Agent Plugins: The USB-C of AI Skills?

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Guesty MCP Server is an open-source Model Context Protocol server that connects AI clients to Guesty property-management accounts. It exposes tools for reservations, listings, guests, calendars, financial reports, operations, reviews, messaging, pricing, tasks, webhooks, IoT, and property-health workflows, letting Claude, ChatGPT, Copilot, Cline, and other MCP-compatible clients answer questions or perform property-management actions from structured Guesty data. The project is useful for short-term-rental operators, property managers, automation builders, and agencies that manage Guesty portfolios and want AI assistants inside operational workflows. It launched on Show HN as the first MCP server for Guesty, and the npm registry plus official GitHub repo verify installability, README details, MIT licensing, and production use on real rentals.
Kagi Session2API MCP is an open-source MCP server that lets AI assistants access Kagi Search and Summarizer through existing session tokens rather than a separate API key. It is aimed at Claude Desktop, Cursor, Windsurf, Hermes, and other MCP-client users who want high-quality web search available directly inside agent workflows. The project is useful for research assistants, coding agents, and personal automation setups where search and summarization need to be called as tools. Its appeal is pragmatic: it bridges a paid search product into the model-context ecosystem with local configuration and no heavyweight platform. It is notable now because recent GitHub MCP searches showed strong early interest and stars for a very specific agent-tooling gap.
CMUX Agent MCP is a programmable terminal control plane for orchestrating multiple AI coding agents inside CMUX workspaces. An orchestrator can launch Claude Code, Gemini CLI, Codex CLI, OpenCode, or Goose sessions, inject prompts, read pane output, manage layouts, recover sessions, and coordinate separate projects without manually switching terminals. The project also exposes browser automation, notifications, sidebar metadata, and automatic session saving through a single MCP server. It is aimed at developers and technical leads who run parallel agent work and need a visible, inspectable control surface rather than a collection of disconnected terminal tabs. Its current repository is substantial, with documented installation, 81 tools, tests, TypeScript sources, and 176 commits. That makes it a more credible multi-agent infrastructure tool than a thin MCP demo.
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Use this prompt to design the handoff contract between multiple AI agents, tools, or services before a workflow becomes brittle. It turns a rough description of an agent system into a structured interoperability brief covering responsibilities, input and output schemas, state handoff rules, approval checkpoints, escalation paths, and failure recovery. It is useful for builders creating agent chains, internal automations, or tool integrations where the biggest problem is often not model intelligence but unclear boundaries between steps. The output helps product and engineering teams specify what each agent should receive, produce, validate, and pass forward, so the system becomes easier to debug, govern, and swap across different tools or standards.
Business & strategyDescribe any recurring workflow — support triage, lead qualification, research ops, QA, reporting, or back-office reviews — and get a concrete AI agent deployment plan. The output maps the workflow into agent responsibilities, human approval points, tool access, permission scopes, failure modes, observability needs, and rollout phases. It is designed for teams that want to move from vague agent ideas to something production-ready without skipping governance.
Business & strategyThis prompt helps teams evaluate whether an AI agent feature is actually ready for real-world deployment instead of just looking impressive in a demo. It is designed for product managers, founders, operators, and technical leads who need to assess permissions, observability, spend controls, approval checkpoints, failure handling, and auditability before putting agentic workflows in front of customers or employees. The output turns a vague concept or existing workflow into a governance readiness audit with specific risks, missing controls, and prioritized improvements. That makes it useful when a team is moving from prototype to production, preparing for enterprise buyers, or trying to avoid expensive trust failures. It focuses on the operational layer that determines whether an agent can be governed responsibly, not just whether the underlying model is smart enough.
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