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dag-executor

Decompose natural language tasks into DAG workflows with parallel execution.

10|1|Updated Mar 17, 2026
Search Tags:#automation#workflow#orchestration#dag#parallel-execution#task-decomposition
One-click install
npx skills add https://github.com/curiositech/windags-skills --skill dag-executor-curiositech
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dag-executor
Source: https://github.com/curiositech/windags-skills/tree/main/skills/dag-executor
Command: npx skills add https://github.com/curiositech/windags-skills --skill dag-executor-curiositech

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Decomposes complex, manual tasks into structured, parallelizable DAG workflows and manages cross-agent orchestration to save time and reduce errors.

Core Features & Use Cases

  • Decomposition of tasks into executable graphs with dependency handling
  • Wave-based execution planning with lock coordination and conflict resolution
  • Result aggregation and context propagation across waves

Quick Start

Use the dag-executor skill to decompose a user request into a DAG and execute it with wave-based parallelism.

Frequently Asked Questions about dag-executor

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

FAQPage Schema
How do I decompose complex natural language tasks into parallel workflows?▼

DAG-based task decomposition translates natural language requests into executable dependency graphs, enabling wave-based parallel execution across multiple agents to automate complex multi-step workflows.

What is wave-based execution planning for DAG orchestration?▼

Wave-based execution planning organizes DAG subtasks into sequential execution waves, coordinating locks and resolving conflicts so parallel agents run safely and context propagates across each completed wave.

How do I handle dependency conflicts during parallel task automation?▼

Dependency conflicts during parallel task automation are managed through lock coordination and conflict resolution mechanisms within the DAG executor, ensuring subtasks execute safely without race conditions across agents.

Can I use natural language to trigger multi-step automation with the Task tool?▼

Yes, natural language requests are decomposed into structured DAG workflows and integrated with the Task tool for end-to-end automation, orchestrating subtasks across multiple agents without manual step-by-step scripting.

When should I use DAG workflows instead of sequential task execution?▼

DAG workflows are ideal for complex, multi-step automation tasks requiring dependency-aware planning and parallel execution, whereas sequential execution suits simple linear tasks without independent subtasks to parallelize.

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