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mongodb/mongodb-schema-design

MongoDB Schema-Design-Patterns und Anti-Patterns. Verwenden Sie dies beim Entwerfen von Datenmodellen, Überprüfen von Schemas, Migrieren von SQL oder Beheben von Leistungsproblemen, die durch Schema-Probleme verursacht werden.

Was ist mongodb-schema-design?

Dieses Skill-Paket bietet eine umfassende Referenz zu MongoDB Schema-Design-Patterns und -Anti-Patterns, die für Entwickler und Datenarchitekten unverzichtbar ist, die mit MongoDB arbeiten. Es unterstützt die Entscheidungsfindung zwischen Einbettung (Embedding) und Referenzierung (Referencing), stellt gängige Schema-Design-Patterns wie Approximation, Polymorphismus, und Document Versioning vor und identifiziert Anti-Patterns wie unbegrenzte Arrays (Unbounded Arrays), Index-Explosionen und unnötige Indizes. Es enthält außerdem Richtlinien für Schema-Validierung mit JSON Schema, TTL-Indizes für Datenlebenszyklus-Management sowie Strategien zur Schema-Migration und Archivierung. Das Skill-Paket ist plattformunabhängig und in jeder Umgebung nutzbar, die KI-Agenten unterstützt – wie Claude Code, Cursor, Codex und andere – und wird durch konkrete Auslöser (Trigger-Keywords) aktiviert, wenn der Entwickler Hilfe bei Schema-Entwürfen oder -Problemen benötigt.

Funktioniert mit~Claude Code~Codex CLI~Cursor
Part ofDesign & UI
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Dokumentation

Was macht mongodb-schema-design?

MongoDB Schema-Design-Patterns und Anti-Patterns. Verwenden Sie dies beim Entwerfen von Datenmodellen, Überprüfen von Schemas, Migrieren von SQL oder Beheben von Leistungsproblemen, die durch Schema-Probleme verursacht werden. Auslöser bei "Schema entwerfen", "Embedding vs. Referencing", "MongoDB Datenmodell", "Schema-Review", "unbegrenzte Arrays", "Eins-zu-Viele", "Baumstruktur", "16MB-Grenze", "Schema-Validierung", "JSON Schema", "Zeitreihen", "Schema-Migration", "polymorph", "TTL", "Datenlebenszyklus", "Archivieren", "Index-Explosion", "unnötige Indizes", "Approximationsmuster", "Dokumentversionierung".

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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