The Scenario
You are a platform engineer. A new backend engineer is joining on Monday. Today is Thursday.
Your Supabase project has forty-three tables across three schemas. The new engineer needs a data dictionary before day one — every table name, column name, data type, and nullable flag. Your team has never written this down, and the Supabase dashboard does not export it.
The slow version of Friday:
- You write a query against
information_schema.columnsin the Supabase SQL editor - You run it, copy the JSON output
- You open Excel, paste — the columns land as one blob of text
- You spend an hour parsing the JSON into columns manually
- The result has column names like
column_nameandis_nullable— not friendly for a junior engineer - You send it Saturday morning and still get a follow-up question about what
is_nullablemeans.
The fast version is one prompt.
The Easy Way: One Prompt in SheetXAI
SheetXAI reads your Supabase schema directly and writes the data dictionary into the workbook, so you do not have to write information_schema queries or parse JSON by hand.
Open the SheetXAI sidebar and type:
List all tables in my Supabase project abc123. For each table, write the table name, schema, column name, data type, and nullable flag into this workbook — one row per column. Use readable headers: Table, Schema, Column, Type, Required. Sort by table name then column name.
SheetXAI calls Supabase's schema API, walks every table and column, and writes the formatted data dictionary into the workbook.
What You Get
A complete data dictionary in the workbook:
- One row per column — not one row per table
- Readable headers — Table, Schema, Column, Type, Required
- Sorted — by table name, then column name within each table
- All forty-three tables — nothing skipped
The new engineer does not need database credentials to read an Excel workbook. She has the full schema structure before her first commit.
What If the Schema Needs More Context
Raw column lists are a starting point. SheetXAI can annotate and organise in the same prompt.
When table names use inconsistent naming conventions
Some tables use snake_case, some use camelCase.
List all tables in Supabase project abc123 and write the data dictionary into this workbook. Add a "Naming Issue" column and flag any table or column names that are not lowercase snake_case.
When you only want the public schema
The project has three schemas and you only need to document public.
List all tables in the public schema of Supabase project abc123 and write the column-level data dictionary into this workbook. Exclude the auth and storage schemas.
When you want foreign keys documented
The new engineer needs to understand table relationships, not just columns.
List all tables in Supabase project abc123 and write the data dictionary into this workbook. Add a Foreign Key column showing which column references which table, if any.
When you need schema documentation plus a table-level summary
Full column list plus a one-row-per-table summary with column counts and likely purpose.
List all tables in Supabase project abc123. Write the full column-level data dictionary into the Columns tab. In the Tables tab, write one row per table showing table name, schema, column count, and a one-sentence plain-English description of what the table likely stores based on its column names.
The pattern: ask for the documentation shape that the new engineer actually needs, not just the raw data dump.
Try It
Get the 7-day free trial of SheetXAI and open any Excel workbook, then ask it to document your Supabase schema. The Supabase integration is included in every SheetXAI plan. For related workflows, see how to run a cohort SQL query or the Supabase in Excel overview.