calculate-parallelism

Calculate optimal subagent parallelism from system memory, CPU cores, and load.

1|Updated Feb 1, 2026

Search Tags:#performance #optimization #subagents #resource management #system monitoring #parallelism

Ready to run

npx skills add https://github.com/waynebrantley/aitools --skill calculate-parallelism

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: calculate-parallelism
Source: https://github.com/waynebrantley/aitools/tree/main/plugins/ai-tools/skills/calculate-parallelism
Command: npx skills add https://github.com/waynebrantley/aitools --skill calculate-parallelism

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill prevents system overload by intelligently determining the maximum number of parallel subagents that can run safely based on available system resources like memory and CPU.

Core Features & Use Cases

Resource Monitoring: Analyzes real-time system memory, CPU cores, and load average.
Dynamic Calculation: Computes an optimal parallelism limit, considering memory per subagent and system load.
Safety Caps: Enforces minimum (2) and maximum (6) parallelism to prevent underutilization or overload.
Use Case: When running a suite of tests in parallel, this skill ensures you don't launch too many processes, which could crash your system or slow down execution significantly.

Quick Start

Run the calculate-parallelism script to determine the optimal number of parallel subagents for your system.

Frequently Asked Questions about calculate-parallelism

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

FAQPage Schema

How do I calculate optimal parallelism for subagents without crashing my system?▼

This skill determines safe parallel task execution by monitoring available system memory, CPU cores, and current load average. It dynamically adjusts the number of concurrent processes to prevent resource exhaustion and ensure stable execution.

How does dynamic load-based reduction work for parallel task execution?▼

Dynamic load-based reduction works by evaluating current system load and available memory, then lowering the parallelism limit accordingly. It enforces memory reserves and applies reductions to prevent system overload during parallel task execution.

What are the minimum and maximum limits for spawning parallel subagents?▼

The minimum limit for spawning parallel subagents is 2, and the maximum is 6. These safety caps prevent both underutilization of system resources and system overload during parallel task execution.

Why does running tests in parallel slow down my system or cause memory exhaustion?▼

Running tests in parallel slows down your system because launching too many processes consumes available memory and CPU resources. Calculating an optimal parallelism limit based on current load prevents this resource exhaustion and stabilizes execution.

Can I configure memory reserves when calculating parallel subagent limits?▼

Yes, you can configure memory reserves when calculating parallel subagent limits. The skill enforces these predefined memory reserves and applies dynamic load-based reductions to ensure parallel task execution remains stable.