Agent Skills Hub

dev-workflow

Full-stack SDLC agent workflow managing the entire production lifecycle from intake and planning to automated testing, CI/CD, and infrastructure deployment using MCP tools.

debug-mcp

Systematic debugging workflow for MCP servers and Microsoft Copilot Studio integrations, featuring common fix patterns and validation scripts.

wormhole

Shared memory and collaboration layer for AI coding agents to track actions, manage sessions, detect conflicts, and preserve project context across tools.

context-optimization

Optimize agent context windows through KV-caching, observation masking, summarization-based compaction, and context partitioning to reduce costs and latency.

kagent

Expert guide for kagent: the Kubernetes-native framework for building, deploying, and managing AI agents, MCP tools, and A2A protocols.

clinical-trial-protocol-skill

Generate clinical trial protocols for medical devices and drugs. Supports modular, waypoint-based design, research integration, and regulatory documentation alignment.

Confidence Check

Pre-implementation confidence assessment tool for developers. Ensures 90%+ readiness via duplicate checks, architecture compliance, official docs verification, and root cause analysis.

FastMCP Development

Build and manage MCP servers using the FastMCP framework. Guide for creating tools, resources, prompts, Claude Desktop integration, and deployment with Python and TypeScript.

mcp-client

Universal MCP client for connecting to any MCP server with progressive disclosure. Wraps MCP servers as skills to prevent context window bloat from tool definitions. Use for Zapier, GitHub, sequential thinking, and file operations.

skill-template

A standardized template for creating and documenting modular Agent Skills to ensure consistent, efficient context engineering across AI agent systems.

swarm-advanced

Orchestrate complex multi-agent swarms with topologies like mesh, hierarchical, and star for research, development, and testing workflows.

context-driven-testing

Apply context-driven testing principles to adapt testing strategies based on project goals, risks, and constraints rather than relying on universal best practices.