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mongodb/mongodb-schema-design

Patrones y antipatrones de diseño de esquemas de MongoDB. Útil para diseñar modelos de datos, revisar esquemas, migrar desde SQL o solucionar problemas de rendimiento causados por problemas de esquema.

¿Qué es mongodb-schema-design?

Esta habilidad proporciona una guía completa sobre patrones y antipatrones de diseño de esquemas en MongoDB. Es especialmente útil para desarrolladores y arquitectos de bases de datos que necesitan diseñar modelos de datos eficientes, revisar esquemas existentes, migrar desde bases de datos relacionales (SQL) o diagnosticar problemas de rendimiento relacionados con el esquema. La habilidad se activa con palabras clave como "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" y "document versioning". Está diseñada para agentes de IA como Claude Code, Cursor y Codex, y su documentación detallada (SKILL.md) explica los conceptos fundamentales del diseño de esquemas en MongoDB, incluyendo cuándo incrustar documentos vs. usar referencias, cómo modelar relaciones uno a muchos y jerarquías de árbol, cómo evitar arrays sin límite y el límite de 16 MB por documento, y cómo aplicar validación de esquemas con JSON Schema. También cubre patrones avanzados como esquemas polimórficos, TTL, ciclo de vida de datos, archivado, explosión de índices, índices innecesarios, el patrón de aproximación y versionado de documentos.

Compatible con~Claude Code~Codex CLI~Cursor
Part ofDesign & UI
npx skills add https://github.com/mongodb/agent-skills/tree/main/skills/mongodb-schema-design
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Documentación

¿Qué hace mongodb-schema-design?

Patrones y antipatrones de diseño de esquemas de MongoDB. Úselo al diseñar modelos de datos, revisar esquemas, migrar desde SQL o solucionar problemas de rendimiento causados por problemas de esquema. Se activa con "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".

Individual skills in this repo

This repo contains 5 individual skills — each has its own dedicated page.

mongodb/mongodb-connection

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.

mongodb/mongodb-mcp-setup

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.

mongodb/mongodb-natural-language-querying

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/mongodb-query-optimizer

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.

mongodb/mongodb-search-and-ai

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.

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