OpenAI API Agent Skills

Your agent has zero users. This fixes that. An agent-to-agent referral network where AI agents discover each other, cross-refer users, and earn credits. Available as an MCP server and HTTP API. Built by an AI agent that couldn't find its own customers. Published as io.github.oxgeneral/agentnet v1.0.0 You built an agent. It works. Nobody uses it. - 3M+ GPTs on OpenAI — most have zero users - 17,000

Transcribe audio files to text with optional diarization and known-speaker hints. Use when a user asks to transcribe speech from audio/video, extract text from recordings, or label speakers in interviews or meetings.

Deploy applications to Render by analyzing codebases, generating render.yaml Blueprints, and providing Dashboard deeplinks. Use when the user wants to deploy, host, publish, or set up their application on Render's cloud platform.

An MCP server for semantic code search & navigation that helps AI agents work efficiently without burning through costly tokens. Instead of reading entire files, agents can search conceptually and jump directly to the specific functions, classes, and code chunks they need. - OpenAI API Key: Required for generating embeddings (local embedding support planned) - Git: Must be a git repository (respec

English | 中文 k8m 是一款AI驱动的 Mini Kubernetes AI Dashboard 轻量级控制台工具，专为简化集群管理设计。它基于 AMIS 构建，并通过 kom 作为 Kubernetes API 客户端，k8m 内置了 Qwen2.5-Coder-7B，支持deepseek-ai/DeepSeek-R1-Distill-Qwen-7B模型 模型交互能力，同时支持接入您自己的私有化大模型（包括ollama）。 DEMO-InCluster模式 用户名密码 demo/demo - 详细的配置和使用说明请参考文档。 - 更新日志请参考更新日志。 - 开发设计文档-中文 - 开发设计文档-English - 迷你化设计：所有功能整合在一个单一的可执行文件中，部署便捷，使用简单。 - 简便易用：友好的用户界面和直观的操作流程，让 Kubernetes 管理更加轻松。支持

Want to use GPT-5, Gemini, or DeepSeek in Claude Code? Today you need to: 1. Create accounts with 5+ AI providers 2. Manage 5+ API keys and billing systems 3. Pay $20-100/month minimums per provider 4. Configure each provider separately That's too much friction. BlockRun MCP gives you access to 30+ AI models with: - Zero API keys - No accounts needed with OpenAI, Google, etc. - One wallet - Single

Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API) or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, HF Space syncing, and JSON output for automation.

OpenAI's coding-agent CLI. Runs Codex models with tool use, shell execution, and file editing. Ships with the Responses API integration so context management happens inside the model call.

An advanced MCP server that provides intelligent web search capabilities using OpenAI's reasoning models. Perfect for AI assistants that need up-to-date information with smart reasoning capabilities. - 🧠 Reasoning Model Support: Full compatibility with OpenAI's latest reasoning models (gpt-5, gpt-5-mini, gpt-5-nano, o3, o4-mini) - ⚡ Smart Effort Control: Intelligent reasoningeffort defaults based

graphql → ai gqai is a lightweight proxy that exposes GraphQL operations as Model Context Protocol (MCP) tools for AI like Claude, Cursor, and ChatGPT. Define tools using regular GraphQL queries/mutations against your GraphQL backend, and gqai automatically generates an MCP server for you. 🔌 Powered by your GraphQL backend ⚙️ Driven by .graphqlrc.yml + plain .graphql files - 🧰 Define tools using

� Migrate to MCP Platform • 💬 Discord Community • � Legacy Docs Zero-configuration deployment of production-ready MCP servers with Docker containers, comprehensive CLI tools, and intelligent caching. Focus on AI integration, not infrastructure setup. That's it! Your MCP server is running at http://localhost:8080 Perfect for: AI developers, data scientists, DevOps teams building with MCP. Deploy M

MCP Unity is an implementation of the Model Context Protocol for Unity Editor, allowing AI assistants to interact with your Unity projects. This package provides a bridge between Unity and a Node.js server that implements the MCP protocol, enabling AI agents like Cursor, Windsurf, Claude Code, Codex CLI, GitHub Copilot, and Google Antigravity to execute operations within the Unity Editor. MCP Unit

This MCP server allows you to access OpenAI's ChatGPT API directly from Claude Desktop. 📝 Read about why I built this project: I Built an AI That Talks to Other AIs: Demystifying the MCP Hype - Call the ChatGPT API with customisable parameters - Aks Claude and ChatGPT to talk to each other in a long running discussion! - Configure model versions, temperature, and other parameters - Use web search

Manage YouTube activities using the yutu CLI. Use this skill when you need to list channel activities such as uploads, likes, and favorites.

Turn Notion specs into implementation plans, tasks, and progress tracking; use when implementing PRDs/feature specs and creating Notion plans + tasks from them.

Perform language and framework specific security best-practice reviews and suggest improvements. Trigger only when the user explicitly requests security best practices guidance, a security review/report, or secure-by-default coding help. Trigger only for supported languages (python, javascript/typescript, go). Do not trigger for general code review, debugging, or non-security tasks.

Also available in TypeScript Send requests to OpenAI, MistralAI, Anthropic, xAI, Google AI, DeepSeek, Alibaba, Inception using MCP protocol via tool or predefined prompts. Vendor API key required The server implements one tool: - unichat: Send a request to unichat - Takes "messages" as required string arguments - Returns a response - codereview - Review code for best practices, potential issues, a

A multi-model AI orchestration MCP server for automated code review and LLM-powered analysis. Multi-MCP integrates with Claude Code CLI to orchestrate multiple AI models (OpenAI GPT, Anthropic Claude, Google Gemini) for code quality checks, security analysis (OWASP Top 10), and multi-agent consensus. Built on the Model Context Protocol (MCP), this tool enables Python developers and DevOps teams to

Prepare meeting materials with Notion context and Codex research; use when gathering context, drafting agendas/pre-reads, and tailoring materials to attendees.

Deploy applications and websites to Vercel. Use when the user requests deployment actions like "deploy my app", "deploy and give me the link", "push this live", or "create a preview deployment".

This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence.

Local-first MCP runtime for multi-agent consensus guard workflows — AI code review, weighted voting, HITL approval gates, and full observability on SQLite. MCP stdio mode (Claude Desktop, OpenClaw, etc.): Prerequisites: Node.js 20+, npm 10+. Optional: gh CLI (for GitHub PR triggers), OpenAI API key (falls back to deterministic heuristics without one). Any model accessible via the OpenAI or Anthrop

Integrate Claude with Any OpenAI SDK Compatible Chat Completion API - OpenAI, Perplexity, Groq, xAI, PyroPrompts and more. This implements the Model Context Protocol Server. Learn more: https://modelcontextprotocol.io This is a TypeScript-based MCP server that implements an implementation into any OpenAI SDK Compatible Chat Completions API. It has one tool, chat which relays a question to a config