Does it integrate with existing chaos engineering tools?

Yes, it provides guidance and implementation patterns for tools like Chaos Monkey, Gremlin, LitmusChaos, Toxiproxy, and WireMock.

Can Claude generate documentation from these experiments?

Yes, the skill automatically helps generate recovery runbooks and impact analysis reports based on the findings of each experiment.

What is the primary goal of the Chaos Engineering skill?

The goal is to proactively identify system weaknesses by injecting controlled disruptions to ensure the system can recover automatically without manual intervention.

How does it protect against accidental production outages?

The skill follows a strict blast radius progression (starting in Dev/Staging) and uses automated rollback triggers if error rates or latency exceed predefined thresholds.

Which types of failures can this skill simulate?

It can simulate network latency, packet loss, instance kills, disk/CPU pressure, application exceptions, and dependency service outages.

Chaos Engineering & Resilience

Name: Chaos Engineering & Resilience
Author: proffesor-for-testing

byproffesor-for-testing

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보안 및 테스팅

Implements controlled failure injection and resilience validation to ensure distributed systems handle real-world disruptions gracefully.

소개

This skill empowers Claude to perform sophisticated chaos engineering experiments, helping teams discover hidden system weaknesses before they impact end-users. It provides a structured methodology for defining steady-state metrics, automating failure injections—including network latency, instance termination, and dependency timeouts—and validating recovery procedures. By coordinating specialized agents like the qe-chaos-engineer and qe-performance-tester, this skill ensures high-confidence deployments through gradual blast radius expansion and automated safety rollbacks, ultimately hardening infrastructure against unpredictable failures.

주요 기능

Configurable safety triggers and automated rollback mechanisms
Automated generation of disaster recovery runbooks and experiment logs
Real-time steady-state monitoring with automated deviation detection
Automated failure injection across network, infrastructure, and application layers
Multi-agent coordination for end-to-end resilience validation
26 GitHub stars

사용 사례

Hardening high-availability infrastructure during pre-production verification
Testing system stability under simulated network partitions and high latency
Validating disaster recovery and automated failover in microservice architectures

소개

주요 기능

Configurable safety triggers and automated rollback mechanisms
Automated generation of disaster recovery runbooks and experiment logs
Real-time steady-state monitoring with automated deviation detection
Automated failure injection across network, infrastructure, and application layers
Multi-agent coordination for end-to-end resilience validation
26 GitHub stars

사용 사례

Hardening high-availability infrastructure during pre-production verification
Testing system stability under simulated network partitions and high latency
Validating disaster recovery and automated failover in microservice architectures