Agent Communication Debugger
Diagnose and debug Agent-to-Agent (A2A) communication, including orchestrator routing, transport connectivity, agent status, and log analysis for multi-agent systems.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
245 skills found
Diagnose and debug Agent-to-Agent (A2A) communication, including orchestrator routing, transport connectivity, agent status, and log analysis for multi-agent systems.
Framework for building, registering, and orchestrating Model Context Protocol (MCP) tools and AI agent workflows within the Hive native Rust architecture.
AI-powered browser automation server for web interaction, data extraction, and research using the Model Context Protocol.
Guidance and operational tips for identifying, reviewing, and managing pull requests created by the GitHub Copilot coding agent within your repository.
Expert guidance for Django asynchronous task processing with Celery. Best practices for task design, worker configuration, error handling, periodic tasks, and production monitoring.
MCP Gateway design patterns for managing Agent Gateway, Subprocess, and Daemon isolation strategies to optimize context token usage and system performance.
Dedicated E2E testing agent for Playwright and Docker-based web applications, supporting automated test execution, report generation, and test creation.
Build $50k-grade frontend interfaces with production-ready code, professional typography, and high-fidelity image integration.
Scaffold complex, multi-step coding tasks into actionable implementation plans and execute them autonomously using a Claude-driven bash loop.
Build AI agents, multi-agent systems, and workflows using the OpenAI Agents SDK for TypeScript/JavaScript. Supports tools, handoffs, guardrails, MCP, and realtime voice.
Update context-mode to the latest version, rebuild assets, reinstall global NPM dependencies, and refresh hook configurations.
A standardized template for creating and documenting modular Agent Skills to ensure consistent, efficient context engineering across AI agent systems.