MongoDB 쿼리 최적화 및 인덱싱 관련 지원. 사용자가 성능 최적화에 대해 질문할 때만 호출합니다.
이 스킬은 MongoDB 쿼리 최적화와 인덱싱 전략 수립을 도와주는 전문 도구입니다. 사용자가 특정 쿼리의 성능 문제, 인덱스 설계, 느린 쿼리 분석을 요청할 때만 활성화되어야 하며, 일반적인 쿼리 작성 요청에는 사용하지 않습니다. 기본 전략으로 인덱스 기반 최적화를 권장하며, MongoDB MCP(Model Context Protocol)가 사용 가능할 때 이를 우선 활용하여 성능 진단과 개선을 수행합니다. 이 스킬은 Claude Code, Cursor, Codex 등 다양한 AI 에이전트 플랫폼에서 사용할 수 있으며, 사용자에게 explain(), 힌트(oplog, covered query), 복합 인덱스, TTL 인덱스 같은 구체적인 최적화 방안을 제시합니다.
npx skills add https://github.com/mongodb/agent-skills/tree/main/skills/mongodb-query-optimizerInstalled? Explore more 생산성 & 협업 skills: steipete/gemini, steipete/gh-issues, steipete/skill-creator · View all 6 →
MongoDB 쿼리 최적화 및 인덱싱 관련 지원. 사용자가 성능 최적화에 대해 질문할 때만 호출합니다: "이 쿼리를 어떻게 최적화하나요?", "이것을 어떻게 인덱싱하나요?", "이 쿼리가 왜 느린가요?", "느린 쿼리를 고칠 수 있나요?", "내 클러스터에서 느린 쿼리는 무엇인가요?" 등. 일반적인 MongoDB 쿼리 작성 요청에는 사용하지 말고 성능이나 인덱스 관련 질문이 있을 때만 호출하세요. 최적화 전략으로 인덱싱을 우선 선호합니다. MongoDB MCP가 사용 가능할 때는 이를 사용하세요.
This repo contains 5 individual skills — each has its own dedicated page.
Optimize MongoDB client connection configuration (pools, timeouts, patterns) for any supported driver language. Use this skill when working/updating/reviewing on functions that instantiate or configure a MongoDB client (eg, when calling `connect()`), configuring connection pools, troubleshooting connection errors (ECONNREFUSED, timeouts, pool exhaustion), optimizing performance issues related to connections. This includes scenarios like building serverless functions with MongoDB, creating API endpoints that use MongoDB, optimizing high-traffic MongoDB applications, creating long-running tasks and concurrency, or debugging connection-related failures.
Guide users through configuring key MongoDB MCP server options. Use this skill when a user has the MongoDB MCP server installed but hasn't configured the required environment variables, or when they ask about connecting to MongoDB/Atlas and don't have the credentials set up.
Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.
MongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on "design schema", "embed vs reference", "MongoDB data model", "schema review", "unbounded arrays", "one-to-many", "tree structure", "16MB limit", "schema validation", "JSON Schema", "time series", "schema migration", "polymorphic", "TTL", "data lifecycle", "archive", "index explosion", "unnecessary indexes", "approximation pattern", "document versioning".
Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB MCP server.
Gemini CLI one-shot prompts, summaries, generation, skills, hooks, MCP, or Gemma routing.
Fetch GitHub issues, select candidates, spawn background fix agents, open PRs, and optionally process PR review comments.
Create, edit, audit, tidy, validate, or restructure AgentSkills and SKILL.md files.
Search, install, update, sync, or publish agent skills with the ClawHub CLI and registry.
Delegate coding work to Codex, Claude Code, or OpenCode as background workers; not simple edits or read-only code lookup.
List, configure, authenticate, call, and inspect MCP servers/tools with mcporter over HTTP or stdio.