sequential-thinking
Complete guide to using Sequential Thinking MCP server with Claude Code
Complete guide to using Sequential Thinking MCP server with Claude Code
Guides creation of high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK). Covers tool design, authentication, Docker deployment, and evaluation creation. NOT when consuming existing MCP servers (use the server directly).
Set up and orchestrate multi-agent workflows using Beads (shared issue tracking/memory) and MCP Agent Mail (agent messaging). Use when coordinating multiple agents, setting up agent villages, tracking work across agent sessions, or implementing swarm workflows.
Add MCP servers to Claude Code configuration at user level (~/.claude). Supports stdio, HTTP, and SSE transports with environment variable prompting. Use when "add mcp server", "install mcp", "configure mcp server", "new mcp", or "setup mcp server".
Elite multi-agent research orchestration utilizing ref MCP, exa MCP, brave-search MCP, and context7 with parallel execution of deep-research-agent, search-specialist, trend-researcher, and ux-researcher. Use for comprehensive research requiring maximum depth, breadth, and quality. Automatically invoked during /sc:research commands for world-class research capabilities surpassing any traditional deep research approach.
Patterns for building AI-powered chatbot backends using OpenAI Agents SDK with MCP (Model Context Protocol) server integration in FastAPI applications. Supports both standalone MCP servers and function tools.
This skill provides knowledge about Claude tools (Prompts, Skills, Subagents, Commands, and MCPs). Use this skill when knowledge about Claude tools is needed, when the user mentions Claude tools or when the user needs help identifying the best Claude tool to use/create for a task. Common questions include 'Should I create a Claude tool for this use-case?', 'What type of Claude tool should I create for this use-case?."
CLI for Limitless.ai Pendant with lifelog management, FalkorDBLite semantic graph, vector embeddings, and DAG pipelines. Use for personal memory queries, semantic search across lifelogs/chats/persons/topics, entity extraction, and knowledge graph operations. Triggers include "lifelog", "pendant", "limitless", "personal memory", "semantic search", "graph query", "extraction".
Expert in Claude prompting, skill creation, hooks management, MCP configuration, and sub-agents. Use when writing prompts, creating Claude Code skills, configuring hooks, setting up MCP servers, creating custom sub-agents, or asking about Claude Code architecture.
Intelligently coordinates obsidian-markdown, hierarchical-reasoning, and knowledge-graph skills through automatic skill selection, context-aware routing, and hybrid MCP integration. Use when tasks involve knowledge base construction, research synthesis, documentation generation, or learning workflows that could benefit from multi-skill composition. Activates automatically to evaluate whether specialized skills should handle the request.
Routes tasks to appropriate Task tool agents (subagents). Triggers on complex multi-step tasks, research, code review, exploration, or any task benefiting from specialized agent execution. Matches intent to agent types.
Context management tools for Claude Code - provides intelligent codebase mapping with Python, Rust, and C++ parsing, duplicate detection, and MCP-powered symbol queries. Use this skill when working with large codebases that need automated indexing and context management.
Use this skill to audit, review, validate, or check the quality of AI assistant configurations including prompt text, prompt files, skills (SKILL.md), plugins, MCP servers, agents, hooks, memory files (AGENTS.md, CLAUDE.md, GEMINI.md), and composite configurations. Evaluates against GPT Prompting Guide best practices.
Diagnoses and resolves common Model Context Protocol (MCP) server connection issues with OpenAI Agents SDK
Provides FastMCP templates, patterns, and reference materials. Use when building MCP servers, implementing FastMCP tools/resources/prompts, or working with MCP protocol compliance.
ONLY trigger this skill when the user EXPLICITLY asks for MCP-based testing: **Required triggers (ALL must mention "MCP" explicitly):** - "test connector with mcp" - "test mcp connector" - "test [provider] with mcp" - "use mcp to test [provider]" - "run mcp connector test" - "mcp test for [provider]" **DO NOT trigger for:** - Generic "test the connector" requests (use stackone run / test_actions instead) - "test [provider]" without explicit MCP mention - Regular validation or testing requests - Any testing that doesn't explicitly mention MCP This skill builds a REAL agent with Claude Agent SDK that sends natural language prompts to evaluate if action descriptions are agent-friendly. It's more intensive than regular testing and should only be used when explicitly requested.
Integrate Databricks Genie rooms as tools in agent workflows. Use when integrating Genie spaces with AI agents, querying Genie rooms programmatically via SDK or MCP, managing Genie conversations and polling, handling Genie API responses and errors, or building tool-calling agents that use Genie as a data source. Covers SDK patterns, MCP tool integration, conversation management, error handling, and performance optimization for Genie-based agent tools.
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Creates and configures hosted Model Context Protocol (MCP) server connections for OpenAI Agents SDK