Mongodb logo

Mongodb

Community
bobmatnyc
mongodb

MongoDB - NoSQL document database with flexible schema design, aggregation pipelines, indexing strategies, and Spring Data integration

Overview

Publisherbobmatnyc
Repositoryclaude-mpm-skills
Skill namemongodb
Stars
75
Forks
19
Bundled files
1
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by bobmatnyc on GitHub. Read the source before you install it.

Installation

Install the Mongodb AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/bobmatnyc/claude-mpm-skills.git /tmp/claude-mpm-skills
mkdir -p .claude/skills
cp -r /tmp/claude-mpm-skills/toolchains/databases/mongodb .claude/skills/mongodb
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mongodb in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Mongodb on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Mongodb is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

MongoDB - Document Database Patterns

Overview

MongoDB is a document-oriented NoSQL database that stores data in flexible, JSON-like documents. It excels at handling unstructured or semi-structured data, hierarchical relationships, and scenarios requiring horizontal scaling.

Key Features:

  • Flexible schema (schemaless documents)
  • Rich query language with secondary indexes
  • Aggregation framework for analytics
  • Horizontal scaling (sharding)
  • Replica sets for high availability
  • Change streams for real-time data
  • Geospatial and full-text search

When to Use MongoDB:

  • Rapidly evolving schemas
  • Hierarchical/nested data (embedded documents)
  • Real-time analytics with aggregation
  • Geospatial applications
  • Content management systems
  • IoT data ingestion
  • Catalog/inventory systems

When NOT to Use MongoDB:

  • Complex multi-table joins (use RDBMS)
  • ACID transactions across many documents (improved in 4.0+, but limited)
  • Strict schema enforcement requirements

Schema Design Fundamentals

Document Structure

javascript
// MongoDB document (BSON format)
{
  "_id": ObjectId("507f1f77bcf86cd799439011"),  // Primary key (auto-generated)
  "email": "alice@example.com",
  "name": "Alice Johnson",
  "profile": {                                   // Embedded document
    "bio": "Software developer",
    "avatar": "https://example.com/avatar.jpg",
    "social": {
      "twitter": "@alice",
      "github": "alice-dev"
    }
  },
  "tags": ["developer", "python", "mongodb"],    // Array field
  "createdAt": ISODate("2024-01-15T10:30:00Z"),
  "updatedAt": ISODate("2024-01-20T14:22:00Z")
}

Embedded vs Referenced Documents

Embedded (Denormalized) - Store related data in same document:

javascript
// Embedded: Good for 1:1 or 1:Few relationships
// User with embedded address
{
  "_id": ObjectId("..."),
  "name": "Alice",
  "address": {
    "street": "123 Main St",
    "city": "San Francisco",
    "zipCode": "94102"
  }
}

// Embedded: Order with line items (1:Many bounded)
{
  "_id": ObjectId("..."),
  "orderNumber": "ORD-2024-001",
  "customer": { "name": "Alice", "email": "alice@example.com" },
  "items": [
    { "productId": "SKU001", "name": "Widget", "quantity": 2, "price": 29.99 },
    { "productId": "SKU002", "name": "Gadget", "quantity": 1, "price": 49.99 }
  ],
  "total": 109.97
}

When to Embed:

  • Data is queried together frequently
  • Child data doesn't make sense without parent
  • 1:1 or 1:Few relationships
  • Child data is bounded (won't grow unbounded)
  • Data doesn't need to be accessed independently

Referenced (Normalized) - Store references to other documents:

javascript
// Referenced: Good for 1:Many unbounded or Many:Many
// User document
{
  "_id": ObjectId("user123"),
  "name": "Alice",
  "email": "alice@example.com"
}

// Posts collection (references user)
{
  "_id": ObjectId("post456"),
  "authorId": ObjectId("user123"),   // Reference to user
  "title": "MongoDB Schema Design",
  "content": "...",
  "commentCount": 42
}

// Comments collection (references post)
{
  "_id": ObjectId("comment789"),
  "postId": ObjectId("post456"),     // Reference to post
  "authorId": ObjectId("user999"),   // Reference to commenter
  "text": "Great article!",
  "createdAt": ISODate("2024-01-20T10:00:00Z")
}

When to Reference:

  • Many:Many relationships
  • 1:Many with unbounded growth (comments, logs)
  • Data is accessed independently
  • Document size would exceed 16MB limit
  • Need atomic updates on referenced document

Hybrid Pattern (Extended Reference)

javascript
// Post with denormalized author info + reference
{
  "_id": ObjectId("post456"),
  "title": "MongoDB Best Practices",
  "content": "...",
  "author": {
    "_id": ObjectId("user123"),      // Reference for lookups
    "name": "Alice",                 // Denormalized for display
    "avatar": "https://..."          // Frequently needed fields
  },
  "commentCount": 42,
  "lastCommentAt": ISODate("...")
}

Query Patterns

Basic CRUD Operations

javascript
// Find documents
db.users.find({ email: "alice@example.com" })
db.users.find({ age: { $gte: 18, $lte: 65 } })
db.users.find({ tags: { $in: ["developer", "designer"] } })

// Find with projection (select specific fields)
db.users.find(
  { status: "active" },
  { name: 1, email: 1, _id: 0 }  // Include name, email; exclude _id
)

// Find one
db.users.findOne({ email: "alice@example.com" })

// Insert
db.users.insertOne({ name: "Bob", email: "bob@example.com" })
db.users.insertMany([
  { name: "Charlie", email: "charlie@example.com" },
  { name: "Diana", email: "diana@example.com" }
])

// Update
db.users.updateOne(
  { email: "alice@example.com" },
  { $set: { name: "Alice Updated", updatedAt: new Date() } }
)

db.users.updateMany(
  { status: "inactive" },
  { $set: { archived: true } }
)

// Upsert (update or insert)
db.users.updateOne(
  { email: "new@example.com" },
  { $set: { name: "New User", createdAt: new Date() } },
  { upsert: true }
)

// Delete
db.users.deleteOne({ email: "bob@example.com" })
db.users.deleteMany({ status: "deleted" })

Query Operators

javascript
// Comparison
db.products.find({ price: { $gt: 100 } })        // Greater than
db.products.find({ price: { $gte: 100 } })       // Greater than or equal
db.products.find({ price: { $lt: 50 } })         // Less than
db.products.find({ price: { $lte: 50 } })        // Less than or equal
db.products.find({ price: { $ne: 0 } })          // Not equal
db.products.find({ category: { $in: ["A", "B"] } })   // In array
db.products.find({ category: { $nin: ["C", "D"] } })  // Not in array

// Logical
db.users.find({ $and: [{ age: { $gte: 18 } }, { status: "active" }] })
db.users.find({ $or: [{ role: "admin" }, { role: "moderator" }] })
db.users.find({ age: { $not: { $lt: 18 } } })

// Element
db.users.find({ middleName: { $exists: true } })  // Field exists
db.users.find({ age: { $type: "number" } })       // Field type

// Array
db.posts.find({ tags: "mongodb" })                // Contains element
db.posts.find({ tags: { $all: ["mongodb", "database"] } })  // Contains all
db.posts.find({ tags: { $size: 3 } })             // Array size
db.posts.find({ "tags.0": "featured" })           // First element

// Embedded documents
db.users.find({ "address.city": "San Francisco" })
db.users.find({ "profile.social.twitter": { $exists: true } })

// Regex
db.users.find({ name: { $regex: /^alice/i } })
db.users.find({ email: { $regex: /@example\.com$/ } })

Update Operators

javascript
// Field updates
db.users.updateOne(
  { _id: userId },
  {
    $set: { name: "New Name" },           // Set field value
    $unset: { temporaryField: "" },       // Remove field
    $rename: { oldName: "newName" },      // Rename field
    $inc: { loginCount: 1 },              // Increment
    $mul: { price: 1.1 },                 // Multiply
    $min: { lowestScore: 50 },            // Set if less than current
    $max: { highestScore: 100 },          // Set if greater than current
    $currentDate: { updatedAt: true }     // Set to current date
  }
)

// Array updates
db.posts.updateOne(
  { _id: postId },
  {
    $push: { tags: "new-tag" },           // Add to array
    $addToSet: { tags: "unique-tag" },    // Add if not exists
    $pop: { tags: 1 },                    // Remove last (-1 for first)
    $pull: { tags: "old-tag" },           // Remove specific value
    $pullAll: { tags: ["a", "b"] }        // Remove multiple values
  }
)

// Array with modifiers
db.posts.updateOne(
  { _id: postId },
  {
    $push: {
      comments: {
        $each: [comment1, comment2],      // Add multiple
        $sort: { createdAt: -1 },         // Sort after push
        $slice: -100                      // Keep only last 100
      }
    }
  }
)

// Positional update (update matched array element)
db.posts.updateOne(
  { _id: postId, "comments._id": commentId },
  { $set: { "comments.$.text": "Updated comment" } }
)

// Update all matching array elements
db.posts.updateOne(
  { _id: postId },
  { $set: { "comments.$[elem].read": true } },
  { arrayFilters: [{ "elem.userId": currentUserId }] }
)

Indexing Strategies

Index Types

javascript
// Single field index
db.users.createIndex({ email: 1 })         // Ascending
db.users.createIndex({ createdAt: -1 })    // Descending

// Compound index
db.orders.createIndex({ customerId: 1, createdAt: -1 })

// Unique index
db.users.createIndex({ email: 1 }, { unique: true })

// Partial index (index subset of documents)
db.orders.createIndex(
  { createdAt: 1 },
  { partialFilterExpression: { status: "pending" } }
)

// TTL index (auto-delete old documents)
db.sessions.createIndex(
  { createdAt: 1 },
  { expireAfterSeconds: 3600 }  // Delete after 1 hour
)

// Text index (full-text search)
db.articles.createIndex({ title: "text", content: "text" })
// Query: db.articles.find({ $text: { $search: "mongodb tutorial" } })

// Geospatial index
db.locations.createIndex({ coordinates: "2dsphere" })
// Query: db.locations.find({
//   coordinates: {
//     $near: {
//       $geometry: { type: "Point", coordinates: [-122.4, 37.8] },
//       $maxDistance: 5000  // meters
//     }
//   }
// })

// Multikey index (for array fields - automatic)
db.posts.createIndex({ tags: 1 })

// Hashed index (for sharding)
db.users.createIndex({ email: "hashed" })

Index Best Practices

javascript
// ESR Rule for compound indexes: Equality, Sort, Range
// Query: find users where status = "active", sorted by createdAt, age > 18
// Index: { status: 1, createdAt: -1, age: 1 }
//        ^Equality   ^Sort          ^Range

// Covered queries (all fields in index)
db.users.createIndex({ email: 1, name: 1, status: 1 })
db.users.find(
  { email: "alice@example.com" },
  { name: 1, status: 1, _id: 0 }  // All fields from index
)
// Check with explain: "totalDocsExamined": 0

// Analyze query performance
db.users.find({ email: "alice@example.com" }).explain("executionStats")
// Look for:
// - IXSCAN (index scan) vs COLLSCAN (collection scan)
// - totalDocsExamined vs totalKeysExamined
// - executionTimeMillis

// List indexes
db.users.getIndexes()

// Drop index
db.users.dropIndex("email_1")
db.users.dropIndex({ email: 1, name: 1 })

Aggregation Pipeline

Pipeline Stages

javascript
// Basic aggregation structure
db.orders.aggregate([
  { $match: { status: "completed" } },           // Filter (like WHERE)
  { $group: { _id: "$customerId", total: { $sum: "$amount" } } },  // Group
  { $sort: { total: -1 } },                      // Sort
  { $limit: 10 }                                 // Limit results
])

// Common stages
db.collection.aggregate([
  // $match - Filter documents
  { $match: { status: "active", createdAt: { $gte: ISODate("2024-01-01") } } },

  // $project - Reshape documents (include/exclude/compute fields)
  { $project: {
    name: 1,
    email: 1,
    fullName: { $concat: ["$firstName", " ", "$lastName"] },
    yearCreated: { $year: "$createdAt" }
  }},

  // $addFields - Add new fields (keeps existing)
  { $addFields: {
    totalPrice: { $multiply: ["$price", "$quantity"] }
  }},

  // $group - Group and aggregate
  { $group: {
    _id: "$category",
    count: { $sum: 1 },
    totalRevenue: { $sum: "$amount" },
    avgPrice: { $avg: "$price" },
    maxPrice: { $max: "$price" },
    products: { $push: "$name" },          // Collect into array
    uniqueTags: { $addToSet: "$tag" }      // Unique values
  }},

  // $sort
  { $sort: { totalRevenue: -1, count: 1 } },

  // $skip and $limit (pagination)
  { $skip: 20 },
  { $limit: 10 },

  // $unwind - Deconstruct array field
  { $unwind: "$tags" },
  // { tags: ["a", "b"] } becomes { tags: "a" }, { tags: "b" }

  // $lookup - Join collections
  { $lookup: {
    from: "users",
    localField: "authorId",
    foreignField: "_id",
    as: "author"
  }},
  { $unwind: "$author" },  // Convert single-element array to object

  // $facet - Multiple pipelines in parallel
  { $facet: {
    results: [{ $skip: 0 }, { $limit: 10 }],
    totalCount: [{ $count: "count" }]
  }}
])

Real-World Aggregation Examples

javascript
// Sales analytics by month
db.orders.aggregate([
  { $match: { status: "completed" } },
  { $group: {
    _id: {
      year: { $year: "$createdAt" },
      month: { $month: "$createdAt" }
    },
    totalSales: { $sum: "$amount" },
    orderCount: { $sum: 1 },
    avgOrderValue: { $avg: "$amount" }
  }},
  { $sort: { "_id.year": -1, "_id.month": -1 } }
])

// Top customers with order details
db.orders.aggregate([
  { $match: { createdAt: { $gte: ISODate("2024-01-01") } } },
  { $group: {
    _id: "$customerId",
    totalSpent: { $sum: "$amount" },
    orderCount: { $sum: 1 },
    lastOrder: { $max: "$createdAt" }
  }},
  { $sort: { totalSpent: -1 } },
  { $limit: 10 },
  { $lookup: {
    from: "customers",
    localField: "_id",
    foreignField: "_id",
    as: "customer"
  }},
  { $unwind: "$customer" },
  { $project: {
    customerName: "$customer.name",
    customerEmail: "$customer.email",
    totalSpent: 1,
    orderCount: 1,
    lastOrder: 1
  }}
])

// Product category performance with nested unwind
db.orders.aggregate([
  { $unwind: "$items" },
  { $lookup: {
    from: "products",
    localField: "items.productId",
    foreignField: "_id",
    as: "product"
  }},
  { $unwind: "$product" },
  { $group: {
    _id: "$product.category",
    totalRevenue: { $sum: { $multiply: ["$items.quantity", "$items.price"] } },
    unitsSold: { $sum: "$items.quantity" },
    uniqueProducts: { $addToSet: "$product._id" }
  }},
  { $addFields: {
    uniqueProductCount: { $size: "$uniqueProducts" }
  }},
  { $sort: { totalRevenue: -1 } }
])

Transactions (MongoDB 4.0+)

javascript
// Multi-document transaction
const session = client.startSession();

try {
  session.startTransaction();

  // Transfer money between accounts
  await accounts.updateOne(
    { _id: fromAccountId },
    { $inc: { balance: -amount } },
    { session }
  );

  await accounts.updateOne(
    { _id: toAccountId },
    { $inc: { balance: amount } },
    { session }
  );

  await transactions.insertOne(
    {
      from: fromAccountId,
      to: toAccountId,
      amount: amount,
      createdAt: new Date()
    },
    { session }
  );

  await session.commitTransaction();
} catch (error) {
  await session.abortTransaction();
  throw error;
} finally {
  session.endSession();
}

Change Streams

javascript
// Watch for changes in real-time
const changeStream = db.orders.watch([
  { $match: { "fullDocument.status": "pending" } }
]);

changeStream.on("change", (change) => {
  console.log("Change detected:", change.operationType);
  console.log("Document:", change.fullDocument);

  if (change.operationType === "insert") {
    // Process new pending order
    processNewOrder(change.fullDocument);
  }
});

// Resume from specific point (for fault tolerance)
const resumeToken = change._id;
const changeStream = db.orders.watch([], {
  resumeAfter: resumeToken,
  fullDocument: "updateLookup"  // Include full document on updates
});

Spring Data MongoDB Integration

Entity Class

java
@Document(collection = "users")
@Data
@Builder
@NoArgsConstructor
@AllArgsConstructor
public class User {

    @Id
    private String id;  // Maps to _id

    @Indexed(unique = true)
    private String email;

    private String name;

    @Field("password_hash")  // Custom field name
    private String passwordHash;

    @DBRef  // Reference to another collection
    private List<Role> roles;

    private Address address;  // Embedded document

    private List<String> tags;

    @CreatedDate
    private LocalDateTime createdAt;

    @LastModifiedDate
    private LocalDateTime updatedAt;

    @Version
    private Long version;  // Optimistic locking
}

@Data
public class Address {
    private String street;
    private String city;
    private String zipCode;

    @GeoSpatialIndexed(type = GeoSpatialIndexType.GEO_2DSPHERE)
    private GeoJsonPoint location;
}

Repository Interface

java
public interface UserRepository extends MongoRepository<User, String> {

    // Derived queries
    Optional<User> findByEmail(String email);
    List<User> findByNameContainingIgnoreCase(String name);
    List<User> findByTagsContaining(String tag);

    // Custom query with @Query
    @Query("{ 'createdAt': { $gte: ?0 }, 'status': 'active' }")
    List<User> findActiveUsersCreatedAfter(LocalDateTime date);

    // Projection
    @Query(value = "{ 'email': ?0 }", fields = "{ 'name': 1, 'email': 1 }")
    Optional<UserSummary> findSummaryByEmail(String email);

    // Aggregation
    @Aggregation(pipeline = {
        "{ $match: { 'status': 'active' } }",
        "{ $group: { _id: '$country', count: { $sum: 1 } } }",
        "{ $sort: { count: -1 } }"
    })
    List<CountryStats> getActiveUsersByCountry();

    // Geospatial query
    List<User> findByAddressLocationNear(Point location, Distance distance);
}

MongoTemplate for Complex Queries

java
@Service
@RequiredArgsConstructor
public class UserService {

    private final MongoTemplate mongoTemplate;

    public List<User> searchUsers(UserSearchCriteria criteria) {
        Query query = new Query();

        if (criteria.getName() != null) {
            query.addCriteria(Criteria.where("name")
                .regex(criteria.getName(), "i"));
        }

        if (criteria.getTags() != null && !criteria.getTags().isEmpty()) {
            query.addCriteria(Criteria.where("tags")
                .in(criteria.getTags()));
        }

        if (criteria.getCreatedAfter() != null) {
            query.addCriteria(Criteria.where("createdAt")
                .gte(criteria.getCreatedAfter()));
        }

        query.with(Sort.by(Sort.Direction.DESC, "createdAt"));
        query.with(PageRequest.of(criteria.getPage(), criteria.getSize()));

        return mongoTemplate.find(query, User.class);
    }

    public AggregationResults<UserStats> getUserStatsByStatus() {
        Aggregation aggregation = Aggregation.newAggregation(
            Aggregation.match(Criteria.where("createdAt")
                .gte(LocalDateTime.now().minusMonths(1))),
            Aggregation.group("status")
                .count().as("count")
                .avg("loginCount").as("avgLogins"),
            Aggregation.sort(Sort.Direction.DESC, "count")
        );

        return mongoTemplate.aggregate(aggregation, "users", UserStats.class);
    }

    public void bulkUpdateStatus(List<String> userIds, String newStatus) {
        BulkOperations bulkOps = mongoTemplate.bulkOps(
            BulkOperations.BulkMode.UNORDERED, User.class);

        for (String userId : userIds) {
            Query query = Query.query(Criteria.where("_id").is(userId));
            Update update = Update.update("status", newStatus)
                .currentDate("updatedAt");
            bulkOps.updateOne(query, update);
        }

        bulkOps.execute();
    }
}

Performance Optimization

Connection Pooling

java
// Spring Boot configuration
spring:
  data:
    mongodb:
      uri: mongodb://localhost:27017/mydb
      auto-index-creation: false  # Create indexes manually in production

# Connection pool settings (via URI)
# mongodb://localhost:27017/mydb?maxPoolSize=50&minPoolSize=10&maxIdleTimeMS=30000

Query Optimization

javascript
// Use projections to limit returned fields
db.users.find({ status: "active" }, { name: 1, email: 1 })

// Use hint to force specific index
db.users.find({ status: "active" }).hint({ status: 1, createdAt: -1 })

// Limit results for pagination
db.users.find().sort({ createdAt: -1 }).skip(20).limit(10)

// Use $exists: false for missing fields (can use index)
db.users.createIndex({ optionalField: 1 }, { sparse: true })
db.users.find({ optionalField: { $exists: true } })

Schema Optimization

javascript
// Avoid large arrays (cap at reasonable size)
// Use bucketing pattern for time-series data
{
  _id: "sensor1_2024-01-15",
  sensorId: "sensor1",
  date: ISODate("2024-01-15"),
  readings: [
    { ts: ISODate("..."), value: 23.5 },
    { ts: ISODate("..."), value: 24.1 },
    // ... up to N readings per bucket
  ],
  count: 288  // Track count for full bucket detection
}

// Pre-aggregate for reporting
{
  _id: "stats_2024-01",
  month: "2024-01",
  totalOrders: 1523,
  totalRevenue: 152300.50,
  avgOrderValue: 100.00,
  topProducts: ["SKU001", "SKU002", "SKU003"]
}

Best Practices

1. Schema Design

javascript
// Embed when: data is queried together, bounded arrays
// Reference when: unbounded arrays, many-to-many, independent access
// Use extended reference pattern for frequently accessed fields

2. Indexing

javascript
// Create indexes for query patterns, not just fields
// Use compound indexes following ESR rule
// Monitor slow queries: db.setProfilingLevel(1, { slowms: 100 })
// Avoid indexing low-cardinality fields alone

3. Write Operations

javascript
// Use bulk operations for multiple writes
// Avoid unbounded array growth
// Use write concern appropriate to durability needs
db.orders.insertOne(doc, { writeConcern: { w: "majority" } })

4. Read Operations

javascript
// Always use projections to limit returned data
// Use explain() to verify index usage
// Prefer aggregation over multiple queries
// Use read preference for scaling reads
db.orders.find().readPref("secondaryPreferred")

5. Connection Management

javascript
// Use connection pooling
// Set appropriate pool size (default: 100)
// Handle connection errors with retry logic
// Close connections properly on shutdown

Common Pitfalls

Unbounded array growth:

javascript
// BAD: Comments array grows forever
{ _id: "post1", comments: [...thousands of comments...] }

// GOOD: Separate collection with references
{ _id: "comment1", postId: "post1", text: "..." }

Missing indexes:

javascript
// Always create indexes for query patterns
// Check with explain() - look for COLLSCAN (bad)
db.users.find({ email: "..." }).explain()

Over-indexing:

javascript
// Each index adds write overhead
// Only index fields used in queries
// Monitor index usage: db.users.aggregate([{ $indexStats: {} }])

Resources

Related Skills

When using MongoDB, consider these complementary skills:

  • spring-boot: Java framework integration with Spring Data MongoDB
  • docker: Running MongoDB in containers
  • nodejs: MongoDB with Mongoose ODM
  • aggregation-pipelines: Advanced analytics patterns

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Mongodb AI skill do?

MongoDB - NoSQL document database with flexible schema design, aggregation pipelines, indexing strategies, and Spring Data integration

Why use Mongodb on TypingMind?

Because you install it once and use it with any model. Mongodb is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Mongodb in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bobmatnyc/claude-mpm-skills/tree/main/toolchains/databases/mongodb. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Mongodb?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Mongodb?

As many as you like. As long as a model supports skills, you can use Mongodb with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Mongodb AI skill free?

Yes. It is published on GitHub by bobmatnyc under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Related AI Skills

View all
obra logo

Systematic Debugging

obra
CommunityPopular

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

25.8K
288.1K
10 files
View
obra logo

Test Driven Development

obra
CommunityPopular

Use when implementing any feature or bugfix, before writing implementation code

25.8K
288.1K
1 files
View
obra logo

Using Git Worktrees

obra
CommunityPopular

Use when starting feature work that needs isolation from current workspace or before executing implementation plans - ensures an isolated workspace exists via native tools or git worktree fallback

25.8K
288.1K
Instructions
View
obra logo

Using Superpowers

obra
CommunityPopular

Use when starting any conversation - establishes how to find and use skills, requiring skill invocation before ANY response including clarifying questions

25.8K
288.1K
5 files
View

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇