, generates a traceability matrix, creates comprehensive documentation, and confirms deployment readiness. It ensures the feature is robust, tested, and production-ready.

This tool executes the foundational SPARC Specification Phase (Phase 1) for new features or projects. It guides the user through systematically gathering comprehensive requirements, including functional/non-functional needs, defining concrete acceptance criteria (Given/When/Then), identifying technical constraints, and mapping edge cases. It creates a robust, formalized specification document that serves as the blueprint for all subsequent design and development phases.

A comprehensive guide to architectural patterns for building scalable, production-ready backend services using Java and Spring Boot. Covers essential topics including RESTful API design, layered architecture (Controller, Service, Repository), data persistence (JPA), caching, asynchronous processing, robust error handling, and middleware filtering.

This guide covers essential security best practices for Spring Boot applications. Learn how to implement robust authentication (JWT, OAuth2), authorization using method security, secure input validation, prevent SQL injection, manage secrets, and configure vital security headers (CORS, CSRF). Essential for building production-ready, secure Java backend services.

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.

This pattern demonstrates how to build a robust, thread-safe data persistence layer in Swift using the 'actor' model. It combines fast in-memory caching with reliable file-backed storage, automatically eliminating data races and the need for manual locks. Ideal for offline-first applications and managing shared mutable state in modern Swift concurrency.