agentsclimarketplace

Chaos runner

Skill a5c-ai/babysitter/library/specializations/software-architecture/skills/chaos-runner

Run chaos engineering experiments using Chaos Monkey, Litmus, or GremlinFrom its SKILL.md

Install
npx -y skills add a5c-ai/babysitter --skill chaos-runner

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

3.5 KB, 838 tokens by cl100k_base, as published. Nobody here has run it

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

{
  "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

{
  "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

{
  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
      }
    }
  }
}

What ships with it: 1 file

384 B alongside SKILL.md

Keep looking

Skills are one crate of 326,059. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.