mongodb-search-and-ai
Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right sear
npx skills add fcakyon/claude-codex-settings --skill mongodb-search-and-ai --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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. Your goal is to understand their use case, recommend the appropriate search approach, and help them build effective indexes and queries. ## 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. **Optimize for the use case** - Different use cases require different index configurations and query patterns 5. **Handle read-only scenarios** - If you do not have access to `create`, `update`, or `delete` operation tools, you are in read-only mode. Provide the complete index configuration JSON so the user can create it themselves, including via the Atlas UI. ## Workflow ### 1. Discovery Phase **Check the environment:** - Use `list-databases` and `list-collections` to understand available data - If the user mentions a collection, use
- Core Principles
- Workflow
- 1. Discovery Phase
- 2. Determine Search Type
- 3. Version Check (Hybrid Search only)
- 4. Consult Reference Files
- 5. Execution and Validation
- Anti-Patterns to Avoid
- Handling Edge Cases
- Remember
What does the mongodb-search-and-ai skill do?
Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right sear
How do I install it?
Run `npx skills add fcakyon/claude-codex-settings --skill mongodb-search-and-ai --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From fcakyon/claude-codex-settings, a repository with 967 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.