chaos-engineering

Chaos Engineering

Design experiments that surface real weaknesses in production systems — without becoming outages. Most "chaos engineering" attempts skip steady-state measurement, define no abort criteria, and have no blast-radius bound. This skill enforces the discipline that makes chaos experiments safe and useful.

When to use

• Planning a chaos experiment (what to break, where, when, how to abort)
• Calculating blast radius before running the experiment
• Reviewing an existing experiment plan for safety
• Choosing a chaos tool (Chaos Toolkit / Chaos Mesh / Litmus / Gremlin / AWS FIS)
• Writing a chaos experiment postmortem
• Running a Game Day exercise

When NOT to use

• General incident response (use incident-response)
• Threat hunting / red-team (use red-team, threat-detection)
• Performance load testing (different goal — chaos is about failure modes, not capacity)
• Production debugging (chaos discovers weaknesses preemptively, not after-the-fact)

Core principle: chaos without abort criteria is an outage

The 4 Principles of Chaos Engineering (Netflix, 2016):

1. Build a hypothesis around steady-state behavior. Not "what breaks?" but "X holds; will it still hold under fault Y?"
2. Vary real-world events. Inject realistic failures: kill nodes, slow networks, lose cache, throttle dependencies.
3. Run experiments in production. Staging never has the same failure modes. Start small.
4. Automate experiments to run continuously. One-off chaos is a press release; continuous chaos is engineering.

Add a fifth: Define abort criteria up front. A chaos experiment with no abort criteria is an outage by another name.

Quick start

SKILL=engineering/chaos-engineering/skills/chaos-engineering

# 1. Design an experiment
python "$SKILL/scripts/experiment_designer.py" --target "checkout-svc" --hypothesis "p99 latency stays <500ms" --attack latency --duration-min 15

# 2. Calculate blast radius
python "$SKILL/scripts/blast_radius_calculator.py" --traffic-share 0.05 --user-pop 1000000 --duration-min 15

# 3. Generate postmortem after the experiment
python "$SKILL/scripts/experiment_postmortem.py" --plan experiment.json --result-log results.txt

The 3 Python tools

All stdlib-only. Run with --help.

experiment_designer.py

Generates a structured experiment plan from inputs. Enforces the required sections (hypothesis, steady-state metric, blast radius, abort criteria, rollback).

python scripts/experiment_designer.py \
  --target "checkout-svc" \
  --hypothesis "p99 latency stays <500ms when payment-svc is slow" \
  --attack latency \
  --magnitude "+200ms" \
  --duration-min 15 \
  --blast-radius "5% of US traffic" \
  --abort-if "p99 > 1000ms OR error_rate > baseline + 1pp"

Outputs a markdown plan with: hypothesis, steady-state, attack, magnitude, duration, blast radius, abort criteria, rollback procedure, monitoring dashboards, and learning question.

blast_radius_calculator.py

Computes the blast radius of a planned experiment. Given traffic share + user population + duration, calculates expected affected users, expected error budget burn, and a risk score.

python scripts/blast_radius_calculator.py \
  --traffic-share 0.05 \
  --user-pop 1000000 \
  --duration-min 15 \
  --baseline-availability 0.999 \
  --expected-impact-availability 0.95

Outputs:

• Expected affected users
• Error budget consumed (in minutes of error budget)
• Risk score: GREEN / YELLOW / RED
• Recommendation: PROCEED / REDUCE / ABORT

GREEN = <1% error budget; YELLOW = 1-10%; RED = >10%.

experiment_postmortem.py

Produces a structured postmortem from an experiment plan + results. Catches the common postmortem failure modes: no learning recorded, no follow-up actions, blame-laden language.

python scripts/experiment_postmortem.py --plan experiment.json --result-log results.txt

Outputs markdown with: summary, hypothesis (was it confirmed/refuted?), what we learned, what surprised us, follow-up actions with owners, and link to next experiment.

The 7 attack types (taxonomy)

Different attacks reveal different weaknesses. See references/attack_taxonomy.md for full detail.

| Attack | What it tests | Tooling | |---|---|---| | Latency | Timeouts, retries, circuit breakers | tc, Chaos Mesh NetworkChaos | | Error | Error handling, fallback paths | Chaos Mesh HTTPChaos, Toxiproxy | | Resource (CPU, memory, disk) | Saturation handling, autoscaling | Chaos Mesh StressChaos, stress-ng | | Network partition | Split-brain, consensus, failover | Chaos Mesh NetworkChaos partition | | Dependency failure | Graceful degradation, fallback | Service mesh fault injection | | Time | Clock skew, NTP issues | libfaketime, Chaos Mesh TimeChaos | | Infrastructure (kill instance) | Auto-recovery, failover | AWS FIS, Chaos Monkey |

Pick the attack that matches the hypothesis. "What happens if X is slow?" → latency. "What happens if X loses network?" → partition.

Tooling chooser

| Tool | Best for | Pricing | Stack | |---|---|---|---| | Chaos Toolkit | Lightweight, language-agnostic, JSON experiments | OSS | Any | | Chaos Mesh | Kubernetes-native, rich CRDs, in-cluster | OSS | Kubernetes | | Litmus | Kubernetes, Argo-integrated, large library | OSS + Enterprise | Kubernetes | | Gremlin | Enterprise SaaS, multi-cloud, audit | Paid | Any | | AWS FIS | AWS-native, IAM-integrated, EC2/ECS/EKS | Paid (AWS) | AWS | | Custom | Niche needs, single-cloud, low budget | None | Any |

Decision rules:

• k8s-only stack + OSS → Chaos Mesh or Litmus (Litmus has bigger experiment library)
• Multi-cloud + OSS → Chaos Toolkit
• AWS-heavy + simple needs → AWS FIS
• Enterprise + audit/compliance → Gremlin

See references/tooling_landscape.md for trade-offs.

Workflows

Workflow 1: Design and run a single experiment

1. State a hypothesis: "When [fault], steady-state metric X stays within Y."
2. Identify the steady-state metric — must be measurable BEFORE the experiment.
3. Run blast_radius_calculator.py — confirm GREEN before proceeding.
4. Run experiment_designer.py to produce the plan.
5. Get a peer review of the plan; confirm abort criteria are concrete.
6. Notify the on-call team in #incidents (or whatever channel).
7. Run the experiment with monitoring open.
8. If abort criteria are hit, abort immediately; record what happened.
9. Run experiment_postmortem.py to capture learnings.
10. File follow-up actions; link to next experiment.

Workflow 2: Game Day exercise

1. Pick a scenario (e.g., "primary database fails over").
2. Identify all dependent services that should keep working.
3. Build a multi-experiment plan covering each layer.
4. Schedule with stakeholders; on-call coverage required.
5. Run with a facilitator who manages the scenario.
6. Capture observations in a shared doc as they happen.
7. Single combined postmortem covering all observations.
8. Track follow-up actions in a board with owners.

Workflow 3: Continuous chaos (game days → daily)

1. Start: weekly Game Day in staging.
2. Move to: weekly Game Day in production with limited blast radius.
3. Mature to: continuous chaos via scheduled experiments (Litmus chaos schedule, Gremlin scenarios).
4. Wire to deployment: every prod deploy triggers a baseline chaos sweep.
5. Track: experiments per week, weaknesses discovered, MTTR trend.

Composition with other skills

This skill explicitly composes with two others in this library:

| Skill | Composition | |---|---| | feature-flags-architect | Kill switches defined there are the abort triggers here | | kubernetes-operator | Operators are common chaos targets (test reconcile under fault) | | incident-response | Chaos experiments that escalate become incidents |

Anti-patterns

• No hypothesis — "let's break things" is sabotage, not engineering
• No steady-state metric — without a