MongoDB 스키마 설계 패턴과 안티패턴을 다룹니다. 데이터 모델 설계, 스키마 검토, SQL에서 마이그레이션, 또는 스키마 문제로 인한 성능 이슈 해결 시 사용하세요.
이 스킬은 MongoDB 스키마 설계의 모범 사례와 일반적인 실수를 피하는 방법을 제공하는 기술 참고 자료입니다. 개발자가 데이터 모델을 설계하거나 기존 스키마를 검토할 때, 특히 SQL 기반 데이터베이스에서 MongoDB로 마이그레이션하는 경우나 스키마로 인한 성능 문제를 진단할 때 유용합니다. 이 스킬은 '임베드 vs 참조', '무제한 배열', '16MB 제한', '스키마 유효성 검사', '타임 시리즈', '다형성 패턴', 'TTL', '데이터 수명 주기' 등 주요 개념을 다룹니다. 모든 플랫폼에서 사용 가능하며, 구체적인 설명과 예제를 통해 개발자가 적절한 스키마 설계 결정을 내릴 수 있도록 돕습니다.
npx skills add https://github.com/mongodb/agent-skills/tree/main/skills/mongodb-schema-designMongoDB 스키마 설계 패턴과 안티패턴. 데이터 모델 설계, 스키마 검토, SQL에서 마이그레이션, 또는 스키마 문제로 인한 성능 이슈 해결 시 사용하세요. 다음 키워드에 반응합니다: "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".
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.
Help with MongoDB query optimization and indexing. Use only when the user asks for optimization or performance: "How do I optimize this query?", "How do I index this?", "Why is this query slow?", "Can you fix my slow queries?", "What are the slow queries on my cluster?", etc. Do not invoke for general MongoDB query writing unless user asks for performance or index help. Prefer indexing as optimization strategy. Use MongoDB MCP when available.
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.
ElevenLabs text-to-speech with mac-style say UX.
Oracle CLI second-model review/debug/refactor/design with selected files, dry-run token checks, API or browser engine.
Capture and automate macOS UI with the Peekaboo CLI.
Generate multiple radically different interface designs for a module using parallel sub-agents. Use when user wants to design an API, explore interface options, compare module shapes, or mentions "design it twice".
Build a throwaway prototype to flesh out a design — a runnable terminal app for state/business-logic questions, or several radically different UI variations toggleable from one route.
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".