mcp-manager
Conversational interface for managing MCP (Model Context Protocol) server configurations in Claude Code
Conversational interface for managing MCP (Model Context Protocol) server configurations in Claude Code
Analyzes and optimizes prompt files for LLM agents. Use this skill when working with .prompt files, improving agent instructions, enhancing prompt clarity, or debugging agent behavior. Helps with prompt engineering, structure optimization, and LangGraph agent prompt management.
Work on Agent Studio chat (UI + streaming) and the /api/chat route. Use for tasks like message rendering, reasoning display, editing/regenerate, Zustand chat stores, model/settings UI, and OpenRouter or Vercel AI SDK integration. 聊天功能开发/排障:流式输出、Reasoning 展示、消息编辑与重新生成、模型与参数设置。
Infinite Improvement Framework: Move from Probabilistic Agents to Deterministic Workflows.
Create a new Claude Code skill with proper directory structure and SKILL.md format.
Macで登録済みClaude agent skillsをスキャンし一覧表示。「スキルを調べて」「登録済みスキル一覧」などで使用。読み取り専用で安全に実行。
This skill should be used when the user asks to "add resiliency to a skill", "make this skill more robust", "improve error handling", "add validation mechanisms", "create self-correcting behavior", or discusses determinism, robustness, error correction, or homeostatic patterns in Agent Skills. Applies biological resiliency principles from Michael Levin's work to Agent Skill design.
Maintain context across Claude Code sessions using filesystem persistence. Prevents context loss, enables plan continuity, and supports multi-session workflows.
Guide for building AI-powered applications using the Vercel AI SDK v6. Use when developing with generateText, streamText, useChat, tool calling, agents, structured output generation, MCP integration, or any LLM-powered features in TypeScript/JavaScript applications. Covers React, Next.js, Vue, Svelte, and Node.js implementations.
Comprehensive LLM model evaluation and ranking system. Use when users ask to compare language models, find the best model for a specific task, understand model capabilities, get pricing information, or need help selecting between GPT-4, Claude, Gemini, Llama, or other LLMs. Provides benchmark-based rankings, cost analysis, and use-case-specific recommendations across reasoning, code generation, long context, multimodal, and other capabilities.
Choose the best Codex model (gpt-4 family, gpt-4o-mini, or legacy davinci) based on the workload described; use when the user asks for a model suggestion, wants to optimize quality vs cost/latency, or says “change model” for the current answer.
A simple demonstration skill that greets users and provides helpful information
Setup and use Docker AI (Gordon) for intelligent container operations
Comprehensive overview of Claude Code features, architecture, and capabilities. Learn about hooks, skills, commands, permissions, and best practices for configuring Claude Code as your AI development assistant. This is about the TOOL itself, not project code.
Use when building Retrieval-Augmented Generation systems. Covers document chunking, embedding generation, vector indexing, semantic search, context building, and integration of retrieval with LLM completion for accurate Q&A on Physical AI textbook content.
Generates high-quality, predictable, and efficient .agent/skills/ directories based on user requirements. Use when the user asks to create a new skill or defining a new agent capability.
This skill should be used when working with DSPy.rb, a Ruby framework for building type-safe, composable LLM applications. Use this when implementing predictable AI features, creating LLM signatures and modules, configuring language model providers (OpenAI, Anthropic, Gemini, Ollama), building agent systems with tools, optimizing prompts, or testing LLM-powered functionality in Ruby applications.
Activate when the user needs help conducting proactive threat hunting, investigating suspicious activity, or building hypothesis-driven hunts in LimaCharlie.
[REPLACE] Apply domain-specific logic based on detected context. Use when [REPLACE with specific triggers].
Persistent memory system for AI agents with semantic, episodic, and procedural memory types. Use when users want to (1) remember facts, preferences, or context across sessions, (2) track interaction history and experiences, (3) store reusable workflows or procedures, (4) build personalized agents that learn from conversations, or (5) implement any form of long-term memory for AI applications.
Ensures questions are answered literally before taking action. Triggers on user input containing '?' or patterns like 'why did you...?', 'will that work?', 'have you considered...?'. Use when user asks about your decisions, challenges an approach, or requests assessment. Prevents interpreting questions as implicit instructions or criticism.
Discovers and lists Claude Code session transcripts from .claude/logs/ for analysis. Use when the user wants to find available sessions, view session timelines, identify which sessions to analyze for workflow improvements, or understand session history. Triggers when user mentions "what sessions do I have", "analyze my workflow", "show my recent sessions", "find transcripts from [date]", or similar session discovery requests.