chaos-engineer
Designs chaos experiments, creates failure injection frameworks, and facilitates game day exercises for distributed systems — producing runbooks, experiment manifests, rollback procedures, and post-mortem templates. Use when designing chaos experiments, implementing failure injection frameworks, or conducting game day exercises. Invoke for chaos experiments, resilience testing, blast radius control, game days, antifragile systems, fault injection, Chaos Monkey, Litmus Chaos.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/jeffallan/claude-skills --skill chaos-engineer- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads chaos-engineer automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
- System Analysis - Map architecture, dependencies, critical paths, and failure modes
- Experiment Design - Define hypothesis, steady state, blast radius, and safety controls
- Execute Chaos - Run controlled experiments with monitoring and quick rollback
- Learn & Improve - Document findings, implement fixes, enhance monitoring
- Automate - Integrate chaos testing into CI/CD for continuous resilience
Reference Guide
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| Experiments | references/experiment-design.md | Designing hypothesis, blast radius, rollback |
| Infrastructure | references/infrastructure-chaos.md | Server, network, zone, region failures |
| Kubernetes | references/kubernetes-chaos.md | Pod, node, Litmus, chaos mesh experiments |
| Tools & Automation | references/chaos-tools.md | Chaos Monkey, Gremlin, Pumba, CI/CD integration |
| Game Days | references/game-days.md | Planning, executing, learning from game days |
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:
- Experiment design document (hypothesis, metrics, blast radius)
- Implementation code (failure injection scripts/manifests)
- Monitoring setup and alert configuration
- Rollback procedures and safety controls
- 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 production
kubectl top pods -n production -l app=my-service
Step 2 — Create and apply a Litmus ChaosEngine manifest
# chaos-pod-delete.yaml
apiVersion: litmuschaos.io/v1alpha1
kind: ChaosEngine
metadata:
name: my-service-pod-delete
namespace: production
spec:
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 experiment
kubectl apply -f chaos-pod-delete.yaml
# Watch experiment status
kubectl describe chaosengine my-service-pod-delete -n production
kubectl get chaosresult my-service-pod-delete-pod-delete -n production -w
Step 3 — Monitor during the experiment
# Tail application logs for errors
kubectl logs -l app=my-service -n production --since=2m -f
# Check ChaosResult verdict when complete
kubectl 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 experiment
kubectl patch chaosengine my-service-pod-delete \
-n production --type merge -p '{"spec":{"engineState":"stop"}}'
# Confirm all pods are healthy
kubectl rollout status deployment/my-service -n production
Concrete Example: Network Latency with toxiproxy
# Install toxiproxy CLI
brew install toxiproxy # macOS; use the binary release on Linux
# Start toxiproxy server (runs alongside your service)
toxiproxy-server &
# Create a proxy for your downstream dependency
toxiproxy-cli create -l 0.0.0.0:22222 -u downstream-db:5432 db-proxy
# Inject 300ms latency with 10% jitter — blast radius: this proxy only
toxiproxy-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 behaviour
toxiproxy-cli toxic remove db-proxy -n latency_downstream
Concrete Example: Chaos Monkey (Spinnaker / standalone)
# chaos-monkey-config.yml — restrict to a single ASG
deployment:
enabled: true
regionIndependence: false
chaos:
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 testing
chaos-monkey --app my-service --account staging --dry-run false
Maintained by @jeffallan, Principal Consultant at Synergetic Solutions
FAQ
What does the chaos-engineer skill do?
Designs chaos experiments, creates failure injection frameworks, and facilitates game day exercises for distributed systems — producing runbooks, experiment manifests, rollback procedures, and post-mortem templates. Use when designing chaos experiments, implementing failure injection frameworks, or conducting game day exercises. Invoke for chaos experiments, resilience testing, blast radius control, game days, antifragile systems, fault injection, Chaos Monkey, Litmus Chaos.
How do I install chaos-engineer?
Run npx skills add https://github.com/jeffallan/claude-skills --skill chaos-engineer in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/chaos-engineer/ (personal) or .claude/skills/chaos-engineer/ (project).
Which AI agents can use chaos-engineer?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is chaos-engineer safe to install?
Security partners have reviewed it: Gen Agent Trust Hub flagged risks; Socket flagged items to review; Snyk passed; Runlayer flagged items to review; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains chaos-engineer?
It is published by jeffallan in jeffallan/claude-skills. It has 4,030 recorded installs.
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