Claude Desktop Руководство установки
Десктопный клиент с поддержкой локальной конфигурации MCP.
MCP Servers

Drop a .pcap file, ask questions in plain English — get answers backed by real tshark data.

PiloTY: AI pilot for PTY operations via MCP - enables AI agents to control interactive terminals like a human

MCP server to enable an LLM to do basic static triage of a PE.

Intercept AI requests, track usage, inject MCP tools centrally

🔥 MCP Mastery: Build AI Apps with Claude, LangChain and Ollama

is the easiest way to connect any MCP client (like Claude or Cursor) with the browser using browser-use.
Agent Skills

本 Skill 将数学建模任务拆分为 三个阶段。既可以按顺序完成整道题,也可以只执行其中一个阶段。

AI got superpowers through Skills and MCPs. What about humans?

The easiest way to evaluate your Agent Skills. Tests that AI agents correctly discover and use your skills.

My personal library of domain-agnostic agent skills, reused across every project. Small, composable, and hackable — works with any harness that supports skills: Claude Code, Codex, opencode, Cursor,...

This repo may be replaced by nuxt-skill.onmax.me. Stay tuned.

Agent Plugins is an open, vendor-neutral standard for packaging reusable components that extend AI agents into distributable plugins.

Give an AI agent a link to an OpenAPI spec, GraphQL schema, or MCP server and get back a ready-to-use Agent Skill with wrapper scripts, references, and usage notes.

A source-available Codex skill for distilling photographs into sparse editorial abstractions.

A curated, installable collection of AI agent skills for research, writing, analysis, and scientific workflows.

A skill bundle in Claude Code Skills format. It covers the 100 most-used Bioconductor packages the top 100 rising-star packages (newly released since ~2021 and fast-growing) from the BioMate-KB...

🚀 Supercharge your AI coding assistant with curated, production-ready skills for Microsoft Azure. Works seamlessly with: 🟣 | 🔵 | 🟢 | 🔴 | 🩵 | 🟠 | ⚪ | 🌸

Generate and evaluate agent skills based on traces with agents. Create skills with teacher models (expensive/slow) that student models (cheap/fast) can use to perform harder tasks reliably.