kwp-data-write-query

Translate natural language data requirements into dialect-specific SQL queries.

7|5|Updated May 7, 2026
One-click install
npx skills add https://github.com/14790897/MiQi --skill kwp-data-write-query
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: kwp-data-write-query
Source: https://github.com/14790897/MiQi/tree/main/miqi/skills/kwp/data/write-query
Command: npx skills add https://github.com/14790897/MiQi --skill kwp-data-write-query

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill eliminates the friction of writing complex, performant SQL queries by translating natural language requirements into dialect-specific, best-practice code.

Core Features & Use Cases

  • Dialect-Specific Syntax: Generates optimized code for Snowflake, BigQuery, PostgreSQL, and more.
  • Performance Optimization: Automatically structures queries using CTEs, efficient joins, and proper filtering to handle large datasets.
  • Use Case: Quickly generate a complex cohort retention analysis query for a Snowflake data warehouse without manually writing window functions or managing CTE syntax.

Quick Start

Use the write-query skill to generate a SQL query that calculates the top 10 users by order count over the last 30 days for a PostgreSQL database.

Frequently Asked Questions about kwp-data-write-query

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate optimized SQL queries from natural language for different database dialects?

Generating optimized SQL queries from natural language involves translating data requirements into dialect-specific syntax using CTEs and efficient joins. This skill automates that translation, structuring queries for platforms like Snowflake, BigQuery, and PostgreSQL.

What is the best way to write complex analytical SQL with multi-CTE joins and aggregations?

Writing complex analytical SQL with multi-CTE joins requires structuring queries with proper filtering and aggregations to handle large datasets. This skill automatically formats these complex structures into performance-tuned code.

Does this natural language to SQL tool support performance tuning for large-scale partitioned tables?

Yes, performance tuning for large-scale partitioned tables is supported. The skill structures queries using efficient joins and proper filtering mechanisms tailored for large datasets to ensure optimal performance across specific dialects.

Can I generate Snowflake or BigQuery specific syntax without manually writing window functions?

Yes, you can generate Snowflake or BigQuery specific syntax without manual window function coding. The skill translates natural language requirements directly into dialect-appropriate, best-practice code structures.

How do I inspect schema metadata before writing complex queries for a data warehouse?

Inspecting schema metadata before writing complex data warehouse queries requires integrating with data warehouse MCP servers. This skill uses those integrations to inspect schemas and provide accurate, dialect-appropriate syntax automatically.