How to Use Chaos Engineer Skill in Claude Code: Beginner Guide with Examples

Feb 14, 2026

Cowrie

Dev @ Bswen

Purpose

This post demonstrates how to use the Chaos Engineer skill in Claude Code for DevOps development.

Environment

Claude Code (latest version)
claude-skills plugin
Basic DevOps knowledge

What is Chaos Engineer?

The Chaos Engineer skill in Claude Code helps you practice chaos engineering principles. It simulates failures and tests system resilience.

There are two main goals:

Failure injection: Test how your system responds to failures
Resilience validation: Verify your system can recover

I use this skill when I need to test system reliability or prepare for production incidents.

Installation and Setup

First, install the claude-skills plugin:

npm install -g @jeffallan/claude-skills

Then activate the Chaos Engineer skill in your Claude configuration:

{
  "skills": ["chaos-engineer"]
}

Verify the installation:

claude skill list

I can see the output:

Available skills:
- chaos-engineer: Inject failures and test resilience

Core Usage Patterns

The Chaos Engineer skill triggers when you ask about failure scenarios or resilience testing.

Common trigger phrases:

“Test what happens when the database fails”
“Simulate a service outage”
“Check if my system can handle network failures”
“Run a chaos engineering experiment”

Here’s how I invoke it:

Use chaos-engineer to test my API resilience

Practical Examples

Example 1: Database Failure Simulation

I wanted to test how my application handles database connection failures.

Use chaos-engineer to simulate database outage

The skill suggested:

// Simulate database failure
function simulateDBFailure() {
  // Randomly fail 30% of requests
  if (Math.random() < 0.3) {
    throw new Error('Database connection failed');
  }
  return { status: 'connected' };
}

When I ran this test, I found my application crashed instead of gracefully handling failures.

So the solution is to add error handling:

async function handleRequest() {
  try {
    const result = await database.query();
    return { success: true, data: result };
  } catch (error) {
    // Retry logic
    const retryResult = await retryDatabaseQuery();
    if (retryResult) {
      return { success: true, data: retryResult };
    }
    // Fallback to cache
    return { success: false, cached: getCachedData() };
  }
}

Now test again:

npm test chaos-db

You can see that I succeeded to make the system resilient to database failures.

Example 2: Service Degradation

I used chaos-engineer to test partial service failures:

Use chaos-engineer to simulate slow API responses

The skill recommended adding latency:

// Add artificial delay to simulate slow service
function simulateSlowAPI() {
  const delay = Math.random() * 5000; // 0-5 seconds
  return new Promise(resolve => setTimeout(resolve, delay));
}

When I tested this, I discovered my UI would freeze during slow requests.

Hence, an obvious way is to add timeouts:

async function fetchWithTimeout(url: string, timeout: number) {
  const controller = new AbortController();
  const timeoutId = setTimeout(() => controller.abort(), timeout);

  try {
    const response = await fetch(url, {
      signal: controller.signal
    });
    clearTimeout(timeoutId);
    return response;
  } catch (error) {
    if (error.name === 'AbortError') {
      throw new Error('Request timeout');
    }
    throw error;
  }
}

This prevents UI freezes and shows users a proper error message.

Example 3: Network Partition Testing

I tested what happens when microservices can’t communicate:

Use chaos-engineer to simulate network partition between services

The skill suggested:

// Simulate network failure
const isNetworkDown = () => Math.random() < 0.2;

async function callService(url: string) {
  if (isNetworkDown()) {
    throw new Error('Network unreachable');
  }
  return fetch(url);
}

I found that services would retry indefinitely, causing cascading failures.

So the solution is to implement circuit breakers:

class CircuitBreaker {
  private failureCount = 0;
  private lastFailureTime = 0;
  private state: 'CLOSED' | 'OPEN' | 'HALF_OPEN' = 'CLOSED';

  async execute(fn: Function) {
    if (this.state === 'OPEN') {
      if (Date.now() - this.lastFailureTime > 60000) {
        this.state = 'HALF_OPEN';
      } else {
        throw new Error('Circuit breaker is OPEN');
      }
    }

    try {
      const result = await fn();
      this.onSuccess();
      return result;
    } catch (error) {
      this.onFailure();
      throw error;
    }
  }

  private onSuccess() {
    this.failureCount = 0;
    if (this.state === 'HALF_OPEN') {
      this.state = 'CLOSED';
    }
  }

  private onFailure() {
    this.failureCount++;
    this.lastFailureTime = Date.now();
    if (this.failureCount >= 5) {
      this.state = 'OPEN';
    }
  }
}

Now the system fails fast when services are unreachable.

Best Practices

DO

Start with small failure probabilities (5-10%)
Test in non-production environments first
Monitor metrics during chaos experiments
Document failure scenarios and recovery procedures
Use chaos engineering proactively, not reactively

DON’T

Run chaos tests in production without preparation
Use high failure rates initially (start low, increase gradually)
Forget to clean up after tests
Test without monitoring and alerting
Assume your system is resilient without proof

Tips for Maximum Effectiveness

Gradual increase: Start with 5% failure rate, increase to 20-30%
Targeted testing: Test specific components, not entire systems at once
Automate experiments: Create reproducible chaos test suites
Measure impact: Track error rates, latency, and user impact
Learn from failures: Each chaos test should improve resilience

Chaos Engineer works well with:

security-review: Test security under failure conditions
tdd-workflow: Write tests for failure scenarios
planning: Design chaos experiments before implementation

Summary

In this post, I showed how to use the Chaos Engineer skill in Claude Code. The key point is that chaos engineering helps you build resilient systems by practicing failures before they happen in production.

I covered installation, basic usage patterns, and three practical examples: database failures, slow APIs, and network partitions. Each example showed how to inject failures and improve system resilience.

I think the key reason to use chaos-engineer is that testing resilience proactively prevents production outages. Instead of waiting for real failures, you can simulate them and fix weaknesses beforehand.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Claude Skills Documentation
👨‍💻 Claude Skills GitHub Repository

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!