Chaos Engineering

chaos-engineering

Test system resilience through controlled failures. Use when validating fault tolerance, disaster recovery, or system reliability. Covers chaos experiments.

majiayu000/claude-skill-registry·0installs663starsDevOps & CI/CD

SKILL.md

Full skill instructions

Chaos Engineering

Principles

Build a Hypothesis: Define expected behavior
Minimize Blast Radius: Start small
Run in Production: Real conditions matter
Automate: Make experiments repeatable
Minimize Impact: Have abort conditions

Experiment Process

Steady State: Define normal metrics
Hypothesis: "System will maintain X under condition Y"
Introduce Variables: Inject failure
Observe: Compare to steady state
Analyze: Confirm or disprove hypothesis

Common Experiments

Network Failures

# Add latency
tc qdisc add dev eth0 root netem delay 100ms

# Packet loss
tc qdisc add dev eth0 root netem loss 10%

# Remove
tc qdisc del dev eth0 root

Resource Exhaustion

# CPU stress
stress --cpu 4 --timeout 60s

# Memory stress
stress --vm 2 --vm-bytes 1G --timeout 60s

# Disk fill
dd if=/dev/zero of=/tmp/fill bs=1M count=1024

Service Failures

Kill processes
Restart containers
Terminate instances
Block dependencies

Chaos Tools

Chaos Monkey: Random instance termination
Gremlin: Comprehensive chaos platform
Litmus: Kubernetes chaos engineering
Chaos Mesh: Cloud-native chaos

Experiment Template

## Experiment: [Name]

### Hypothesis
If [condition], then [expected behavior].

### Steady State
- Metric A: [baseline value]
- Metric B: [baseline value]

### Method
1. [Step 1]
2. [Step 2]
3. [Step 3]

### Abort Conditions
- If [condition], stop immediately

### Results
[What happened]

### Findings
[What we learned]

Safety Rules

Start in non-production
Have rollback ready
Monitor continuously
Communicate with team
Document everything

Install skill

Copy and run in your terminal

npx -y skills add https://github.com/majiayu000/claude-skill-registry --skill chaos-engineering

View on GitHub

skills/data/chaos-engineering/SKILL.md

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