claude-skills/docs/skills/engineering/chaos-engineering.md

---
title: "Chaos Engineering — Agent Skill for Codex & OpenClaw"
description: "Use when planning, running, or learning from chaos engineering experiments. Triggers on 'chaos experiment', 'fault injection', 'gameday', 'resilience. Agent skill for Claude Code, Codex CLI, Gemini CLI, OpenClaw."
---

# Chaos Engineering

<div class="page-meta" markdown>
<span class="meta-badge">:material-rocket-launch: Engineering - POWERFUL</span>
<span class="meta-badge">:material-identifier: `chaos-engineering`</span>
<span class="meta-badge">:material-github: <a href="https://github.com/alirezarezvani/claude-skills/tree/main/engineering/skills/chaos-engineering/SKILL.md">Source</a></span>
</div>

<div class="install-banner" markdown>
<span class="install-label">Install:</span> <code>claude /plugin install engineering-advanced-skills</code>
</div>


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_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.

```bash
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.

```bash
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 baseline, you can't tell if X broke
- **No blast radius bound** — full-prod experiment without limits = outage
- **No abort criteria** — see above; this is mandatory
- **No on-call coverage** — chaos without monitoring is unmonitored production
- **Chaos in staging only** — staging never has prod failure modes
- **Chaos in dev** — useless; dev has different failure modes from prod
- **One-off chaos** — single experiment is a press release; learning requires recurrence
- **Blame-laden postmortem** — record causes, not blame; teams stop running chaos otherwise

## References

- `references/chaos_principles.md` — the 4 principles, history, when to start
- `references/experiment_design.md` — hypothesis structure, steady-state metrics, abort criteria
- `references/attack_taxonomy.md` — 7 attack types with examples and tooling
- `references/tooling_landscape.md` — Chaos Toolkit / Mesh / Litmus / Gremlin / FIS / DIY

## Slash command

`/chaos-experiment` — interactive experiment design wizard that runs all 3 tools.

## Asset templates

- `assets/experiment_template.md` — fill-in plan template
- `assets/postmortem_template.md` — structured postmortem template

## Verifiable success

A team using this skill should achieve:

- 100% of chaos experiments have a written hypothesis, abort criteria, and blast-radius calculation
- Blast radius for any single experiment never exceeds 10% of 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