chaos-engineering
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
Profile →npx skills add alirezarezvani/claude-skills --skill chaos-engineering --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- When to use
- When NOT to use
- Core principle: chaos without abort criteria is an outage
- Quick start
- The 3 Python tools
- experimentdesigner.py
- blastradiuscalculator.py
- experimentpostmortem.py
- The 7 attack types (taxonomy)
- Tooling chooser
- Workflows
- Workflow 1: Design and run a single experiment
- Workflow 2: Game Day exercise
- Workflow 3: Continuous chaos (game days → daily)
python "$SKILL/scripts/experiment_designer.py" --target "checkout-svc" --hypothesis "p99 latency stays <500ms" --attack latency --duration-min 15 python "$SKILL/scripts/blast_radius_calculator.py" --traffic-share 0.05 --user-pop 1000000 --duration-min 15 python "$SKILL/scripts/experiment_postmortem.py" --plan experiment.json --result-log results.txt python scripts/experiment_designer.py \ python scripts/blast_radius_calculator.py \ python scripts/experiment_postmortem.py --plan experiment.json --result-log results.txt
What does the chaos-engineering skill do?
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
How do I install it?
Run `npx skills add alirezarezvani/claude-skills --skill chaos-engineering --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From alirezarezvani/claude-skills, a repository with 23,791 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.