Can I use this skill to secure my workflows?

Yes, the skill includes patterns for RBAC configuration, ensuring your automation runs with the minimum required permissions to reduce the security attack surface.

What are Argo WorkflowTemplates?

WorkflowTemplates are standalone, reusable resources in Argo Workflows that allow you to define automation logic once and reference it across multiple triggers, schedules, or other workflows.

How do these patterns improve reliability?

They implement standardized error handling and retry strategies, such as exponential backoff, which allow workflows to recover gracefully from transient infrastructure or network failures.

How does this prevent YAML drift?

By extracting logic into versioned WorkflowTemplates rather than copying inline YAML, you ensure that updates and bug fixes are applied globally to all referencing resources.

Argo WorkflowTemplate Patterns

Name: Argo WorkflowTemplate Patterns
Author: adaptive-enforcement-lab

byadaptive-enforcement-lab

Deployment & DevOps

Streamlines Argo Workflow development by providing versioned, reusable building blocks for production-grade automation.

About

This skill provides standardized patterns and best practices for creating Argo WorkflowTemplates, the foundation of maintainable automation in Kubernetes. It helps developers move beyond fragile, inline YAML definitions to modular, tested components that support parameter contracts, exponential backoff retries, and secure RBAC configurations. By using these patterns, teams can eliminate configuration drift, improve pipeline reliability, and establish a clear contract for complex automation tasks.

Key Features

Pre-configured volume patterns for persistent storage and secrets
Multi-stage pipeline patterns using init containers and sequential setup
Advanced retry strategies including exponential backoff for resilience
Fine-grained RBAC configuration for secure workflow execution
Standardized parameter contracts for reusable automation logic
0 GitHub stars

Use Cases

Building resilient data processing pipelines with automated error recovery
Implementing secure automation with minimal-permission security profiles
Standardizing CI/CD build steps across multiple repositories and teams

About

Key Features

Pre-configured volume patterns for persistent storage and secrets
Multi-stage pipeline patterns using init containers and sequential setup
Advanced retry strategies including exponential backoff for resilience
Fine-grained RBAC configuration for secure workflow execution
Standardized parameter contracts for reusable automation logic
0 GitHub stars

Use Cases

Building resilient data processing pipelines with automated error recovery
Implementing secure automation with minimal-permission security profiles
Standardizing CI/CD build steps across multiple repositories and teams