mesh-job-orchestrator

Name: mesh-job-orchestrator
Availability: InStock
Author: 47Network

Orchestrate distributed compute jobs using MapReduce, Pipeline, ScatterGather, or LayerSplit strategies.

5|Updated Apr 15, 2026

Search Tags:#pipeline #orchestrate #job-management #map-reduce #distributed-compute #scatter-gather #layer-split

Ready to run

npx skills add https://github.com/47network/Sven --skill mesh-job-orchestrator

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mesh-job-orchestrator
Source: https://github.com/47network/Sven/tree/main/skills/compute-mesh/mesh-job-orchestrator
Command: npx skills add https://github.com/47network/Sven --skill mesh-job-orchestrator

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Creates and manages distributed compute jobs by decomposing them into work units using MapReduce, Pipeline, ScatterGather, or LayerSplit strategies, enabling scalable execution across multiple workers.

Core Features & Use Cases

Decomposes large jobs into manageable work units for parallel processing across a compute mesh.
Tracks progress, retries failed units, and aggregates results to provide end-to-end visibility.
Use cases include large-scale data processing, parallel workflows, and heterogeneous environments requiring dynamic scheduling.

Quick Start

Create a new distributed compute job with a chosen strategy and payloads to start orchestration and monitor progress.

Frequently Asked Questions about mesh-job-orchestrator

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

FAQPage Schema

How do I orchestrate distributed compute jobs across multiple workers?▼

You orchestrate distributed compute jobs by decomposing large tasks into manageable work units using strategies like MapReduce, Pipeline, ScatterGather, or LayerSplit. This enables scalable parallel execution, dynamic scheduling, and result aggregation across a compute mesh.

What is the best way to track progress and aggregate results for parallel workflows?▼

To track progress and aggregate results for parallel workflows, the system monitors work units dynamically, retries failed units, and aggregates outputs to provide end-to-end visibility. You can retrieve progress reports and statistics via a typed API.

When do I need to use MapReduce or ScatterGather strategies for job management?▼

You need MapReduce or ScatterGather strategies when processing large-scale data or executing complex workflows that require scalable, multi-worker execution. These decomposition strategies allow heterogeneous environments to dynamically schedule and process work units in parallel.

Can I cancel a distributed compute job after it has started?▼

Yes, you can cancel a distributed compute job after it has started. The typed API supports actions for cancelling active jobs, as well as creating, retrieving, listing, and reporting progress on work units to maintain full control over execution.

How do I handle failed work units in a multi-worker execution environment?▼

To handle failed work units in a multi-worker execution environment, the orchestration system automatically retries failed units. It tracks completion and failure states through the typed API, ensuring robust execution and accurate result aggregation for complex workflows.