What is the primary goal of the Chaos Engineering & Resilience skill?

The goal is to proactively find system weaknesses by injecting controlled failures, ensuring the system can recover automatically without impacting users.

Does this skill provide safety mechanisms for production testing?

Yes, it includes automated rollback triggers, steady-state monitoring, and incremental blast radius progression to minimize unintended impact.

Which AI agents are involved in running these experiments?

The skill utilizes the qe-chaos-engineer for execution, qe-performance-tester for baselining, and qe-production-intelligence for real-time monitoring.

Can I use this with existing tools like Gremlin or Chaos Monkey?

Absolutely. This skill is designed to coordinate with industry-standard tools including Chaos Monkey, Gremlin, LitmusChaos, and network utility tools.

Chaos Engineering & Resilience

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

byproffesor-for-testing

•

보안 및 테스팅

Automates controlled failure injection and resilience testing to validate system recovery and fault tolerance.

This skill empowers Claude to orchestrate complex chaos engineering experiments, helping teams build more resilient distributed systems. By systematically injecting network, infrastructure, and application failures while monitoring steady-state metrics, the skill identifies hidden weaknesses before they lead to costly outages. It features integrated blast radius controls, automatic rollback triggers, and the ability to generate recovery runbooks, making it an essential tool for SREs and QA engineers practicing modern shift-right testing.

주요 기능

01Automated generation of disaster recovery runbooks based on findings

02Controlled failure injection across network, infrastructure, and application layers

03Safety-first blast radius management with automatic rollback triggers

0499 GitHub stars

05Automated steady-state monitoring and deviation detection

06Multi-agent coordination for experiment execution and production monitoring

사용 사례

01Validating microservices behavior during database or third-party API outages

02Testing disaster recovery and auto-scaling capabilities in cloud environments

03Measuring the impact of network latency or packet loss on user experience

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add proffesor-for-testing/agentic-qe chaos-engineering-resilience

For use in Claude.ai and ChatGPT

Download Skill