happy-sim-add-instrumentation

Add probes, latency trackers, and throughput monitors to discrete-event simulations.

11|Updated Mar 16, 2024
One-click install
npx skills add https://github.com/adamfilli/happy-simulator --skill happy-sim-add-instrumentation
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: happy-sim-add-instrumentation
Source: https://github.com/adamfilli/happy-simulator/tree/main/.claude/skills/happy-sim-add-instrumentation
Command: npx skills add https://github.com/adamfilli/happy-simulator --skill happy-sim-add-instrumentation

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps you add detailed observability to your discrete-event simulations, allowing you to measure performance and understand system behavior.

Core Features & Use Cases

  • Add Probes: Collect data like queue depth over time.
  • Track Latency: Measure end-to-end event processing times.
  • Monitor Throughput: Calculate events processed per unit of time.
  • Visualize Data: Optionally generate interactive charts for live debugging or static plots.
  • Use Case: You have a simulation of a call center. Use this Skill to add probes to track the waiting time in each queue and the overall call completion rate.

Quick Start

Use the happy-sim-add-instrumentation skill to add latency tracking to the provided simulation script.

Frequently Asked Questions about happy-sim-add-instrumentation

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

FAQPage Schema
How do I add observability metrics to a discrete-event simulation?

You can add observability to a discrete-event simulation by inserting probes, latency trackers, and throughput monitors to collect metrics like queue depth and end-to-end latency for performance analysis.

What is the best way to track queue depth and end-to-end latency in a simulation?

The best way to track queue depth and end-to-end latency in a simulation is to add dedicated probes and latency trackers that collect performance metrics directly from the discrete-event environment.

Can I visualize simulation metrics like throughput using Python libraries?

Yes, you can visualize simulation metrics like throughput using Python libraries to generate either interactive charts for live debugging or static plots for performance analysis.

How do I measure event processing throughput in a discrete-event simulation?

You measure event processing throughput in a discrete-event simulation by adding throughput monitors that calculate the number of events processed per unit of time.

Does this instrumentation approach work with existing simulation scripts?

Yes, this instrumentation approach works with existing simulation scripts by adding probes and trackers directly to the code, requiring no dependencies to facilitate performance analysis.