메뉴
Master MongoDB schema design and data modeling patterns. Learn embedding vs referencing, relationships, normalization, and schema evolution. Use when designing databases, normalizing data, or optimizing queries.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/pluginagentmarketplace/custom-plugin-mongodb --skill mongodb-schema-design명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Master MongoDB aggregation pipeline for complex data transformations. Learn pipeline stages, grouping, filtering, and data transformation. Use when analyzing data, creating reports, or transforming documents.
Master MongoDB Atlas cloud setup, cluster configuration, security, networking, backups, and monitoring. Get production-ready cloud database in minutes. Use when setting up cloud MongoDB, configuring clusters, or managing Atlas.
Master MongoDB authentication methods including SCRAM, X.509 certificates, LDAP, and Kerberos. Learn user creation, role assignment, and securing MongoDB deployments.
SOC 직업 분류 기준
SKILL.md 표시 중
| name | mongodb-schema-design |
| version | 2.1.0 |
| description | Master MongoDB schema design and data modeling patterns. Learn embedding vs referencing, relationships, normalization, and schema evolution. Use when designing databases, normalizing data, or optimizing queries. |
| sasmp_version | 1.3.0 |
| bonded_agent | 03-mongodb-data-modeling |
| bond_type | PRIMARY_BOND |
| capabilities | ["embedding-strategy","referencing-strategy","relationship-modeling","schema-patterns","evolution-planning"] |
| input_validation | {"required_context":["use_case_domain","access_patterns"],"optional_context":["data_volume_estimate","update_frequency","query_requirements"]} |
| output_format | {"schema_design":"object","collection_definitions":"array","relationship_diagram":"string","validation_rules":"object","evolution_strategy":"string"} |
| error_handling | {"common_errors":[{"code":"SCHEMA001","condition":"Unbounded array growth","recovery":"Use referencing instead of embedding for 1-to-many"},{"code":"SCHEMA002","condition":"Document exceeds 16MB","recovery":"Split document, use GridFS, or reference external data"},{"code":"SCHEMA003","condition":"Denormalization update anomaly","recovery":"Implement sync mechanism or reduce denormalization"}]} |
| prerequisites | {"mongodb_version":"4.0+","required_knowledge":["document-model","crud-operations"],"design_inputs":["List of access patterns","Expected data volumes"]} |
| testing | {"unit_test_template":"// Validate schema design\nconst doc = { /* sample document */ }\nconst validation = await db.command({\n collMod: 'collection',\n validator: jsonSchema\n})\nexpect(validation.ok).toBe(1)\n"} |
Master data modeling and schema patterns.
// User with single address - embed if always accessed together
{
_id: ObjectId('...'),
name: 'John',
email: 'john@example.com',
address: {
street: '123 Main St',
city: 'New York',
zip: '10001'
}
}
// User with multiple tags - embed if limited size
{
_id: ObjectId('...'),
name: 'John',
tags: ['mongodb', 'database', 'nosql'],
posts: [
{ _id: 1, title: 'Post 1', content: '...' },
{ _id: 2, title: 'Post 2', content: '...' }
]
}
// User with many orders - reference if potentially large
{
_id: ObjectId('user1'),
name: 'John',
email: 'john@example.com'
}
// Orders collection
{
: (),
: (),
:
}
// Products with categories
{
_id: ObjectId('product1'),
name: 'Laptop',
categoryIds: [
ObjectId('electronics'),
ObjectId('computers')
]
}
// Categories collection
{
_id: ObjectId('electronics'),
name: 'Electronics'
}
// Store variant attributes flexibly
{
_id: ObjectId('...'),
productName: 'T-Shirt',
attributes: [
{ key: 'color', value: 'blue' },
{ key: 'size', value: 'L' },
{ key: 'material', value: 'cotton' }
]
}
// Different document types in same collection
{
_id: ObjectId('...'),
type: 'email',
to: 'user@example.com',
subject: 'Hello'
}
{
_id: ObjectId('...'),
type: 'sms',
phoneNumber: '+1234567890',
message: 'Hi there'
}
// Parent-child relationships
{
_id: ObjectId('...'),
name: 'Electronics',
parent: null
}
{
_id: ObjectId('...'),
name: 'Computers',
parent: ObjectId('electronics')
}
// Track document history
{
_id: ObjectId('...'),
name: 'Product',
description: 'Latest description',
versions: [
{ v: 1, name: 'Product', description: 'Original', date: ISODate(...) },
{ v: 2, name: 'Product', description: 'Updated', date: ISODate(...) }
]
}
Does the related data grow unbounded?
YES → Use referencing
NO → Consider embedding
Is the related data frequently accessed separately?
YES → Use referencing
NO → Consider embedding
Do updates need to be atomic across documents?
YES → Use embedding
NO → Use referencing
# User with embedded address
users.insert_one({
'name': 'John',
'email': 'john@example.com',
'address': {
'street': '123 Main St',
'city': 'New York'
}
})
# User with references to orders
users.insert_one({
'_id': ObjectId('...'),
'name': 'John'
})
orders.insert_one({
'userId': ObjectId('...'),
'total': 99.99
})
# Query with $lookup
users.aggregate([
{ '$lookup': {
'from': 'orders',
'localField': '_id',
'foreignField': 'userId',
'as': 'orders'
}}
])
✅ Embed when data is always accessed together ✅ Reference for unbounded arrays ✅ Keep document size under 16MB ✅ Consider query patterns when designing ✅ Denormalize carefully for performance ✅ Plan for schema evolution ✅ Use validation schemas ✅ Document your design decisions