mongodb

mongodb is a skill for Claude Code, Codex from G1Joshi/Agent-Skills. It costs 39 tokens per session (1,255 once invoked), scanned A, original, MIT.

A document database that stores flexible, JSON-like records in collections instead of rows in tables.

In plain words
What is it for?
Use it for prototypes, content systems, and product catalogs where records may have different fields.
Why use it?
It lets data structures change as an application evolves, reducing the need for database migrations. It also supports aggregation queries and distributed scaling through MongoDB Atlas.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it for prototypes, content systems, and product catalogs where records may have different fields.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/g1joshi/agent-skills/mongodb
Install

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.

Any agent
npx skills add G1Joshi/Agent-Skills --skill mongodb
Clone the repo
git clone --depth 1 https://github.com/G1Joshi/Agent-Skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for mongodb

README.md
[![agentmods](https://agentmods.dev/badge/skills/g1joshi/agent-skills/mongodb/github.svg)](https://agentmods.dev/skills/g1joshi/agent-skills/mongodb)
Your own site
<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.

agentmods 80×15 button for mongodb

Your own site · 80×15
<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>
Per session 39 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,255 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 24d ago against content hash 27821d341e71, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

skills/databases/mongodb/SKILL.md · 150 lines

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 },
]);

Read the full file on GitHub · 150 lines

Changes

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.

  1. 24d ago Changed 27821d341e71
  2. 28d ago Changed · +88 lines · +22 tokens per session eb7b5711702e
  3. 1mo ago First seen · 62 lines · 17 tokens per session scan A 88448b1e9bea

Subscribe to this mod's changes

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.

Related

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…

addyosmani/agent-skills · 69 tokens

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.

mongodb/agent-skills · 52 tokens

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.

khasky/awesome-agent-skills · 49 tokens

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.

hamzabellouch/agent-skills · 54 tokens

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.

borhen68/SkillEngine · 59 tokens

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…

mongodb/agent-skills · 162 tokens