chaos-engineering

Files (10)

• SKILL.md
• experiment_template.md · assets/
• postmortem_template.md · assets/
• attack_taxonomy.md · references/
• chaos_principles.md · references/
• experiment_design.md · references/
• tooling_landscape.md · references/
• blast_radius_calculator.py · scripts/
• experiment_designer.py · scripts/
• experiment_postmortem.py · scripts/

SKILL.md (first lines)

# 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_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 |

From Okohedeki/dotclaude, MIT.

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