mongodb
MongoDB and Mongoose schema design, querying, aggregation pipelines, and performance optimization.
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What it does
Covers both native MongoDB driver and Mongoose ODM with connection setup, CRUD operations, and graceful shutdown patterns
Schema design guidance including embedded vs. referenced documents, virtuals, validation, indexes, and text search configuration
Query operations with 20+ operators (comparison, logical, array, regex) plus find, update, and upsert patterns with query builders
Aggr
Installation Guide
How to use mongodb on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
mongodb
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches mongodb from hoodini/ai-agents-skills and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate mongodb. Access via /mongodb in your agent's command palette.
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
MongoDB & Mongoose
Build and query MongoDB databases with best practices.
Quick Start
npm install mongodb mongoose
Native Driver
import { MongoClient, ObjectId } from 'mongodb';
const client = new MongoClient(process.env.MONGODB_URI!);
const db = client.db('myapp');
const users = db.collection('users');
// Connect
await client.connect();
// CRUD Operations
await users.insertOne({ name: 'Alice', email: '[email protected]' });
const user = await users.findOne({ email: '[email protected]' });
await users.updateOne({ _id: user._id }, { $set: { name: 'Alice Smith' } });
await users.deleteOne({ _id: user._id });
Mongoose Setup
import mongoose from 'mongoose';
await mongoose.connect(process.env.MONGODB_URI!, {
maxPoolSize: 10,
serverSelectionTimeoutMS: 5000,
socketTimeoutMS: 45000,
});
// Connection events
mongoose.connection.on('connected', () => console.log('MongoDB connected'));
mongoose.connection.on('error', (err) => console.error('MongoDB error:', err));
mongoose.connection.on('disconnected', () => console.log('MongoDB disconnected'));
// Graceful shutdown
process.on('SIGINT', async () => {
await mongoose.connection.close();
process.exit(0);
});
Schema Design
Basic Schema
import mongoose, { Schema, Document, Model } from 'mongoose';
interface IUser extends Document {
email: string;
name: string;
password: string;
role: 'user' | 'admin';
profile: {
avatar?: string;
bio?: string;
};
createdAt: Date;
updatedAt: Date;
}
const userSchema = new Schema<IUser>({
email: {
type: String,
required: [true, 'Email is required'],
unique: true,
lowercase: true,
trim: true,
match: [/^\S+@\S+\.\S+$/, 'Invalid email format'],
},
name: {
type: String,
required: true,
trim: true,
minlength: 2,
maxlength: 100,
},
password: {
type: String,
required: true,
select: false, // Never return password by default
},
role: {
type: String,
enum: ['user', 'admin'],
default: 'user',
},
profile: {
avatar: String,
bio: { type: String, maxlength: 500 },
},
}, {
timestamps: true, // Adds createdAt, updatedAt
toJSON: {
transform(doc, ret) {
delete ret.password;
delete ret.__v;
return ret;
},
},
});
// Indexes
userSchema.index({ email: 1 });
userSchema.index({ createdAt: -1 });
userSchema.index({ name: 'text', 'profile.bio': 'text' }); // Text search
const User: Model<IUser> = mongoose.model('User', userSchema);
Embedded Documents vs References
// ✅ Embed when: Data is read together, doesn't grow unbounded
const orderSchema = new Schema({
customer: {
name: String,
email: String,
address: {
street: String,
city: String,
country: String,
},
},
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Use Cases
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
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Reviews
- CChaitanya Patil★★★★★Dec 28, 2024
We added mongodb from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- MMia Mehta★★★★★Dec 28, 2024
mongodb has been reliable in day-to-day use. Documentation quality is above average for community skills.
- AAma Haddad★★★★★Dec 24, 2024
Useful defaults in mongodb — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- SSakura White★★★★★Dec 16, 2024
I recommend mongodb for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- AAanya Harris★★★★★Dec 4, 2024
Keeps context tight: mongodb is the kind of skill you can hand to a new teammate without a long onboarding doc.
- AAditi Harris★★★★★Nov 23, 2024
I recommend mongodb for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- PPiyush G★★★★★Nov 19, 2024
mongodb fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- CCharlotte Haddad★★★★★Nov 11, 2024
mongodb has been reliable in day-to-day use. Documentation quality is above average for community skills.
- VValentina Bhatia★★★★★Nov 7, 2024
Keeps context tight: mongodb is the kind of skill you can hand to a new teammate without a long onboarding doc.
- RRen Sethi★★★★★Oct 26, 2024
Registry listing for mongodb matched our evaluation — installs cleanly and behaves as described in the markdown.
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