The Scenario
You are a solutions engineer. A new Salesforce integration goes live in three weeks. Before you can map Spoki's contact data model to Salesforce's schema, you need to know every custom field defined in Spoki — label, code, and data type.
You have never seen the full list. Nobody has written it down. The last integration was built by someone who left the company and the only documentation is a Slack message from eight months ago that references fields that may not even exist anymore.
The bad version:
- Log into Spoki
- Find the custom fields section
- Read each field: label, code, data type
- Copy them into a Google Sheet by hand
- Realize you are not sure if "telefono_secondario" is a text field or a phone field
- Go back and check
- Finish the list, then realize the integration mapping meeting is tomorrow and you built the wrong column order
- Go into the mapping session with a sheet that the developer immediately asks you to rebuild.
The fast version is one prompt.
The Easy Way: One Prompt in SheetXAI
SheetXAI reads every Spoki custom field and writes the complete data model into the sheet, ready for the mapping session.
Open the SheetXAI sidebar and type:
List all custom fields in Spoki and write field_id, label, code, and data_type into columns A through D of this Google Sheet — one row per custom field.
SheetXAI calls Spoki's custom fields endpoint and writes every definition into the sheet. You have the complete data model before the mapping meeting starts.
What You Get
A complete custom field directory in the sheet:
- Column A — field ID
- Column B — label (human-readable name)
- Column C — code (API field name)
- Column D — data type (text, phone, date, etc.)
- One row per field — no manual reading in the Spoki UI
The code column is what the developer actually needs for the integration. The label is what the UI shows. The code is what the API sends. Both in one row means the mapping session has the right information from the start.
What If the Data Is Not Quite Ready
A raw custom field export is the starting point. Integration mapping needs more structure on top.
When the developer wants to know which fields are required versus optional
Before mapping, the developer needs to know if skipping a field will cause a Spoki API error.
List all custom fields in Spoki and write field_id, label, code, data_type, and whether the field is required into columns A through E. Flag any required field with "REQUIRED" in column E.
When you want to add a column for the equivalent Salesforce field name
The mapping document needs a column for the Salesforce field that each Spoki field maps to.
List all Spoki custom fields and write field_id, label, code, and data_type into columns A through D. Add an empty column E labeled "Salesforce Field" for the team to fill in during the mapping session.
When the team wants fields grouped by data type
Text fields together, date fields together, phone fields together — easier to map in bulk.
List all Spoki custom fields and write field_id, label, code, and data_type into columns A through D. Sort by data_type so all fields of the same type appear together.
When you need the full field directory plus example values from existing contacts
Before finalizing the mapping, the engineer wants to see what real data looks like in each field to confirm the data type assumption.
List all custom fields in Spoki and write field_id, label, code, and data_type into columns A through D. Then fetch one example contact from Spoki that has data in the most custom fields and write the example value for each field into column E.
The pattern: pull the field definitions, add the mapping context the session needs, all in one prompt.
Try It
Get the 7-day free trial of SheetXAI and ask it to export your Spoki custom field schema into any open sheet. The Spoki integration is included in every plan. For related workflows, see how to export Spoki tags into a sheet or the Spoki in Google Sheets overview.