Chaos Engineer

When to Use This Skill

• Designing and executing chaos experiments
• Implementing failure injection frameworks (Chaos Monkey, Litmus, etc.)
• Planning and conducting game day exercises
• Building blast radius controls and safety mechanisms
• Setting up continuous chaos testing in CI/CD
• Improving system resilience based on experiment findings

Core Workflow

1. System Analysis - Map architecture, dependencies, critical paths, and failure modes
2. Experiment Design - Define hypothesis, steady state, blast radius, and safety controls
3. Execute Chaos - Run controlled experiments with monitoring and quick rollback
4. Learn & Improve - Document findings, implement fixes, enhance monitoring
5. Automate - Integrate chaos testing into CI/CD for continuous resilience

Reference Guide

Load detailed guidance based on context:

Safety Checklist

Non-obvious constraints that must be enforced on every experiment:

• Steady state first — define and verify baseline metrics before injecting any failure
• Blast radius cap — start with the smallest possible impact scope; expand only after validation
• Automated rollback ≤ 30 seconds — abort path must be scripted and tested before the experiment begins
• Single variable — change only one failure condition at a time until behaviour is well understood
• No production without safety nets — customer-facing environments require circuit breakers, feature flags, or canary isolation
• Close the loop — every experiment must produce a written learning summary and at least one tracked improvement

Output Templates

When implementing chaos engineering, provide:

1. Experiment design document (hypothesis, metrics, blast radius)
2. Implementation code (failure injection scripts/manifests)
3. Monitoring setup and alert configuration
4. Rollback procedures and safety controls
5. Learning summary and improvement recommendations

Concrete Example: Pod Failure Experiment (Litmus Chaos)

The following shows a complete experiment — from hypothesis to rollback — using Litmus Chaos on Kubernetes.

Step 1 — Define steady state and apply the experiment

# Verify baseline: p99 latency < 200ms, error rate < 0.1%kubectl get deploy my-service -n productionkubectl top pods -n production -l app=my-service

Step 2 — Create and apply a Litmus ChaosEngine manifest

# chaos-pod-delete.yamlapiVersion: litmuschaos.io/v1alpha1kind: ChaosEnginemetadata:  name: my-service-pod-delete  namespace: productionspec:  appinfo:    appns: production    applabel: "app=my-service"    appkind: deployment  # Limit blast radius: only 1 replica at a time  engineState: active  chaosServiceAccount: litmus-admin  experiments:    - name: pod-delete      spec:        components:          env:            - name: TOTAL_CHAOS_DURATION              value: "60"          # seconds            - name: CHAOS_INTERVAL              value: "20"          # delete one pod every 20s            - name: FORCE              value: "false"            - name: PODS_AFFECTED_PERC              value: "33"          # max 33% of replicas affected

# Apply the experimentkubectl apply -f chaos-pod-delete.yaml
# Watch experiment statuskubectl describe chaosengine my-service-pod-delete -n productionkubectl get chaosresult my-service-pod-delete-pod-delete -n production -w

Step 3 — Monitor during the experiment

# Tail application logs for errorskubectl logs -l app=my-service -n production --since=2m -f
# Check ChaosResult verdict when completekubectl get chaosresult my-service-pod-delete-pod-delete \  -n production -o jsonpath='{.status.experimentStatus.verdict}'

Step 4 — Rollback / abort if steady state is violated

# Immediately stop the experimentkubectl patch chaosengine my-service-pod-delete \  -n production --type merge -p '{"spec":{"engineState":"stop"}}'
# Confirm all pods are healthykubectl rollout status deployment/my-service -n production

Concrete Example: Network Latency with toxiproxy

# Install toxiproxy CLIbrew install toxiproxy   # macOS; use the binary release on Linux
# Start toxiproxy server (runs alongside your service)toxiproxy-server &
# Create a proxy for your downstream dependencytoxiproxy-cli create -l 0.0.0.0:22222 -u downstream-db:5432 db-proxy
# Inject 300ms latency with 10% jitter — blast radius: this proxy onlytoxiproxy-cli toxic add db-proxy -t latency -a latency=300 -a jitter=30
# Run your load test / observe metrics here ...
# Remove the toxic to restore normal behaviourtoxiproxy-cli toxic remove db-proxy -n latency_downstream

Concrete Example: Chaos Monkey (Spinnaker / standalone)

# chaos-monkey-config.yml — restrict to a single ASGdeployment:  enabled: true  regionIndependence: falsechaos:  enabled: true  meanTimeBetweenKillsInWorkDays: 2  minTimeBetweenKillsInWorkDays: 1  grouping: APP           # kill one instance per app, not per cluster  exceptions:    - account: production      region: us-east-1      detail: "*-canary"  # never kill canary instances
# Apply and trigger a manual kill for testingchaos-monkey --app my-service --account staging --dry-run false

Documentation