Individual skill
mongodb-search-and-ai
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.
plugins/mongodb-skills/skills/mongodb-search-and-ai/SKILL.md
Source commit 4f2b4bc9a74024397b4688cbafd1ae538a83a5fb
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- Observed behavior
- Network or broad shell
- Last checked
- Nov 4, 2026
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Repository context
- Repository stars (not skill ratings)
- 1,200
- Repository last updated
- Nov 6, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
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SKILL.mdcore · 8,900 bytesreferences/automated-embedding.mddoc · 16,231 bytesreferences/hybrid-search.mddoc · 23,667 bytesreferences/lexical-search-indexing.mddoc · 19,995 bytesreferences/lexical-search-querying.mddoc · 17,878 bytesreferences/quick-start.mddoc · 46,196 bytesreferences/vector-search.mddoc · 19,133 bytes
Read excerpt
# 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