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 query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or releva
npx skills add fcakyon/claude-codex-settings --skill mongodb-natural-language-querying --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 Natural Language Querying You are an expert MongoDB read-only query and aggregation pipeline generator. ## Query Generation Process ### 1. Gather Context Using MCP Tools **Required Information:** - Database name and collection name (use `mcp__mongodb__list-databases` and `mcp__mongodb__list-collections` if not provided) - User's natural language description of the query **Fetch in this order:** 1. **Indexes** (for query optimization): ``` mcp__mongodb__collection-indexes({ database, collection }) ``` 2. **Schema** (for field validation): ``` mcp__mongodb__collection-schema({ database, collection, sampleSize: 50 }) ``` - Returns flattened schema with field names and types - Includes nested document structures and array fields 3. **Sample documents** (for understanding data patterns): ``` mcp__mongodb__find({ database, collection, limit: 4 }) ``` - Shows actual data values and formats - Reveals common patterns (enums, ranges, etc.) ### 2. Analyze Context and Validate Fields Before generating a query, always validate field names against the schema you fetched. MongoDB won't error on nonexistent field names - it will simply return no results or behave unexpectedly, making bug
- Query Generation Process
- 1. Gather Context Using MCP Tools
- 2. Analyze Context and Validate Fields
- 3. Choose Query Type: Find vs Aggregation
- 4. Format Your Response
- Best Practices
- Query Quality
- Aggregation Pipeline Quality
- Error Prevention
- Schema Analysis
- Sample Document Usage
- Error Handling
- Example Workflow
- Managing Context Size
What does the mongodb-natural-language-querying skill do?
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 query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or releva
How do I install it?
Run `npx skills add fcakyon/claude-codex-settings --skill mongodb-natural-language-querying --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.