Does it monitor system response?

Yes, the skill assists in defining key metrics and monitoring system behavior to observe how recovery mechanisms like circuit breakers activate under stress.

Is this skill safe for production environments?

The skill follows best practices such as starting with small-scale experiments (blast radius control), but users should always execute chaos tests in staged environments first.

What tools does this skill support?

It integrates guidance for industry-standard tools including Chaos Mesh, Gremlin, Toxiproxy, and AWS Fault Injection Simulator (FIS).

Can it help with Kubernetes resilience?

Yes, it provides specific strategies for Kubernetes environments, including pod-level failure injection and network partitioning.

Chaos Engineering Toolkit

Name: Chaos Engineering Toolkit
Author: jeremylongshore

byjeremylongshore

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883

Seguridad y Pruebas

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

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This skill transforms Claude into a chaos engineering specialist capable of architecting and implementing 'GameDay' scenarios to harden system reliability. It guides users through the entire experiment lifecycle—from defining steady-state hypotheses and selecting appropriate tools like Chaos Mesh or AWS FIS to simulating network latency and analyzing recovery mechanisms like circuit breakers. It is an essential utility for DevOps and SRE teams looking to proactively identify weaknesses in cloud-native architectures and ensure high availability under stress.

Características Principales

883 GitHub stars
Validating recovery mechanisms like circuit breakers and retry logic
Designing failure injection scenarios for cloud and Kubernetes environments
Recommending platform-specific tools like AWS FIS and Gremlin
Simulating network latency and congestion using Toxiproxy
Analyzing system behavior under stress to identify architectural weaknesses

Casos de Uso

Stress-testing microservices by injecting CPU and memory exhaustion
Validating system resilience against regional cloud provider downtime
Testing automated database failover processes during simulated outages

Acerca de

Características Principales

883 GitHub stars
Validating recovery mechanisms like circuit breakers and retry logic
Designing failure injection scenarios for cloud and Kubernetes environments
Recommending platform-specific tools like AWS FIS and Gremlin
Simulating network latency and congestion using Toxiproxy
Analyzing system behavior under stress to identify architectural weaknesses

Casos de Uso

Stress-testing microservices by injecting CPU and memory exhaustion
Validating system resilience against regional cloud provider downtime
Testing automated database failover processes during simulated outages