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pyats-parallel-ops

Coordinate parallel health checks, config audits, and state collection across testbed devices.

Updated Feb 28, 2026
Search Tags:#automation#mcp#parallel#network-testing#fleet#openclaw#pyats
One-click install
npx skills add https://github.com/dgethings/netclaw --skill pyats-parallel-ops-dgethings
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pyats-parallel-ops
Source: https://github.com/dgethings/netclaw/tree/main/workspace/skills/pyats-parallel-ops
Command: npx skills add https://github.com/dgethings/netclaw --skill pyats-parallel-ops-dgethings

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Fleet-wide device operations are often slow and monolithic; pyats-parallel-ops solves this by coordinating parallel health checks, audits, and state collection across many devices.

Core Features & Use Cases

  • Parallel execution across devices to speed data collection and reduce overall testbed time.
  • Fleet-wide health checks and config audits with aggregated results and unified reporting.
  • Severity-sorted reporting and failure isolation to prevent cascading outages and quickly surface critical issues.

Quick Start

Run a parallel fleet operation on your testbed to perform a full health check across all devices.

Frequently Asked Questions about pyats-parallel-ops

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

FAQPage Schema
How do I run parallel health checks across multiple network devices?▼

Parallel health checks across network devices are executed by coordinating fleet-wide operations to test devices simultaneously, aggregating state collection and config audit results for unified reporting. This reduces overall testbed time and isolates failures quickly.

What is the best way to perform fleet-wide config audits without causing cascading outages?▼

Fleet-wide config audits prevent cascading outages by executing parallel operations across devices and using severity-sorted reporting to surface critical issues. This approach isolates failures quickly while aggregating results for cross-device comparisons.

Do I need a specific environment to run parallel pyats network testing operations?▼

Parallel pyats network testing requires Python 3, an OpenClaw environment, and a configured testbed specified via the PYATS_TESTBED_PATH variable to run parallel MCP calls across your device fleet.

Can I collect state data from large testbeds in parallel to speed up validation?▼

State collection across large testbeds is accelerated by coordinating parallel execution across devices. This enables bulk actions and fleet-wide validation, allowing you to gather state data and perform cross-device comparisons efficiently.

Why does fleet-wide device testing take so long to execute sequentially?▼

Sequential fleet-wide device testing is slow because operations run monolithically. Coordinating parallel execution across devices solves this by performing health checks and state collection simultaneously, drastically reducing overall testbed time.

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