Chaos Engineering
End-to-end discipline for chaos experiments — design, run, learn — without becoming an outage.
What's inside
- 3 stdlib Python tools — experiment designer, blast-radius calculator, postmortem generator
- 4 reference docs — principles, experiment design, attack taxonomy, tooling landscape
- 2 templates — experiment plan, postmortem
/chaos-experimentslash command — interactive design wizard
Install
# Via Claude Code marketplace
/plugin install chaos-engineering
# Or clone the repo
git clone https://github.com/alirezarezvani/claude-skills.git
cd claude-skills/engineering/chaos-engineering
Quick start
SKILL=engineering/chaos-engineering/skills/chaos-engineering
# 1. Design an experiment
python "$SKILL/scripts/experiment_designer.py" \
--target checkout-svc \
--hypothesis "p99 < 500ms when payment slows" \
--attack latency --magnitude "+200ms" \
--abort-if "p99 > 1000ms OR error_rate > +1pp"
# 2. Calculate blast radius
python "$SKILL/scripts/blast_radius_calculator.py" \
--traffic-share 0.05 --user-pop 1000000 \
--duration-min 15
# 3. Generate postmortem after running
python "$SKILL/scripts/experiment_postmortem.py" \
--plan plan.json --result-log results.txt
Key principles
- Build a hypothesis around steady-state behavior — measurable, falsifiable
- Vary real-world events — realistic failures only, not astronomy-grade
- Run experiments in production — staging never has prod failure modes
- Automate experiments to run continuously — single experiment = press release; continuous = engineering
- Define abort criteria up front — chaos without abort = outage
The 7 attack types
| Attack | Tests | Magnitude examples |
|---|---|---|
| Latency | Timeouts, retries, circuit breakers | +200ms, +2s |
| Error | Error handling, fallback paths | 1%, 50%, 100% |
| Resource | Saturation, autoscaling, OOM | 80% CPU, 90% memory, fill /var |
| Network partition | Consensus, leader election, failover | drop 100% to peer X |
| Dependency failure | Graceful degradation | timeout 100% to dep X |
| Time skew | Clocks, TTLs, retry backoff | +5min, +1day |
| Infrastructure | Auto-recovery, replica maintenance | kill 1 of N |
Composition with other skills
| Skill | Composition |
|---|---|
feature-flags-architect |
Kill switches there are abort triggers here |
kubernetes-operator |
Operators are common chaos targets |
incident-response |
Chaos that escalates becomes an incident |
Skill structure
chaos-engineering/
├── README.md
├── .claude-plugin/plugin.json
└── skills/chaos-engineering/
├── SKILL.md
├── scripts/
│ ├── experiment_designer.py
│ ├── blast_radius_calculator.py
│ └── experiment_postmortem.py
├── references/
│ ├── chaos_principles.md
│ ├── experiment_design.md
│ ├── attack_taxonomy.md
│ └── tooling_landscape.md
└── assets/
├── experiment_template.md
└── postmortem_template.md
Verifiable success
A team using this skill should achieve:
- 100% of chaos experiments have written hypothesis, abort criteria, blast-radius calc
- Blast radius for any single experiment ≤10% of monthly error budget
- Mean time between chaos experiments <14 days (continuous, not one-off)
- Each experiment produces ≥1 follow-up action that gets shipped
- No chaos experiment escalates to a customer-impacting incident in trailing 90 days
License
MIT — see repo root LICENSE.