Does it support any database besides Supabase?

This specific skill is configured to work with the Supabase 'cowork' project and the public.enquete_summary table.

Will it save data automatically?

No. A key rule of this skill is that it must always ask for user confirmation before executing any INSERT operations.

Does this skill save raw data directly?

No, it is designed to analyze and abstract raw data into structured insights, creating a concise summary rather than just copying raw text.

How does it handle duplicate survey names?

The skill performs an initial SQL check to retrieve existing survey names, allowing the AI to either append insights to an existing survey or create a unique entry.

Research Data & Survey Summarizer

Name: Research Data & Survey Summarizer
Author: iketomo

byiketomo

0•

Database Management

Analyzes and structures raw research data, such as surveys and interviews, into actionable insights stored directly in Supabase.

This skill streamlines the process of converting raw qualitative and quantitative research data into structured database records. By leveraging a specialized sub-agent architecture, it analyzes interview transcripts, form responses, and survey results to extract key insights, mapping them to the enquete_summary table in Supabase. It ensures data integrity by checking for existing entries and requiring explicit user confirmation before saving, making it an essential tool for UX researchers and product managers looking to build a searchable knowledge base of user feedback without manual data entry.

Key Features

01Granular data mapping using a '1 Insight = 1 Record' architecture

020 GitHub stars

03Context-efficient sub-agent processing for complex data analysis

04Strict safety protocols requiring user confirmation before any database write

05Native Supabase integration for direct database storage

06Automated analysis of raw survey and interview data into structured summaries

Use Cases

01Converting bulk Google Form responses into a structured insight repository

02Organizing disparate product feedback into a unified research summary table

03Extracting and saving key takeaways from user interview transcripts to a database

Key Features

01Granular data mapping using a '1 Insight = 1 Record' architecture

020 GitHub stars

03Context-efficient sub-agent processing for complex data analysis

04Strict safety protocols requiring user confirmation before any database write

05Native Supabase integration for direct database storage

06Automated analysis of raw survey and interview data into structured summaries

Use Cases

01Converting bulk Google Form responses into a structured insight repository

02Organizing disparate product feedback into a unified research summary table

03Extracting and saving key takeaways from user interview transcripts to a database