dag-task-scheduler

Name: dag-task-scheduler
Availability: InStock
Author: Curiositech

Generates wave-based execution plans for DAG tasks with resource constraints.

10|1|Updated Mar 17, 2026

Search Tags:#orchestration #scheduling #throughput #dag #parallelism #scheduler #resource-allocation

Ready to run

npx skills add https://github.com/curiositech/windags-skills --skill dag-task-scheduler

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dag-task-scheduler
Source: https://github.com/curiositech/windags-skills/tree/main/skills/dag-task-scheduler
Command: npx skills add https://github.com/curiositech/windags-skills --skill dag-task-scheduler

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Scheduling and coordinating large DAGs with many tasks is complex and often limited by resource contention and dependency constraints. The DAG Task Scheduler provides wave-based execution plans that maximize concurrency while respecting CPU, memory, and timing constraints.

Core Features & Use Cases

Wave-based scheduling groups independent tasks into parallel waves to maximize throughput while preserving dependencies.
Adaptive scheduling supports runtime feedback, early completion, and dynamic rescheduling to maintain efficiency.
Resource-aware planning allocates budgets and enforces limits to prevent contention and bottlenecks in complex workflows.
Use Case: orchestrating a data processing pipeline where stages can run concurrently after dependencies are satisfied.

Quick Start

Provide a sample DAG and run the scheduler to generate a wave-based execution plan.

Frequently Asked Questions about dag-task-scheduler

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

FAQPage Schema

How do I schedule DAG tasks to maximize parallelism and throughput?▼

DAG task scheduling maximizes throughput by grouping independent tasks into parallel waves, ensuring concurrent execution while preserving dependency constraints and respecting resource limits. This wave-based approach prevents contention and optimizes workflow efficiency across the graph.

How does adaptive rescheduling handle dynamic runtime feedback in task orchestration?▼

Adaptive rescheduling uses dynamic runtime feedback, such as early task completion, to maintain orchestration efficiency. The scheduler adjusts subsequent wave execution dynamically, reallocating resources to prevent bottlenecks and adapt to real-time workflow changes.

Can I use wave-based scheduling for data processing pipelines with concurrent stages?▼

Wave-based scheduling supports data processing pipelines where stages run concurrently after dependencies are satisfied. It orchestrates parallel execution waves to maximize pipeline throughput while maintaining strict task ordering and resource boundaries.

What is the best way to coordinate large directed acyclic graphs with many dependencies?▼

Coordinating large directed acyclic graphs requires wave-based execution plans that maximize concurrency. By grouping independent tasks into parallel waves, the scheduler handles complex dependency constraints and resource limits, preventing contention across the workflow.

How does resource-aware planning enforce CPU and memory limits in DAG scheduling?▼

Resource-aware planning enforces CPU and memory limits during DAG scheduling to prevent resource contention and bottlenecks. By allocating budgets within constraints, it ensures complex workflows execute efficiently without exceeding available system capacity.