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mongodb/mongodb-query-optimizer

Ajuda com otimização de consultas e indexação no MongoDB. Use apenas quando o usuário pedir ajuda com otimização ou desempenho de consultas.

O que é mongodb-query-optimizer?

Esta skill especializada foca em otimização de consultas e indexação no MongoDB. Ela deve ser acionada exclusivamente quando o usuário solicitar explicitamente ajuda com desempenho, como perguntas sobre otimização de consultas lentas, criação de índices ou identificação de gargalos no cluster. A habilidade prioriza estratégias de indexação como solução principal e não deve ser usada para escrita geral de consultas MongoDB, a menos que o foco seja desempenho. Quando disponível, recomenda-se o uso do MongoDB MCP para obter informações mais detalhadas sobre o ambiente, como consultas lentas reais e uso de índices existentes.

Funciona com~Claude Code~Codex CLI~Cursor
Part ofAgent Workflows
npx skills add https://github.com/mongodb/agent-skills/tree/main/skills/mongodb-query-optimizer

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Documentação

O que mongodb-query-optimizer faz?

Ajuda com otimização de consultas e indexação no MongoDB. Use apenas quando o usuário solicitar otimização ou desempenho: "Como otimizar esta consulta?", "Como indexar isto?", "Por que esta consulta está lenta?", "Você pode corrigir minhas consultas lentas?", "Quais são as consultas lentas no meu cluster?", etc. Não invoque para escrita geral de consultas MongoDB, a menos que o usuário peça ajuda com desempenho ou indexação. Prefira indexação como estratégia de otimização. Use o MongoDB MCP quando disponível.

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-schema-design

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".

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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