Agent skill · marketing growth · aradotso
genpark-marketing-cloud-sql-audience-generator
Natural language to Marketing Cloud SQL converter for building targeted audience segments
Why this skill is useful
Adds a powerful natural language processing capability to generate complex SQL queries for Salesforce Marketing Cloud that marketers can use without SQL expertise.
What it needs
Requires git, pip installed locally. Requires genpark account access. About 5k tokens when loaded. Last updated 2026-08-05. 8 stars on the source repository.
What this skill does
GenPark Marketing Cloud SQL Audience Generator Skill by ara.so — Marketing Skills collection This project converts natural language audience intent into Marketing Cloud SQL queries, enabling marketers and developers to build targeted audience segments without deep SQL knowledge. It translates plain English descriptions of target audiences into valid Salesforce Marketing Cloud (SFMC) SQL queries for data extensions. Installation Core Functionality The skill generates SQL queries for Marketing Cloud data extensions based on natural language input. It understands common marketing segmentation criteria like demographics, behavior, engagement, and purchase history. Basic Usage Expected output: Configuration Set up environment variables: Configuration file example (config.yaml): Common Audience Patterns High-Value Customer Segment Re-engagement Campaign Geographic Targeting Advanced Usage Custom Data Extension Mapping Batch Processing Query Validation API Reference AudienceGenerator Class Key Methods generatesql(): Converts natural language to SQL explainquery(): Reverse operation - SQL to natural language validateschema(): Check data extension schema compatibility optimizequery(): Suggest query optimizations CLI Usage Example: Complete Workflow Troubleshooting API Authentication Errors Invalid SQL Generated Query Performance Issues Schema Mismatch Best Practices 1. Be specific: More detailed intents produce better SQL 2. Define schema: Provide data extension schema for accuracy 3. Validate queries: Always validate before executing in production 4. Use environment variables: Never hardcode API keys 5. Test with small datasets: Validate logic before full execution 6. Monitor performance: Track query execution times in Marketing Cloud Resources Homepage: https://genpark.ai Repository: https://github.com/alphaparkinc/genpark-marketing-cloud-sql-audience-generator-skill Marketing Cloud SQL Reference: Salesforce Marketing Cloud SQL documentation
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aradotso/genpark-marketing-cloud-sql-audience-generator