# Mongodb Search And AI

Covers everything about search in MongoDB: Atlas Search (full-text), Vector Search...

- Repository: https://github.com/fcakyon/claude-codex-settings (1195 stars)
- Category: AI and agents
- License: Apache-2.0
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add fcakyon/claude-codex-settings --skill mongodb-search-and-ai -g -a claude-code -y`
Codex: `npx skills add fcakyon/claude-codex-settings --skill mongodb-search-and-ai -g -a codex -y`
Cursor: `npx skills add fcakyon/claude-codex-settings --skill mongodb-search-and-ai -g -a cursor -y`
Gemini CLI: `npx skills add fcakyon/claude-codex-settings --skill mongodb-search-and-ai -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add fcakyon/claude-codex-settings`

## Description

Covers everything about search in MongoDB: Atlas Search (full-text), Vector Search (semantic), and Hybrid Search. Use this skill for ANY question about MongoDB search, including open-ended and explanatory ones asked before the user has any data or use case, 'how does search work in MongoDB?', 'I'm new to MongoDB search, where do I start?', 'what's the difference between Atlas Search and Vector Search?', 'which search type should I use?', 'what is vector search?', 'how does automated embedding work?', 'can you explain hybrid search?'. Do not answer these from general knowledge; use this skill. Also use to build search features: full-text queries, autocomplete, fuzzy matching, faceted search, semantic similarity, embeddings, RAG, text containment or substring matching ('contains', 'includes'), case-insensitive or multi-field text search, and filtering across many fields. Provides workflows for choosing a search type, creating indexes, writing queries, and optimizing performance via the MongoDB MCP server.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# MongoDB Search and AI Recommendations Skill

You are helping MongoDB users implement, optimize, and troubleshoot Atlas Search (lexical), Vector Search (semantic), and Hybrid Search (combined) solutions.

## Core Principles

1. **Understand before building** - Validate the use case to ensure you recommend the right solution
2. **Always inspect first** - Check existing indexes and schema before making recommendations
3. **Explain before executing** - Describe what indexes will be created and require explicit approval
4. **Handle read-only scenarios** - Without `create`, `update`, or `delete` operation tools you are in read-only mode; hand the user index JSON to create themselves
5. **Explain in accessible language** - Describe technical concepts and map business requirements to technical implementations in terms the user can follow

## Workflow

### 0. Check for a Quickstart Request

`references/quick-start.md` is a guided walkthrough that builds semantic, keyword, and hybrid search against the `sample_mflix` sample dataset. It is a teaching tour, not a way to build search for the user's own data.

**Do not read `references/quick-start.md` until the user has chosen the tour.** A general request like "I'm new to search, help me get started" can just as easily mean "help me add search to my app." Use AskUserQuestion:

> "Two ways I can take this, which fits better?"

- **Guided tour on sample data**, we load Atlas's sample movie dataset and build semantic, keyword, and hybrid search on it step by step. Best for learning how each type works.
- **Build search for my own data**, tell me what you're searching and I'll design the indexes and queries for your collections.

Prompts that should trigger this offer, rather than being answered directly as a question:

The rest: https://github.com/fcakyon/claude-codex-settings/blob/HEAD/plugins/mongodb-skills/skills/mongodb-search-and-ai/SKILL.md