workflows

Name: workflows
Availability: InStock
Author: TrueFoundry

Create and deploy TrueFoundry Workflows pipelines with Python tasks and cron scheduling.

13|Updated Feb 13, 2026

Search Tags:#orchestration #data processing #pipelines #workflows #flyte #ml training

Ready to run

npx skills add https://github.com/truefoundry/tfy-agent-skills --skill workflows-truefoundry

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: workflows
Source: https://github.com/truefoundry/tfy-agent-skills/tree/main/skills/workflows
Command: npx skills add https://github.com/truefoundry/tfy-agent-skills --skill workflows-truefoundry

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the creation, configuration, and deployment of complex data processing and ML training pipelines, abstracting away the intricacies of orchestration.

Core Features & Use Cases

DAG Definition: Define multi-step workflows as Directed Acyclic Graphs (DAGs) using Python decorators (@task, @workflow).
Task Orchestration: Manage dependencies and data flow between individual tasks, each running in its own container.
Scheduling: Automate recurring pipeline runs using cron-based scheduling.
Use Case: Orchestrate an end-to-end ML pipeline that fetches data, preprocesses it, trains a model, and evaluates its performance, all triggered on a daily schedule.

Quick Start

Use the workflows skill to deploy the attached Python file 'ml_pipeline.py' to your TrueFoundry workspace.

Frequently Asked Questions about workflows

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build and deploy ML training pipelines as DAGs in Python?▼

You build ML training pipelines by defining Directed Acyclic Graphs using Python decorators like @task and @workflow, then deploy them with the TrueFoundry SDK and CLI for containerized execution and cron scheduling.

How does task orchestration work for data processing pipelines?▼

Task orchestration manages dependencies and data flow between individual pipeline tasks, with each task running in its own isolated container to ensure reproducible data processing and ML training execution.

Can I schedule recurring data pipeline runs using cron?▼

Yes, you can automate recurring data processing and ML training pipeline runs using cron-based scheduling, enabling daily or periodic execution of your workflows without manual intervention.

Do I need the TrueFoundry SDK and CLI to define and deploy workflows?▼

Yes, the TrueFoundry SDK and CLI are required to define tasks and workflows in Python and deploy them to your TrueFoundry workspace for orchestrated execution.

What's the best way to orchestrate an end-to-end ML pipeline that trains a model daily?▼

Define a multi-step workflow that fetches data, preprocesses it, trains a model, and evaluates performance using Python decorators, then schedule it with cron for daily automated execution.

Does Flyte support containerized execution for data processing tasks?▼

Yes, workflows built on Flyte support containerized execution environments where each individual task runs in its own container, ensuring isolated and reproducible data processing.