mcp-server-stdio
Creates and configures stdio Model Context Protocol (MCP) server connections for OpenAI Agents SDK
Creates and configures stdio Model Context Protocol (MCP) server connections for OpenAI Agents SDK
Automatically adds timestamps and execution duration to all Claude responses
Execute codex CLI as sub-agent for thorough code reviews (codex's specialty), alternative approaches when stuck, or complex problems requiring extended investigation. Note that inference takes significant time.
Gemini CLI is a command-line interface tool that provides direct access to Google's Gemini AI models through the terminal, designed specifically for developers and technical professionals. It serves a...
LLMプロンプトの最適化・テスト支援スキル。プロンプトのパフォーマンス評価、改善提案、A/Bテスト、ベストプラクティス適用、再現性検証を行います。
Create, update, or fix standalone Swift command line agents built with Swift Package Manager that use Promptly packages (PromptlyKit, PromptlyConsole, PromptlyKitTooling, PromptlySubAgents). Use when asked to scaffold a new Promptly-based agent, change its prompts or command line interface, add or adjust tools or sub agents, or diagnose build and run errors in such agents.
Get a specific vector entry by key. Requires authentication. Use for Agentuity cloud platform operations
Expert in Kabardian morphological analysis. Provides word structure breakdown, verb template analysis ([PRAGM]-[GEOM]-[ARGS]-[STEM]-[TAM]-[SUBORD]), prefix/suffix identification, derivational patterns, and multi-person verb analysis. Activates when user requests morphological analysis, mentions "морфология", "разбор слова", "структура глагола", "префикс", "суффикс", or needs help understanding complex word forms.
Orchestrates context retrieval from three CLI sources: limitless (personal life transcripts), research (online documentation/facts), pieces (local code/LTM). Use when external context is needed beyond the current codebase. Triggers on /context, /limitless, /research, /pieces, or balanced detection on complex prompts involving personal memory, technical documentation, or development history.
Initialize the memory system in an AI-ready vault. Creates the folder structure, Memory.md dashboard, and example files needed for cross-session memory persistence. Use when setting up a new vault or adding memory capabilities to an existing vault.
Best practices for memory architecture design including user vs agent vs session memory patterns, vector vs graph memory tradeoffs, retention strategies, and performance optimization. Use when designing memory systems, architecting AI memory layers, choosing memory types, planning retention strategies, or when user mentions memory architecture, user memory, agent memory, session memory, memory patterns, vector storage, graph memory, or Mem0 architecture.
This skill should be used when the user asks to "run local LLMs", "use LM Studio", "configure local AI server", "estimate VRAM requirements", "load a model locally", or needs guidance on OpenAI-compatible local API usage, model quantization selection, GPU offload configuration, MCP server integration, or headless LLM server management. Covers local AI inference, CLI automation, SDK integration, and hardware optimization.
Train LangChain/LangGraph agents using Microsoft Agent-Lightning APO (Automatic Prompt Optimization). Use when user mentions APO training, prompt optimization, agent-lightning, training multi-agent systems, training single agents, or optimizing agent prompts.
音声ファイルをWhisper APIで文字起こしする。音声処理、transcription、文字起こし、Whisperに関する作業時に使用。
OpenAI SDK development with GPT-5 family, Chat Completions, Responses API, embeddings, and tool calling. Use for AI-powered applications, chatbots, agents, and semantic search.
Access and interact with Large Language Models from the command line using Simon Willison's llm CLI tool. Supports OpenAI, Anthropic, Gemini, Llama, and dozens of other models via plugins. Features include chat sessions, embeddings, structured data extraction with schemas, prompt templates, conversation logging, and tool use. This skill is triggered when the user says things like "run a prompt with llm", "use the llm command", "call an LLM from the command line", "set up llm API keys", "install llm plugins", "create embeddings", or "extract structured data from text".
Use when creating a new LangGraph graph. Start with a minimal graph and iterate.
ドメイン固有の知識を持つ再利用可能なagent skillsを作成します。プログレッシブディスクロージャとXML構造を使用し、効果的なスキルプロンプトを生成します。新しいスキルの作成、既存スキルのリファクタリング、スキル構造のベストプラクティス適用が必要な場合に使用してください。
LLM keyring management, multi-provider support, and AI agent orchestration
Expert in designing, orchestrating, and managing multi-agent systems (MAS). Specializes in agent collaboration patterns, hierarchical structures, and swarm intelligence. Use when building agent teams, designing agent communication, or orchestrating autonomous workflows.
A simple example Skill demonstrating basic functionality
Understand the components, mechanics, and constraints of context in agent systems. Use when designing agent architectures, debugging context-related failures, or optimizing context usage.