The Scenario
Your fund hired a data engineer last week. Their first project is building a Redshift pipeline from Affinity. They asked for a complete list of every custom field across company and person objects — field names, field types, and field IDs — so they can write the API calls correctly.
You are not the data engineer. You manage fund operations. You know the fields exist because you helped design them, but you have no idea what their IDs are, and you're not going to look them up manually across 40+ fields across multiple object types.
The bad version:
- Open the Affinity developer docs, find the fields endpoint, and start making API calls in Postman to retrieve field metadata — copying JSON responses into a sheet row by row.
- Realize the company fields and person fields are on separate endpoints and the list-specific fields are on a third endpoint, meaning you need three separate calls and three separate CSV-style dumps to merge.
- Spend 45 minutes reformatting the JSON output into a clean table the data engineer can actually use, only to discover you missed six fields that are specific to one list and not the global entity.
The data engineer is waiting. Their pipeline design is blocked.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. Through its Affinity integration, it can call the field metadata endpoints and write a clean reference table — field names, types, and IDs across company, person, and list objects — directly into your sheet. No JSON parsing, no endpoint juggling.
List all custom fields available for companies in my Affinity account and put the field names, types, and IDs in this sheet starting at row 2, with one field per row
What You Get
- One row per company custom field.
- Columns: field name in A, field type in B, field ID in C.
- All field types represented — dropdown, text, date, number, person, and others as they exist in your account.
- The field IDs in column C are the exact values your data engineer needs for API calls.
What If the Data Is Not Quite Ready
You need person fields as well as company fields
List all custom fields for both companies and persons in my Affinity account, one field per row, with columns for field name, type, ID, and object type (company or person) — write to this sheet starting at row 2
You need the field metadata for a specific list rather than global entities
Get the metadata for every field on my 'Deal Flow' Affinity list and build a reference table in this sheet with columns for field name, type, and ID — starting at row 2
You need to identify which fields are required vs. optional
List all custom fields for companies in my Affinity account. Add a column that marks whether each field is required. Write to this sheet starting at row 2.
Full kill-chain: pull all field metadata across objects, sort by type, flag required fields, write to sheet
Get all custom field metadata for companies, persons, and the 'Deal Flow' list in my Affinity account. Combine them into a single table with columns for object type, field name, field type, field ID, and required flag. Sort by object type then field name alphabetically. Write to this sheet starting at row 2.
One prompt, one table, everything your data engineer needs to start writing the pipeline.
Try It
Get the 7-day free trial of SheetXAI and open a blank sheet, then ask it to inventory your Affinity custom fields for whatever object you need to document first. For checking which Affinity lists your companies appear on, see Check Affinity List Membership for Companies in a Google Sheet or the Affinity integration overview.