Connect workast and RunPod to Build Intelligent Automations
Choose a Trigger
When this happens...
New Task Created
Choose an Action
Automatically do this!
Create a Container Registry
Create a Template
Find a Container Registry Auth
Delete a Container Registry Auth
Delete a Template
Create Registry Authentication
Ready to use workast and RunPod automations
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- Send for Approval acceptCreate Entry of expensesAdd Step
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Actions and Triggers
When this happensTriggers
A trigger is an event that starts a workflow.
New Task Created
Triggers when a new task is created in your Workast account.
Do thisActions
Action is the task that follows automatically within your workast integrations.
Create a Task
create a task in workast app.
Create a Space
create a new space in workast.
Creates a Subtask
Creates a new subtask on a task.
Update Space
Update an existing space.
Create a Tag
Create a new tag
Add Tag
Add a tag to a task
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Frequently Asked Questions
To start, connect both your workast and RunPod accounts to viaSocket. Once connected, you can set up a workflow where an event in workast triggers actions in RunPod (or vice versa).
Absolutely. You can customize how workast data is recorded in RunPod. This includes choosing which data fields go into which fields of RunPod, setting up custom formats, and filtering out unwanted information.
The data sync between workast and RunPod typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between workast and RunPod. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
About workast
Workast is a productivity tool that helps teams capture tasks, manage projects and collaborate to get more work done.
Learn MoreAbout RunPod
RunPod is a platform that provides cloud-based computing resources for running and managing machine learning models, data processing tasks, and other computational workloads. It offers scalable and efficient solutions for developers and data scientists to deploy and execute their projects seamlessly.
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