Results for “mongodb”

Manages MongoDB Atlas Stream Processing (ASP) workflows. Handles workspace provisioning, data source/sink connections, processor lifecycle operations, debugging diagnostics, and tier sizing. Supports Kafka, Atlas clusters, S3, HTTPS, and Lambda integrations for streaming data workloads and event processing. NOT for general MongoDB queries or Atlas cluster management. Requires MongoDB MCP Server with Atlas API credentials.

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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.

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Use when you need framework-agnostic MongoDB and non-relational database query guidance — document schema design, collection modeling, JSON Schema validation, indexes, aggregation pipelines, query performance, consistency trade-offs, transactions, and operational safety — without choosing Spring Boot, Quarkus, or Micronaut. This should trigger for requests such as Design MongoDB document schemas; Review MongoDB queries and indexes; Improve aggregation pipeline performance; Model non-relational data access patterns; Review NoSQL consistency and transaction trade-offs. Part of Plinth Toolkit

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This skill should be used when user asks to "query MongoDB", "show database collections", "get collection schema", "list MongoDB databases", "search records in MongoDB", or "check database indexes".

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Manage MongoDB Atlas Organizations as Azure ARM resources using Azure.ResourceManager.MongoDBAtlas SDK. Use when creating, updating, listing, or deleting MongoDB Atlas organizations through Azure M...

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MongoDB with Mongoose — schemas, models, aggregation pipelines, migrations, and Atlas connections. Use when designing collections, writing queries, or integrating MongoDB into Node.js/Next.js apps.

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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".

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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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Create a Kotlin Spring Boot project with database connections, API documentation, and local services managed through Docker Compose. Set up dependencies and configuration files, then run Gradle tests to check the initial project.

• Starting a Kotlin Spring Boot project
• Setting up local Redis, PostgreSQL, and MongoDB services
• Adding API documentation and architecture test dependencies

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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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".

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Administer MongoDB databases. Configure replica sets, sharding, and backups. Use when managing MongoDB deployments.

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编写Python脚本，实现每天定时从MongoDB获取数据，进行Gzip压缩，并通过HTTP POST请求同步到指定URL，同时处理BytesIO二进制传输错误。

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Apply MongoDB data-modeling, indexing, and query rules from access patterns. Use when designing schemas, choosing embed vs reference, or tuning MongoDB query behavior.

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Native MongoDB driver (the mongodb npm package) - MongoClient lifecycle, typed collections, CRUD result shapes, cursors, aggregation pipelines, index design, transactions

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Use when working with Mongodb — mongoDB database analysis, performance tuning, query optimization, and health monitoring. Covers collection analysis, index recommendations, aggregation pipelines, replica set health, Atlas cluster management, and slow query investigation.

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编写Python脚本，实现每天定时从MongoDB获取数据，进行Gzip压缩，并通过HTTP POST请求同步到指定URL，同时处理BytesIO二进制传输错误。

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Create and validate mirrord.json settings for isolated database copies used in development and testing. Configure connection sources, copying scope, migrations, and authentication so experiments can run without affecting shared databases.

• Preparing isolated databases for development and testing
• Choosing which schemas and data to copy
• Configuring connection sources and IAM authentication

Investigate SQL injection in search forms, filters, and APIs by choosing tests based on actual responses. The skill covers visible query results, response differences, and timing evidence, helping produce reproducible reports with concrete data impact.

• Inspect search and filter inputs for SQL injection
• Choose tests based on endpoint responses
• Review ORM query construction risks

Database specialist covering PostgreSQL, MongoDB, Redis, and advanced data patterns for modern applications

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Multi-backend database security auditor. Audits Supabase, Firebase (Firestore/RTDB/Storage/Functions/Remote Config), MongoDB (self-hosted + Atlas), self-hosted PostgreSQL, and self-hosted MySQL for RLS/rules misconfigurations, exposed credentials, auth bypasses, MongoBleed (CVE-2025-14847), pgBouncer CVE-2025-12819, mysql_native_password drift, ghost auth, and storage exposures. Use this skill whenever the user mentions database security, RLS or rule audits, security review, penetration testing a vibe-coded app, checking if their DB is exposed, hardening a backend, fixing security rules, or auditing apps built with Lovable/Bolt/Replit/Cursor/Claude Code on any of these backends. Trigger on phrases like 'is my app secure', 'check my database', 'audit my Firebase', 'audit my MongoDB', 'audit my Postgres', 'audit my Supabase', 'is my DB exposed', or any cross-backend variant ('audit my full stack').

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在授权渗透测试中挖掘 SQL/NoSQL 注入（SQLi、NoSQLi，含 WAF 绕过、盲注、堆叠查询、类型混淆注入）。当用户输入拼进 SQL/NoSQL 查询时使用——典型场景：id/搜索/排序/过滤参数、数组键当 SQL 片段、PHP 类型混淆、MongoDB `$where`/`$regex`、堆叠查询到 xp_cmdshell。适用目标类型 Web / REST API。触发场景包括用户说"测下 SQL 注入""这个参数能不能注入""盲注/时间注入试试""NoSQL/MongoDB 注入看一下"。输出：注入点 + 类型 + 证据的 finding（含 killed 记录）。

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Database Architect IA — Expert en conception de bases de données (PostgreSQL, MongoDB, Redis, schema design, migrations). Optimise le stockage et l'accès aux données.

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Expert en bases de données (PostgreSQL, MySQL, MongoDB, Redis, query optimization, replication)

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