Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add G1Joshi/Agent-Skills --skill mongodbgit clone --depth 1 https://github.com/G1Joshi/Agent-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/g1joshi/agent-skills/mongodb)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/mongodb"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/mongodb/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/g1joshi/agent-skills/mongodb"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/mongodb.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00039 | $0.01255 |
| Opus 5.5 | $0.00016 | $0.00502 |
| Sonnet 5.5 | $0.00008 | $0.00251 |
| Haiku 4.5 | $0.00004 | $0.00126 |
Grade A, and why
mongodb scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 24d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MongoDB
MongoDB is a document database. It stores data in JSON-like documents (BSON). It is the most popular NoSQL database, known for flexibility and scalability.
When to Use
- Flexible JSON Document Storage: Modeling evolving, nested domain entities with polymorphic schemas in BSON.
- High-Velocity Operational Data Stores: Content management, e-commerce product catalogs, and user personalization platforms.
- Rich Aggregation Pipelines: Transforming, joining ($lookup), grouping, and projecting analytical data streams in real time.
- Global Sharding & Geospatial Indexing: Scaling collections horizontally across shards and executing 2dsphere geo queries.
Quick Start
// 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
BSON Documents & Schema Flexibility
Data is stored as binary JSON (BSON), supporting native dates, 64-bit integers, decimals, and geospatial points:
// MongoDB Document in collections/orders
{
"_id": ObjectId("66f6ab42d1e1c3a628a58a98"),
"orderNumber": "ORD-2026-901",
"customer": { "id": "cust_12", "email": "[email protected]" },
"items": [
{ "sku": "WIDGET-01", "qty": 2, "price": 49.99 }
],
"shippingAddress": {
"type": "Point",
"coordinates": [-73.9851, 40.7488] // 2dsphere GeoJSON
},
"status": "PROCESSING",
"createdAt": ISODate("2026-09-27T10:00:00Z")
}
Powerful Multi-Stage Aggregation Framework
Transforms and aggregates documents through sequential pipeline stages:
db.orders.aggregate([
{
$match: { status: "COMPLETED", createdAt: { $gte: ISODate("2026-01-01") } },
},
{ $unwind: "$items" },
{
$group: {
_id: "$items.sku",
totalRevenue: { $sum: { $multiply: ["$items.qty", "$items.price"] } },
unitsSold: { $sum: "$items.qty" },
},
},
{ $sort: { totalRevenue: -1 } },
{ $limit: 5 },
]);
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 24d ago Changed 27821d341e71
- 28d ago Changed · +88 lines · +22 tokens per session eb7b5711702e
- 1mo ago First seen · 62 lines · 17 tokens per session scan A 88448b1e9bea
mongodb is a skill published in the GitHub repository G1Joshi/Agent-Skills (15 stars, last pushed 25d ago), licensed MIT. It adds 39 tokens to every session and 1,255 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
deprecation-and-migration
Manages deprecation and migration. Use when removing old systems, APIs, or features. Use when migrating users from one implementation to another. Use when migrating a database schema in production, such as renaming or dropping a column without downtime (expand/contract). Use when deciding whether to maintain or sunset…
mongodb-qe-size-estimation
Estimates the storage and memory impact of encrypting fields in collections with Queryable Encryption (QE) enabled. Do NOT use for collections that use Client-Side Field Level Encryption (CSFLE) instead of QE.
awesome-database-audit
Read-only audit of a database layer: schema anti-patterns, query and index fit, integrity, concurrency, migrations, tenancy, with a verdict. Use when asked to review a schema, migrations or data model.
mongodb-schema-and-indexing
Production-grade MongoDB schema design patterns, indexing strategies, ESR rule (Equality, Sort, Range), aggregation framework optimization, and sharding/partitioning architectures. Use when designing MongoDB schemas, tuning query performance, or building aggregation pipelines.
data-engineering
Guides data pipeline design, ETL/ELT workflows, schema evolution, and data quality assurance. Use when building data pipelines, designing data warehouses, migrating schemas, or ensuring data integrity across systems. Use when you need reliable, testable, and observable data flows.
mongodb-natural-language-querying
Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with…