
MAPPING EXISTING FIELDS Copy Link
In Wiresk, Field Mapping is the process of connecting data fields from one app or step in your Flow to the expected fields in another step or destination app. It defines what data goes where.
Field Mapping happens when:
- You receive data from a Trigger (like Notion, a CRM, or an email)
- You pass that data into another Method (like Google Sheets, Slack, or Airtable)
- You match each source field to the correct target field
This ensures that the correct values are sent to the right places.
Example: Mapping Notion to Google Sheets Copy Link
If your Flow pulls pages from Notion and sends them to Google Sheets:
Notion Field | Mapped To Google Sheets Column |
---|---|
Page Title | Title |
Created Time | Date Added |
Status | Task Status |
You choose these mappings manually in Wiresk when setting up the Method.
Where Field Mapping Appears in Wiresk Copy Link
- In each Method configuration screen, Wiresk displays a list of required or optional fields for the app you are sending data to.
- You may see tooltips that describe what kind of data each target field expects.

Why Field Mapping Matters Copy Link
- Prevents data mismatches (e.g., sending text to a date field)
- Ensures your Flow runs correctly without errors
- Lets you transform or reformat values before inserting
- Makes your Flow readable and maintainable
Mapping methods Copy Link
In most cases, Wiresk will create the mapping for you based on common field names. However, you can personalize your field mapping as you see fit:
- Simply click on the field you want to map in your destination app.
- Choose your mapping method:
Flow Input Copy Link
Click on the input field, here you can select preloaded data from your Trigger or your Previous Method.
You can Map the “Input“ fields, by just following the branch of the metadata as in the table below, and select the last metadata name, for example: Data > customer > first_name (select “ first_name “if you want to map this metadata)
Example of field mapping table:
Metadata | Description |
Data > order_Number | Order number |
Data > processed_at | Processed order date and time |
Data > Customer > first_name | Customer first name |
Data > Customer > last_name | Customer last name |
Example: Mapping the email received date to a Spreadsheet.
- Click on “Input” field, a screen pops up, in the list of Input tab (dataset from the Trigger), select “Date” to Map the email received date in header 1.

Select a default value Copy Link
If the data is pre-defined, like a status or category, you can choose from a list of options displayed dynamically.
Manually Enter a Value Copy Link
Need to set a specific value for the field? You can enter it directly in the manual input field by clicking on the lightning bolt icon, or couple it with a flow input (preloaded data from previous steps) by typing “@”. In the list that appeared, just select the wanted data field to map.

MAPPING ARRAYS Copy Link
Explanation of “+ Element” and “+ Map” Buttons:
+ Element Copy Link
This button allows you to manually add a single element to an array.
When mapping data, arrays often contain multiple elements (e.g., multiple invoices, invoice lines, or items in an order). However, if you need to add just one specific element manually instead of mapping a dynamic array, you can use the “+Element” button to do so.
This is useful when:
- You need to insert a fixed value into an array.
- The array should always contain at least one predefined item.
- You are manually configuring an array without relying on dynamic data input.
+ Map Copy Link
This button is used when you have an array in the input data, and you need to generate one corresponding output element for each item in the array.
This allows Wiresk to dynamically iterate over the input array and create structured output data, regardless of whether the array contains one, ten, or a thousand elements.
When to use it:
- When dealing with complex data structures, such as groups of modifiers, options, or add-ons, where each group contains multiple attributes (e.g.,
name
,type
…). - It allows you to map a collection or group of related fields in a structured manner.
Example: mapping option prices from an array:
- Click on the button and select the
option_prices
array in the input data. - For every option price element in the input array, one corresponding output element is created.
- Each field (
external_key
,name
,price
,active
,default
) can be mapped manually or dynamically from the array.

MAPPING WITHIN A STRUCTURED DATASET Copy Link
You can manually Map a field with an Object key-value, with this method, you can specify the order of the Object in an Array from the current step or previous steps.
Mapping from the current step: Copy Link
Requires you to Map an array field first, refer to + Map.
1 – Input manually with the lightning bold icon (refer to the step above):
{{local.0.fieldName
}}
- The
local
keyword refer to the Mapped dataset within the current step. - The
0
represents the depth level of the locally mapped data structure. - The
fieldName
represents a specific field within the indexed array object.
Suppose you receive an object with the following structure:
firstname
lastname
invoices (array of invoices)
├── invoice_date
├── amount
├── invoice_lines (array of line items)
├── productid
├── name
If you map the invoices
array in a step, you’ll be working with an object that contains invoice_date
, amount
, and another array (invoice_lines
).
- To access
invoice_date
within the invoice mapping, you use:local.0.invoice_date
This means “invoice_date
from the first level of mapped array (invoices
)”. - Now, inside the mapping of invoice_lines, you’re at a deeper level (inside
invoice_lines
which is an array insideinvoices
).- To access
productid
insideinvoice_lines
, you use:local.1.productid
- Here,
local.1
refers to the second level of array mapping (invoice_lines
).
- To access
local.0
→ Refers to the first level of mapped data (e.g., fields inside invoices
).local.1
→ Refers to the second level of mapped data (e.g., fields inside invoice_lines
).The numbers in
local.X
represent the depth of the mapping in the hierarchy. 2 – You will need to set each mapped field Calculation type as a String Interpolation.
- Select the Ellipsis icon (…)
- Select String Interpolation

Mapping from previous steps: Copy Link
Enter manually via the Lightning bold icon:
{{context_data.stepx.x.fieldName
}} or {{context_data.stepx.fieldName
}}
where:
context_data
→ The top-level object containing workflow-related data.stepx
→ Represents a specific step in your Flow, where x is the placeholder for the step. 0 is the first step in your Flow, 1 is the second etc…- x (represent a number after stepx) indicates the numerical order within the array.
0
represent the first object,1
the second etc… fieldName
→ The specific field within the object that needs to be mapped.
Don’t forget to set each mapped field Calculation type as a String Interpolation.
Don’t stop here! Copy Link
For a complete tutorial on how to create a Flow, see “How to create a Flow in Wiresk”