Which chaos engineering tools does this skill support?

The skill provides specialized guidance for industry-standard tools including Chaos Mesh, Gremlin, Toxiproxy, and AWS Fault Injection Simulator (FIS).

Can I use this for Kubernetes environments?

Yes, the toolkit is optimized for Kubernetes, offering patterns for simulating pod failures, network partitions, and resource pressure using tools like Chaos Mesh.

Does it help with analyzing the results of an experiment?

Absolutely. Claude assists in monitoring key metrics during the experiment and provides a detailed analysis of system behavior to identify specific vulnerabilities.

What kind of failures can I simulate?

You can simulate a wide range of issues including API latency, database failovers, CPU/memory exhaustion, network packet loss, and service outages.

Chaos Engineering Toolkit

Name: Chaos Engineering Toolkit
Author: intent-solutions-io

byintent-solutions-io

Security & Testing

Designs and executes controlled failure experiments to identify system vulnerabilities and validate resilience mechanisms.

About

The Chaos Engineering Toolkit empowers developers and SREs to proactively test system robustness by simulating real-world failures such as network latency, resource exhaustion, and service outages. By guiding users through experiment design, tool selection, and result analysis, the skill helps teams validate recovery strategies like circuit breakers and retry logic before production issues occur. It acts as a specialist companion for conducting GameDays and ensuring high availability across Kubernetes, AWS, and complex distributed architectures.

Key Features

Automated design of failure injection scenarios and GameDay plans
Integration guidance for Chaos Mesh, Gremlin, Toxiproxy, and AWS FIS
Simulation of network latency, packet loss, and API congestion
Validation of circuit breakers, retry logic, and failover processes
Analysis of system behavior and post-experiment remediation advice
0 GitHub stars

Use Cases

Simulating pod termination in Kubernetes to test service discovery and autoscaling
Testing database failover automation under simulated infrastructure failure
Injecting API latency to verify frontend timeout handling and user experience

About

Key Features

Automated design of failure injection scenarios and GameDay plans
Integration guidance for Chaos Mesh, Gremlin, Toxiproxy, and AWS FIS
Simulation of network latency, packet loss, and API congestion
Validation of circuit breakers, retry logic, and failover processes
Analysis of system behavior and post-experiment remediation advice
0 GitHub stars

Use Cases

Simulating pod termination in Kubernetes to test service discovery and autoscaling
Testing database failover automation under simulated infrastructure failure
Injecting API latency to verify frontend timeout handling and user experience