Agent Skills Hub

skyll

A runtime skill discovery engine for AI agents. Search and retrieve specialized agent skills (SKILL.md) on-demand via REST API or MCP to inject procedural knowledge into your agent's context.

writing-skills

A Test-Driven Development (TDD) framework for writing agent skills, using pressure scenarios to ensure documentation guides agent behavior effectively.

elegant-design

Build professional, accessible, and responsive user interfaces using React, Next.js, and modern design systems like shadcn/ui. Focuses on developer tools, chat interfaces, and real-time streaming components.

analyze-project

Analyze project codebases to generate architecture documentation, coding standards, and development practices for AI onboarding.

memcontext-autopilot

An automated memory middleware for AI agents, implementing a Retrieve-Respond-Save loop to maintain long-term persistent context across conversations.

swift-patterns

Expert SwiftUI assistant for reviewing, refactoring, and building high-performance, testable, and modern iOS applications using Apple's best practices.

handoff-pack

Generates structured Handoff Pack prompts for delegating scoped coding tasks to Gemini with clear instructions, acceptance criteria, and output requirements.

skill-repo

Standardize, validate, and manage Netresearch AI agent skill repositories with automated structure enforcement, distribution workflows, and licensing compliance tools.

ciso-assistant-bootstrap

Bootstrap CISO Assistant environments by guiding users through organizational structure setup, framework selection, and initial risk assessment configuration using MCP tools.

maestro

Intelligent RAG-based gateway that routes coding tasks to specialized Swift/iOS expertise without context window bloat. Uses MCP to retrieve precise patterns from 100+ indexed skills.

Delegating to AWS Agent

Implement an AI agent delegation architecture to keep your main context clean, reduce token costs, and isolate specialized infrastructure or API tasks.

context-fundamentals

Foundational guidelines for context engineering: optimizing token budgets, attention mechanics, and system architecture for AI agents.