chaos-runner
Run chaos engineering experiments using Chaos Monkey, Litmus, or Gremlin
Best use case
chaos-runner is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Run chaos engineering experiments using Chaos Monkey, Litmus, or Gremlin
Teams using chaos-runner should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/chaos-runner/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How chaos-runner Compares
| Feature / Agent | chaos-runner | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Run chaos engineering experiments using Chaos Monkey, Litmus, or Gremlin
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Chaos Engineering Runner Skill
## Overview
Runs chaos engineering experiments using Chaos Monkey, Litmus, or Gremlin including failure injection scenarios, blast radius control, and experiment analysis.
## Capabilities
- Run Chaos Monkey experiments
- Litmus chaos execution
- Gremlin integration
- Failure injection scenarios
- Blast radius control
- Steady state validation
- Experiment rollback
- Results analysis
## Target Processes
- resilience-patterns
## Input Schema
```json
{
"type": "object",
"required": ["experiment"],
"properties": {
"experiment": {
"type": "object",
"properties": {
"name": { "type": "string" },
"type": {
"type": "string",
"enum": ["pod-kill", "network-latency", "cpu-stress", "memory-stress", "disk-fill", "node-drain"]
},
"target": {
"type": "object",
"properties": {
"namespace": { "type": "string" },
"labelSelector": { "type": "object" },
"percentage": { "type": "number" }
}
},
"duration": { "type": "string" }
}
},
"framework": {
"type": "string",
"enum": ["litmus", "gremlin", "chaos-monkey", "toxiproxy"],
"default": "litmus"
},
"steadyState": {
"type": "object",
"properties": {
"probes": { "type": "array" },
"assertions": { "type": "array" }
}
},
"options": {
"type": "object",
"properties": {
"dryRun": {
"type": "boolean",
"default": true
},
"autoRollback": {
"type": "boolean",
"default": true
},
"notifyOnFailure": {
"type": "boolean",
"default": true
}
}
}
}
}
```
## Output Schema
```json
{
"type": "object",
"properties": {
"experimentId": {
"type": "string"
},
"status": {
"type": "string",
"enum": ["passed", "failed", "aborted"]
},
"steadyStateValidation": {
"type": "object",
"properties": {
"before": { "type": "boolean" },
"during": { "type": "boolean" },
"after": { "type": "boolean" }
}
},
"metrics": {
"type": "object",
"properties": {
"affectedPods": { "type": "number" },
"recoveryTime": { "type": "string" },
"errorRate": { "type": "number" }
}
},
"findings": {
"type": "array"
},
"recommendations": {
"type": "array"
}
}
}
```
## Usage Example
```javascript
{
kind: 'skill',
skill: {
name: 'chaos-runner',
context: {
experiment: {
name: 'pod-failure-test',
type: 'pod-kill',
target: {
namespace: 'production',
labelSelector: { app: 'api-service' },
percentage: 50
},
duration: '5m'
},
framework: 'litmus',
steadyState: {
probes: [{ type: 'http', endpoint: '/health' }],
assertions: [{ metric: 'error_rate', operator: '<', value: 0.01 }]
},
options: {
dryRun: false,
autoRollback: true
}
}
}
}
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