102 件 ・ 関連度順
概要と使いどころ
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...
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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".
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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".
日本語の概要は準備中です。原文の説明を表示しています。
Administer MongoDB databases. Configure replica sets, sharding, and backups. Use when managing MongoDB deployments.
日本語の概要は準備中です。原文の説明を表示しています。
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。
日本語の概要は準備中です。原文の説明を表示しています。
Apply MongoDB data-modeling, indexing, and query rules from access patterns. Use when designing schemas, choosing embed vs reference, or tuning MongoDB query behavior.
日本語の概要は準備中です。原文の説明を表示しています。
Native MongoDB driver (the mongodb npm package) - MongoClient lifecycle, typed collections, CRUD result shapes, cursors, aggregation pipelines, index design, transactions
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when writing aggregation pipelines, grouping, joining collections, or optimizing slow MongoDB queries. Trigger words: 集計, aggregate, パイプライン, $group, $lookup, $match, 結合, グループ化, 遅いクエリ, explain.
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。
日本語の概要は準備中です。原文の説明を表示しています。
MongoDB operations expert for queries, aggregation pipelines, indexes, and schema design
日本語の概要は準備中です。原文の説明を表示しています。
Hunting skill for sqli vulnerabilities. Built from 12 public bug bounty reports including modern NoSQL injection (Rocket.Chat CVE-2021-22911 MongoDB $regex, Mongoose ORM CVE-2024-53900 $where bypass), modern ORM raw-fragment SQLi (Django CVE-2024-42005, Sequelize GHSA-wrh9-cjv3-2hpw), second-order SOQL injection (HackerOne Salesforce), time-based blind SQLi in GraphQL resolvers, and SQLi on OIDC-proxy backends. Use when hunting SQLi on any target. Dedicated NoSQL operator injection (MongoDB/CouchDB $where/$regex/$ne) is owned by hunt-nosqli — NoSQL appears here only as adjacent ORM/WAF context.
日本語の概要は準備中です。原文の説明を表示しています。
Hunt NoSQL Injection — MongoDB operator injection ($where, $regex, $gt, $ne), CouchDB, Redis command injection, auth bypass via NoSQLi, data dump. Use when target uses MongoDB/Mongoose, CouchDB, Redis, or shows NoSQL error messages.
日本語の概要は準備中です。原文の説明を表示しています。
Database specialist covering PostgreSQL, MongoDB, Redis, and advanced data patterns for modern applications
日本語の概要は準備中です。原文の説明を表示しています。
Multi-backend database security auditor. Audits Supabase, Firebase (Firestore/RTDB/Storage/Functions/Remote Config), MongoDB (self-hosted + Atlas), self-hosted PostgreSQL, and self-hosted MySQL for RLS/rules misconfigurations, exposed credentials, auth bypasses, MongoBleed (CVE-2025-14847), pgBouncer CVE-2025-12819, mysql_native_password drift, ghost auth, and storage exposures. Use this skill whenever the user mentions database security, RLS or rule audits, security review, penetration testing a vibe-coded app, checking if their DB is exposed, hardening a backend, fixing security rules, or auditing apps built with Lovable/Bolt/Replit/Cursor/Claude Code on any of these backends. Trigger on phrases like 'is my app secure', 'check my database', 'audit my Firebase', 'audit my MongoDB', 'audit my Postgres', 'audit my Supabase', 'is my DB exposed', or any cross-backend variant ('audit my full stack').
日本語の概要は準備中です。原文の説明を表示しています。
在授权渗透测试中挖掘 SQL/NoSQL 注入（SQLi、NoSQLi，含 WAF 绕过、盲注、堆叠查询、类型混淆注入）。当用户输入拼进 SQL/NoSQL 查询时使用——典型场景：id/搜索/排序/过滤参数、数组键当 SQL 片段、PHP 类型混淆、MongoDB `$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）。
日本語の概要は準備中です。原文の説明を表示しています。
Database Architect IA — Expert en conception de bases de données (PostgreSQL, MongoDB, Redis, schema design, migrations). Optimise le stockage et l'accès aux données.
日本語の概要は準備中です。原文の説明を表示しています。
Expert en bases de données (PostgreSQL, MySQL, MongoDB, Redis, query optimization, replication)
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill whenever user-controlled input flows into a MongoDB query, filter, or update. Trigger words: NoSQL Injection, ユーザー入力, req.body, クエリに渡す, find, filter, spread, $where, 検索条件, security review.
Use this skill when implementing full-text search, autocomplete, or vector/semantic search and RAG with MongoDB Atlas. Trigger words: 全文検索, あいまい検索, オートコンプリート, ベクトル検索, セマンティック検索, 類似検索, Atlas Search, $search, $vectorSearch, RAG, embedding.
Use this skill when designing MongoDB schemas, defining Mongoose models, choosing embed-vs-reference, or adding indexes. Trigger words: スキーマ設計, モデル定義, Mongoose, コレクション, インデックス, 埋め込み, 参照, embed, reference, index.