workflow-dag

Define and validate DAG workflows with stages, transitions, and compensation handlers.

Updated Nov 17, 2025
One-click install
npx skills add https://github.com/Valynt/ValueOS --skill workflow-dag
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: workflow-dag
Source: https://github.com/Valynt/ValueOS/tree/main/.windsurf/skills/workflow-dag
Command: npx skills add https://github.com/Valynt/ValueOS --skill workflow-dag

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Defines and validates DAG-based workflows to orchestrate multi-stage, multi-agent processes with clear guardrails and compensation handlers.

Core Features & Use Cases

  • Define stages with timeout, retry configs, and compensation handlers to ensure idempotent rollbacks.
  • Build transitions to form a directed acyclic graph and verify structure at startup.
  • Register and validate workflows across services to guarantee consistent orchestration and lifecycle management.

Quick Start

Create a new workflow DAG constant and register it in the workflow registry, then implement compensation handlers for each stage and verify with tests.

Frequently Asked Questions about workflow-dag

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

FAQPage Schema
How do I orchestrate multi-stage workflows with guaranteed rollback handling?

You orchestrate multi-stage workflows by defining a DAG of stages and transitions, requiring a compensation_handler for each stage to ensure idempotent rollbacks. State is persisted after transitions to guarantee consistent lifecycle management.

What is a directed acyclic graph workflow and when do I need it for process orchestration?

A directed acyclic graph (DAG) workflow is a multi-stage orchestration structure that enforces no cycles, validating agent_type alignment with registered lifecycle stages. You need it for governance, approvals, and multi-agent processes requiring clear guardrails.

Can I use DAG workflows for multi-agent lifecycle management and governance processes?

Yes, you can use DAG workflows for lifecycle management and governance processes by registering and validating workflows across services. This guarantees consistent orchestration and validates agent_type alignment with registered lifecycle stages.

How do I add compensation handlers to workflow transitions for idempotent rollbacks?

You add compensation handlers by defining them for each stage in your workflow DAG constant, alongside timeout and retry configs. This ensures idempotent rollbacks are triggered appropriately when transitions fail or require reversal.

Does workflow DAG validation prevent cycles in multi-stage process orchestration?

Yes, workflow DAG validation enforces no cycles by verifying the directed acyclic graph structure at startup. This prevents infinite loops and ensures your multi-stage transitions proceed correctly through registered lifecycle stages.

What are the limitations of using DAG workflows for complex state transitions?

DAG workflows require a compensation_handler for every stage and enforce strict no-cycle validation, meaning you cannot implement circular or iterative looping processes. Agent_type alignment with registered lifecycle stages is also strictly mandated.