mongodb
MongoDB document database with aggregation pipeline and Atlas. Use for document storage.
Best use case
mongodb is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
MongoDB document database with aggregation pipeline and Atlas. Use for document storage.
Teams using mongodb should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/mongodb/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How mongodb Compares
| Feature / Agent | mongodb | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
MongoDB document database with aggregation pipeline and Atlas. Use for document storage.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# MongoDB
MongoDB is a document database. It stores data in JSON-like documents (BSON). It is the most popular NoSQL database, known for flexibility and scalability.
## When to Use
- **Rapid Prototyping**: Schema-less design allows iterating fast without migrations.
- **Content Management**: Storing diverse assets with varying metadata.
- **Catalogs**: Product catalogs where each product has different attributes (size, color, wattage).
## Quick Start
```javascript
// Using Mongoose (Node.js)
const kittySchema = new mongoose.Schema({
name: String,
});
const Kitten = mongoose.model("Kitten", kittySchema);
const silence = new Kitten({ name: "Silence" });
await silence.save();
```
## Core Concepts
### Documents (BSON)
Data is stored in "documents" (JSON objects) inside "collections" (Tables).
`{ "_id": 1, "name": "Apple", "price": 10 }`
### Embedded Data vs References
- **Embed**: Store related data inside the document for fast reads. (e.g., Comments inside a Post).
- **Reference**: Store the ID and look it up (`$lookup`) for many-to-many relationships.
### Sharding
MongoDB scales horizontally by splitting data across multiple servers (shards) based on a "shard key".
## Best Practices (2025)
**Do**:
- **Use Version 8.0+**: For performance gains in time-series and queryable encryption.
- **Index Early**: "Compass" (GUI) or "Atlas Performance Advisor" will tell you when you miss indexes.
- **Limit Array Growth**: Don't use unbounded arrays (e.g., logging every login in the user document).
**Don't**:
- **Don't join everything**: MongoDB supports `$lookup` (JOINS), but overuse kills performance. Embed data if accessed together.
- **Don't ignore Document Size**: Max document size is 16MB.
## References
- [MongoDB Manual](https://www.mongodb.com/docs/manual/)
- [MongoDB University](https://learn.mongodb.com/)Related Skills
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