Schedule Triggers

schedule triggers initiate a playbook at a configured time they are ideal for automating repetitive tasks, improving productivity, and reducing human errors for example, you can use a schedule trigger to automatically generate reports, eliminating the need to manually retrieve data this guide will show you how to create and configure a schedule trigger in turbine scheduled playbooks (cron triggers) are evaluated in utc you do not need to select a trigger before adding and configuring actions or components in a playbook creating a schedule trigger to set a cron job for your playbook, follow these steps in a playbook, from the add panel, click and drag schedule to the canvas hover over the plus icon to add it to the canvas the trigger panel will display on the right side of the canvas, where you can configure your schedule trigger fill in the following fields based on the descriptions provided below example schedule configuration in this example, we configure a schedule trigger for a yearly cron job that runs at 12 00 am on january 1st each year once the trigger options display, select schedule the trigger panel displays with the trigger type set to schedule and the schedule type set to cron select the desired time schedule from the following options minutely , hourly , daily , weekly , monthly , yearly , or custom depending on the selected schedule, additional configuration fields will display when finished, click save to complete your schedule configuration congratulations! you have successfully created a schedule trigger detailed example with native actions automating weekly data collection and processing this example demonstrates how to use a schedule trigger combined with native actions (http, condition, loop, transform, and create variables) to automate data collection, processing, and reporting scenario you want to collect data from an external api every monday at 8 00 am, process it, and generate a report the report will either be stored or emailed the playbook ensures that only valid data is processed, and any errors will be logged for review steps step 1 configure the schedule trigger to run every monday at 8 00 am (utc) in the schedule trigger panel, select weekly and set the time to 8 00 am ensure the timezone is utc or adjust accordingly step 2 add an http action to make a get request to an external api that provides data for the report drag the http action into the playbook configure the http action to send a get request to the api endpoint (e g , https //api example com/data ) include any required headers (e g , authorization tokens) step 3 use the condition action to verify if the api returns valid data drag a condition action into the flow set the condition to check if the api response contains valid data (e g , response\["data"] is not null) step 4 if valid data is present, use a loop action to iterate through each record in the api response drag a loop action into the playbook configure the loop to iterate over the records in the api response (e g , response\["data"] ) step 5 within the loop, apply a transform action to format the data drag a transform action into the loop configure it to extract and format necessary fields such as date , amount , and category from each record step 6 use the create variables action to store the transformed data drag a create variables action into the flow after the transformation create variables for date , amount , category , etc step 7 depending on the requirement, send the report via email or store it in a database for email, add an email action and configure it to send the report for storage, use an http action or connector to store the report in a database step 8 if the condition in step 3 is not met, log the error and notify the team use the else branch of the condition action to log the error and send a notification example flow schedule trigger executes every monday at 8 00 am http action makes a get request to collect report data from an external api condition action checks if the response contains valid data; if not, logs an error loop action iterates over data records returned by the api transform action formats data fields (e g , "date," "amount," "category") create variables action stores the transformed data as variables for further processing final step store the report in a database or send it via email to stakeholders