chaos-experiment

name	chaos-experiment
description	Design and document chaos engineering experiments. Guide steady state baseline, hypothesis formation, failure injection plans, and results analysis. Use for resilience testing, game days, failure injection experiments, and building confidence in system stability.
license	MIT
metadata	{"version":"1.0.0","model":"claude-sonnet-4-5"}

Chaos Experiment Designer

Design rigorous chaos engineering experiments that build confidence in system resilience.

Triggers

• "chaos experiment"
• "test resilience"
• "failure injection"
• "resilience testing"
• "game day"
• "chaos engineering"

Quick Reference

Phase	Purpose	Output
1. Scope	Define system boundaries and objectives	System under test, success criteria
2. Baseline	Establish steady state metrics	Quantified normal behavior
3. Hypothesis	Form falsifiable hypothesis	Clear prediction statement
4. Injection	Design failure scenarios	Injection plan with blast radius
5. Execute	Run controlled experiment	Observation log
6. Analyze	Compare actual vs expected	Findings and action items

Core Principles

Chaos engineering is the discipline of experimenting on a system to build confidence in its capability to withstand turbulent conditions in production.

The Five Principles

1. Steady State Focus: Measure observable outputs (throughput, error rates, latency percentiles), not internal metrics
2. Real-World Variables: Introduce disruptions that simulate actual failure modes
3. Production Testing: Experiment on live systems with real traffic patterns
4. Continuous Automation: Build experiments into CI/CD pipelines
5. Blast Radius Containment: Minimize customer impact through careful scoping

Process

Phase 1: Scope Definition

Define the experiment boundaries.

Inputs: System architecture, historical incidents, monitoring data

Questions to Answer:

1. What system or subsystem will we test?
2. What is our business justification for this experiment?
3. Who are the stakeholders and who must approve?
4. What is the maximum acceptable customer impact?
5. What time window is safest for execution?

Output: Scoped experiment definition with stakeholder sign-off

Phase 2: Establish Baseline

Quantify normal system behavior.

Collect Steady State Metrics:

Metric Category	Examples	Collection Period
Throughput	Requests/second, transactions/minute	7-30 days
Error Rates	4xx rate, 5xx rate, exception count	7-30 days
Latency	P50, P95, P99 response times	7-30 days
Resource	CPU%, Memory%, Disk I/O, Network I/O	7-30 days
Business	Orders/hour, active sessions, conversion rate	7-30 days

Define Tolerance Thresholds:

• Green: Within normal variance (baseline +/- 1 standard deviation)
• Yellow: Elevated but acceptable (baseline +/- 2 standard deviations)
• Red: Unacceptable degradation (exceeds 2 standard deviations)

Output: Baseline document with metric values and thresholds

Phase 3: Form Hypothesis

Create a falsifiable hypothesis.

Hypothesis Template:

Given [system in steady state],
When [specific failure is injected],
Then [system behavior remains within tolerance]
Because [specific resilience mechanism exists].

Example Hypotheses:

• "Given our API gateway in steady state, when we terminate 50% of backend instances, then P99 latency remains under 500ms because auto-scaling will provision replacements within 60 seconds."
• "Given our payment service in steady state, when we introduce 500ms network latency to the database, then order completion rate remains above 99% because connection pooling and retry logic handle transient delays."

Hypothesis Quality Checklist:

• Specific failure mode identified
• Quantifiable success criteria defined
• Underlying resilience mechanism named
• Timeframe for expected recovery stated

Output: Documented hypothesis with measurable predictions

Phase 4: Design Injection Plan

Plan the controlled failure injection.

Common Failure Categories:

Category	Examples	Tools
Instance Failure	Kill process, terminate VM, evict pod	chaos-monkey, kill, kubectl delete
Network	Partition, latency, packet loss, DNS failure	tc, iptables, toxiproxy, chaos-mesh
Resource Exhaustion	CPU spike, memory pressure, disk fill	stress-ng, dd, memory hogs
Dependency	External service unavailable, slow response	fault injection proxy, mock services
Time	Clock skew, NTP failure	faketime, chrony manipulation
State	Data corruption, cache invalidation	Custom scripts

Injection Plan Elements:

1. Failure Type: Precise description of what will be broken
2. Injection Method: Tool and exact commands to use
3. Scope: Which instances/services/regions affected
4. Duration: How long the failure persists
5. Ramp-up: Gradual vs immediate injection
6. Rollback: How to instantly restore normal operation

Blast Radius Containment:

• Start with smallest possible scope (single instance)
• Use canary deployment pattern for experiments
• Define automatic abort criteria
• Have rollback ready before starting
• Notify on-call before and after

Output: Detailed injection plan with rollback procedures

Phase 5: Execute Experiment

Run the controlled experiment.

Pre-Execution Checklist:

• Stakeholders notified
• On-call team aware
• Monitoring dashboards ready
• Rollback procedure tested
• Customer support briefed (for production)
• Automatic abort criteria configured

During Execution:

1. Record experiment start timestamp
2. Monitor all baseline metrics in real-time
3. Log observations with timestamps
4. If abort criteria met, execute rollback immediately
5. Record experiment end timestamp

Observation Log Format:

[HH:MM:SS] - [Metric/Event]: [Value/Description]
[00:00:00] - Experiment started: Injected 500ms latency to database connection
[00:00:15] - P99 latency: 450ms -> 650ms
[00:00:30] - Circuit breaker: OPEN on database connection pool
[00:01:00] - Retry queue depth: 0 -> 247
[00:01:30] - Auto-recovery initiated
[00:02:00] - P99 latency: 650ms -> 480ms
[00:02:30] - Circuit breaker: CLOSED
[00:03:00] - Experiment ended: Removed latency injection

Output: Timestamped observation log

Phase 6: Analyze Results

Compare actual behavior against hypothesis.

Analysis Questions:

1. Did system behavior stay within tolerance thresholds?
2. Did resilience mechanisms activate as expected?
3. What was the actual recovery time?
4. Were there any unexpected cascading effects?
5. Did monitoring and alerting work correctly?

Verdict Options:

Verdict	Meaning	Action
VALIDATED	Hypothesis confirmed	Document and expand scope
INVALIDATED	Hypothesis falsified	File bugs, prioritize fixes
INCONCLUSIVE	Unable to determine	Refine experiment design

Finding Categories:

• Resilience Strengths: Mechanisms that worked as designed
• Weaknesses Discovered: Gaps in resilience that need fixing
• Monitoring Gaps: Missing visibility during incident
• Documentation Gaps: Runbooks or procedures that need updating
• Unexpected Behaviors: System responses not predicted

Output: Analysis document with prioritized action items

Scripts

Script	Purpose	Usage
generate_experiment.py	Create experiment document from inputs	python scripts/generate_experiment.py --name "API Gateway Resilience"
validate_experiment.py	Validate experiment document completeness	python scripts/validate_experiment.py path/to/experiment.md

Exit Codes

Code	Meaning
0	Success
1	General failure
2	Invalid arguments
10	Validation failure (missing required sections)

Output Directory

Experiments are saved to: .agents/chaos/

.agents/chaos/
  YYYY-MM-DD-experiment-name.md
  YYYY-MM-DD-experiment-name-results.md

Anti-Patterns

Avoid	Why	Instead
Testing in staging only	Production has different traffic patterns	Start small in production
No rollback plan	Cannot recover if things go wrong	Define rollback before starting
Vague hypothesis	Cannot determine success	Use quantifiable predictions
Measuring internal metrics only	Do not reflect customer experience	Focus on observable outputs
Big bang experiments	Blast radius too large	Start with smallest scope
No baseline	Cannot compare results	Collect 7+ days of metrics first
Skipping stakeholder buy-in	Creates political problems	Get approval before execution

Templates

Experiment Document Template

Use templates/experiment-template.md or generate with:

python scripts/generate_experiment.py \
  --name "Database Failover Resilience" \
  --system "Payment Service" \
  --owner "Jane Smith" \
  --output .agents/chaos/

Verification Checklist

Before executing any chaos experiment:

• Scope clearly defined with business justification
• Baseline metrics collected (minimum 7 days)
• Hypothesis is falsifiable with quantifiable criteria
• Injection plan includes specific tools and commands
• Blast radius is contained to acceptable scope
• Rollback procedure is documented and tested
• Stakeholders have approved the experiment
• On-call team is aware of timing
• Monitoring dashboards are ready
• Results template is prepared

Extension Points

1. Failure Categories: Add new failure types to Phase 4 table
2. Tools Integration: Extend scripts to integrate with chaos-mesh, Gremlin, LitmusChaos
3. Automation: Integrate with CI/CD for continuous chaos testing
4. Metrics Sources: Add integrations for Prometheus, Datadog, New Relic
5. Scheduling: Add calendar integration for recurring game days

Related Resources

• Principles of Chaos Engineering
• Chaos Monkey (Netflix)
• Chaos Mesh (CNCF)
• LitmusChaos (CNCF)
• Gremlin (Commercial)

Related Skills

Skill	Relationship
security	Security review for production experiments
devops	CI/CD integration for automated chaos
qa	Test strategy alignment
analyst	Root cause analysis of findings