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Found 2,033 Skills
s-skills 설치 후 MCP 서버(Linear/Slack/Notion)와 GitHub CLI를 대화형으로 설정하는 스킬. 사용 중인 도구만 골라 설정하고, 이미 돼 있으면 스킵, 안 돼 있으면 단계별 안내·검증까지 전부 끌고 간다. Use when asked "셋업", "setup", "처음 설정", "s-skills 설치했어요", or after installing s-skills.
Query up-to-date library documentation and code examples using Context7 MCP. Use when you need current, version-specific documentation for npm packages, Python libraries, or other programming languages.
Connect AI coding agents (Claude Code, Cursor, VS Code, OpenAI Codex) to Grafana Cloud via the Model Context Protocol (MCP) server. Use when the user asks to connect Claude Code to Grafana, set up MCP for Grafana, use Grafana tools in Cursor, query Grafana from an AI agent, configure the Grafana MCP server, or make AI agents interact with Grafana Cloud APIs. Triggers on phrases like "MCP server", "connect Claude Code to Grafana", "Grafana MCP", "AI agent Grafana", "Claude Grafana tools", "Cursor Grafana", or "agent observability".
Pre-indexed code knowledge graph (MCP, SQLite + tree-sitter) for faster, lower-token exploration of brownfield codebases. Use when starting work on a repo larger than ~500 files or when the task involves cross-file traversal — "where is X used", "what calls Y", "what breaks if I change Z", "trace flow from A to B", "explain this subsystem". Skip for single-file edits or sessions shorter than the cold-start cost. Triggers include "codegraph", "code graph", "index this repo", "where is X defined", "find callers of", "callees of", "blast radius of changing X", "explore this codebase". Replaces grep + Read loops with O(1) SQLite lookups and FTS5 search via 8 MCP tools.
Manage Model Context Protocol (MCP) servers - discover, analyze, and execute tools/prompts/resources from configured MCP servers. Use when working with MCP integrations, need to discover available MCP capabilities, filter MCP tools for specific tasks, execute MCP tools programmatically, access MCP prompts/resources, or implement MCP client functionality. Supports intelligent tool selection, multi-server management, and context-efficient capability discovery.
Configure AWS Documentation MCP server to query up-to-date AWS knowledge, APIs, and best practices
Model Context Protocol (MCP) server implementation patterns with LangChain4j. Use when building MCP servers to extend AI capabilities with custom tools, resources, and prompt templates.
Debug MCP server communication. Use for troubleshooting MCP integrations, viewing traffic, and analyzing latency.
Self-modifying AI agent configuration via ruler + MCP + DuckDB. All behavior mods become one-liners.
Automate Mixpanel tasks via Rube MCP (Composio): events, segmentation, funnels, cohorts, user profiles, JQL queries. Always search tools first for current schemas.
Use when tasks require current, source-backed technical information from MCP tools. Apply for library/API questions, dependency version checks, third-party integration work, framework- or SDK-specific debugging, and any case where stale model knowledge could cause incorrect guidance.
Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy patterns. Activate on "optimize prompt", "improve this prompt", "prompt engineering", or ANY complex task request. Requires prompt-learning MCP server. NOT for simple questions (just answer them), NOT for direct commands (just execute them), NOT for conversational responses (no optimization needed).