Mongodb Search And AI
Covers everything about search in MongoDB: Atlas Search (full-text), Vector Search...
Install
npx skills add fcakyon/claude-codex-settings --skill mongodb-search-and-ai -g -a claude-code -y- Repository
- fcakyon/claude-codex-settings1.2k
- Category
- AI and agents
- Updated
- 3d ago
- License
- Apache-2.0
- Context cost
- Mediumabout 1,937 tokens
- Runs scripts
- No, instructions only
What it does
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.
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
- Understand before building - Validate the use case to ensure you recommend the right solution
- Always inspect first - Check existing indexes and schema before making recommendations
- Explain before executing - Describe what indexes will be created and require explicit approval
- Handle read-only scenarios - Without
create,update, ordeleteoperation tools you are in read-only mode; hand the user index JSON to create themselves - 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:
