chaos-engineering

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---
name: chaos-engineering
description: Use when planning, running, or learning from chaos engineering experiments. Triggers on "chaos experiment", "fault injection", "gameday", "resilience test", "blast radius", "steady state", "abort criteria", "Chaos Toolkit", "Chaos Mesh", "Litmus", "Gremlin", "AWS FIS", or any deliberate failure-injection question. Ships experiment designer, blast-radius calculator, and postmortem generator (all stdlib Python), 4 references on chaos principles + experiment design + attack taxonomy + tooling landscape, and a /chaos-experiment slash command. Composes with feature-flags-architect (kill switches as abort triggers) and kubernetes-operator (common chaos targets).
context: fork
version: 2.9.0
author: claude-code-skills
license: MIT
tags: [chaos-engineering, resilience, fault-injection, gameday, sre, reliability, chaos-toolkit, chaos-mesh, litmus, gremlin, aws-fis]
compatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]
---

# 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

```bash
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).

```bash
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_calculat