Guide les utilisateurs dans la configuration des options clés du serveur MCP MongoDB. Utilisez cette compétence lorsque l'utilisateur a installé le serveur MCP MongoDB mais n'a pas configuré les variables d'environnement requises, ou lorsqu'il demande comment se connecter à MongoDB/Atlas sans avoir défini les identifiants.
Cette compétence agit comme un assistant interactif pour configurer le serveur MCP (Model Context Protocol) MongoDB. Elle est conçue pour les développeurs utilisant des agents IA comme Claude Code, Cursor ou Codex, qui ont installé le serveur MCP MongoDB mais n'ont pas encore défini les variables d'environnement nécessaires (comme MONGODB_URI). La compétence guide l'utilisateur à travers les options de configuration essentielles, notamment la connexion à une instance MongoDB locale ou à MongoDB Atlas. Elle explique comment localiser ou générer les identifiants requis, comment les définir dans le fichier de configuration de l'agent (par exemple, claude_desktop_config.json), et comment vérifier que la connexion fonctionne correctement. L'objectif est de réduire les frictions liées à la configuration initiale, permettant ainsi aux développeurs de se concentrer sur l'utilisation des capacités de requête et d'analyse de données de MongoDB via leur agent IA.
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Guide les utilisateurs dans la configuration des options clés du serveur MCP MongoDB. Utilisez cette compétence lorsqu'un utilisateur a installé le serveur MCP MongoDB mais n'a pas configuré les variables d'environnement requises, ou lorsqu'il demande comment se connecter à MongoDB/Atlas sans avoir défini les identifiants.
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.
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.
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.