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開発者向け MCP サーバーとよく使われる AI コードエディタ。
MCP Servers
Add vision to text-only models in Opencode (, ) — , no manual file saving.
Open-source, local-first memory and context for AI agents. One memory store, every agent.
Agent Browser eliminates the need to configure each MCP server in every client. Connect your clients once to Agent Browser, and it will manage all your Server-Sent Events (SSE) MCP servers for you.
Self-hosted, agent-native web app and MCP server for your Obsidian-style Markdown vault. Browse, search, and edit notes from a fast UI or from AI agents.
个人知识库 MCP 服务器 — 对本地文档建立索引，并提供一组飞书 API 工具，通过 MCP 协议供 AI 客户端访问。
LogLead performs log loading, log enhancement, log feature engineering, log analysis, log anomaly detection also via MCP-server.
Agent Skills
A skill that turns natural-language requests into .mmd source, validates syntax before export, and renders to PNG / SVG / PDF via the mmdc CLI or the Kroki HTTP API.
Skills to find unknown bugs before release!
MCP-native command line interface for Z.AI capabilities: vision analysis, web search, web reader, and GitHub repo exploration.
QA and test automation skills for Claude Code, Codex, Cursor, Gemini CLI, VS Code, and other agents that support the Agent Skills Standard.
Agent skill for building on Polymarket — the world's largest prediction market. Gives agents the knowledge to authenticate, place orders, read markets, stream real-time data, manage positions, bridge...
A collection of installable for AI coding agents, built to play together instead of sitting in a grab bag.
skill-check validates agent skills with clear diagnostics, auto-fix support, quality scoring, and optional security scanning.
Two Claude Skills that turn your agent into a working film crew: video writes AI video prompts the way a director, screenwriter and editor would; image writes image prompts the way an art director...
Penetration Flow Skill 是一个面向 Codex / AI 智能体的开源技能项目，用于本地沙盒、CTF、训练靶场、自有代码审计、授权安全评估和逆向分析流程。
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Your Claude Code CLI, delivered to your phone via Telegram.
基于 Skill 的模块化 Agentic Engineering 框架——将工程纪律与 AI 能力系统性结合，覆盖从需求澄清到代码审查的完整研发流程，并通过反馈闭环自动沉淀团队工程经验。