Instrucciones de origen · Vista previa de solo lectura
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).
compatibility
opencode
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
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.
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.
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
