chaos-engineering

Design controlled failure experiments to verify system resilience with blast radius limits.

1|Updated Mar 21, 2026

Search Tags:#kubernetes #chaos engineering #fault tolerance #resilience testing #failure injection #gameday #litmus

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npx skills add https://github.com/kalilurrahman/kr-claudiator-skills-original-prompts --skill chaos-engineering-kalilurrahman

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: chaos-engineering
Source: https://github.com/kalilurrahman/kr-claudiator-skills-original-prompts/tree/main/07-testing-quality/chaos-engineering
Command: npx skills add https://github.com/kalilurrahman/kr-claudiator-skills-original-prompts --skill chaos-engineering-kalilurrahman

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Teams often discover system weaknesses only during real outages. This Skill helps you proactively design controlled chaos experiments that inject failures (instance termination, network latency, resource exhaustion) to verify your system survives them before production incidents occur. ## Core Features & Use Cases - Hypothesis-Driven Experiments: Define steady-state metrics (latency, error rate) and testable hypotheses before injecting failures. - Multi-Tool Support: Includes Chaos Toolkit YAML experiments, Litmus Chaos for Kubernetes, Gremlin API attacks, and a Python failure injection library. - Safety Controls: Blast radius limits, automatic rollback on metric degradation, and GameDay runbooks for coordinated team exercises. - Use Case: Verify your application survives a database failover by terminating the primary instance in staging, monitoring p95 latency and error rates, and confirming automatic failover completes within 30 seconds. ## Quick Start Ask the AI to design a chaos experiment that terminates one application instance in staging and verifies p95 latency stays under 300ms.

Frequently Asked Questions about chaos-engineering

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

FAQPage Schema

How do I run a chaos engineering experiment in Kubernetes?▼

Install the Litmus Chaos operator with kubectl, then apply a ChaosEngine manifest targeting your deployment by label. Experiments like pod-delete or pod-network-latency run for a configured duration while HTTP probes continuously verify application health.

What is the difference between Chaos Toolkit and Litmus Chaos?▼

Chaos Toolkit is a general-purpose framework that runs YAML-defined experiments against any infrastructure including AWS. Litmus Chaos is Kubernetes-native, using custom resources and operators to inject failures into pods and containers.

Should chaos experiments run in production or staging?▼

Start in staging to validate experiment design, then run in production with a limited blast radius such as a single instance or 1% of traffic. Production testing is necessary because real traffic patterns, dependencies, and data volumes cannot be replicated in staging.

How do I prevent chaos experiments from causing outages?▼

Set blast radius limits restricting affected instances to a small percentage of total capacity, and implement automatic rollback that halts the experiment when error rates double or latency exceeds defined thresholds. Always test rollback procedures before running experiments.

What is a steady-state hypothesis in chaos engineering?▼

A steady-state hypothesis defines normal system behavior through measurable metrics like p95 latency under 200ms and error rate under 0.1%. Experiments verify the system maintains this state during failure injection rather than testing random destruction.