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Chaos

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nitinjain999
chaos

Design, run, and debug Chaos Engineering experiments on Kubernetes using Litmus Chaos v3 and Chaos Mesh v2. Covers fault injection (pod-delete, network-loss, CPU stress, node-drain), steady-state hypothesis probes, GameDay runbooks, scheduled experiments, DORA feedback loop, and RBAC setup. Use when asked to "inject a pod fault", "run a GameDay", "schedule chaos experiments", or "debug why my ChaosEngine is stuck".

Overview

Publishernitinjain999
Repositoryplatform-skills
Skill namechaos
Stars
71
Forks
14
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by nitinjain999 on GitHub. Read the source before you install it.

Installation

Install the Chaos AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/nitinjain999/platform-skills/tree/main/plugins/platform-skills/skills/chaos
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/nitinjain999/platform-skills.git /tmp/platform-skills
mkdir -p .claude/skills
cp -r /tmp/platform-skills/plugins/platform-skills/skills/chaos .claude/skills/chaos
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Chaos in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Chaos on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Chaos is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Design, run, and debug Chaos Engineering experiments on Kubernetes.


Interactive Wizard (fires when no arguments are provided)

When invoked with no arguments, ask before proceeding:

Q1 — Mode?

What do you need?
  1. install    — install Litmus Chaos or Chaos Mesh via Helm
  2. experiment — design a fault injection experiment
  3. schedule   — wrap an experiment in a recurring schedule
  4. gameday    — run a structured GameDay experiment
  5. debug      — diagnose a failed or stuck experiment
  6. report     — summarize results after an experiment completes

Enter 1–6 or mode name:

After collecting the mode, ask one follow-up:

  • install: Which tool? Litmus Chaos (recommended default) or Chaos Mesh (network/IO faults)?
  • experiment: Describe the workload to target — name, namespace, fault type (pod-delete / network-loss / cpu-stress / node-drain):
  • schedule: Provide the experiment YAML or describe the experiment to wrap in a schedule:
  • gameday: State the steady-state hypothesis — what metric or probe proves the system is healthy?
  • debug: Describe the symptom or paste the ChaosEngine/ChaosResult status:
  • report: Paste the ChaosResult output or describe the experiment that ran:

Then proceed into the relevant mode below.


Mode: install

Install Litmus Chaos or Chaos Mesh via Helm.

Steps:

  1. Choose the tool:

    • Litmus Chaos: CNCF graduated, ChaosCenter UI, broad experiment library → recommended default
    • Chaos Mesh: CNCF incubating, fine-grained NetworkChaos, IOChaos → choose for network partitions or I/O faults
  2. Install Litmus Chaos:

    bash
    helm repo add litmuschaos https://litmuschaos.github.io/litmus-helm/
    helm repo update
    helm upgrade --install chaos litmuschaos/litmus \
      --namespace litmus \
      --create-namespace \
      --version 3.9.0 \
      -f examples/chaos/litmus-install-values.yaml
  3. Install Chaos Mesh:

    bash
    helm repo add chaos-mesh https://charts.chaos-mesh.org
    helm repo update
    helm upgrade --install chaos-mesh chaos-mesh/chaos-mesh \
      --namespace chaos-mesh \
      --create-namespace \
      --version 2.7.0 \
      -f examples/chaos/chaos-mesh-install-values.yaml
  4. Verify:

    bash
    # Litmus
    kubectl get pods -n litmus
    # Chaos Mesh
    kubectl get pods -n chaos-mesh

    Expected: all pods Running

Mode: experiment

Generate a fault experiment from a description.

Steps:

  1. Identify: tool (Litmus/Mesh), fault class (pod/network/stress/node), target workload name and namespace
  2. Require steady-state hypothesis before generating the experiment:
    • HTTP probe: URL + expected response code
    • Prometheus probe: PromQL query that returns 1 (healthy) or 0 (degraded)
  3. Generate the experiment CRD with:
    • Namespace-scoped label selector
    • duration or TOTAL_CHAOS_DURATION set (default 60s)
    • Probe section referencing the steady-state hypothesis
    • PODS_AFFECTED_PERC: "50" for initial runs (safe); increase to 100% for full resilience test
  4. Output: experiment YAML + kubectl apply command + expected ChaosResult verdict

See examples/chaos/pod-delete-experiment.yaml and examples/chaos/network-loss-experiment.yaml for complete examples.

Mode: schedule

Wrap an experiment in a recurring schedule.

Steps:

  1. Litmus ChaosSchedule:

    yaml
    apiVersion: litmuschaos.io/v1alpha1
    kind: ChaosSchedule
    metadata:
      name: pod-delete-weekly
      namespace: my-namespace
    spec:
      schedule:
        repeat:
          properties:
            minChaosInterval: "168h"
      engineTemplateSpec:
        appinfo:
          appns: my-namespace
          applabel: "app=my-service"
          appkind: deployment
        chaosServiceAccount: litmus-admin
        experiments:
        - name: pod-delete
          spec:
            components:
              env:
              - name: TOTAL_CHAOS_DURATION
                value: "60"
  2. Chaos Mesh Schedule:

    yaml
    apiVersion: chaos-mesh.org/v1alpha1
    kind: Schedule
    metadata:
      name: network-loss-weekly
      namespace: my-namespace
    spec:
      schedule: "0 2 * * 1"
      type: NetworkChaos
      historyLimit: 5
      concurrencyPolicy: Forbid
      networkChaosTemplate:
        spec:
          action: loss
          mode: all
          selector:
            namespaces: [my-namespace]
            labelSelectors:
              app: my-service
          loss:
            loss: "20"
          duration: 60s
  3. Recommend staging only — never schedule experiments in production without a change window

Mode: gameday

Run a structured GameDay experiment.

Steps:

  1. Define steady-state hypothesis:

    • What metric or probe proves the system is healthy?
    • What is the acceptable degradation threshold?
  2. Scope the blast radius:

    • Namespace: <namespace>
    • Label selector: app=<service-name>
    • Max affected pods: 50% for first run
  3. Inject the fault (reference experiment from experiment mode output)

  4. Observe:

    • Watch probe results in ChaosResult
    • Check Prometheus/Grafana for error rate, latency, saturation
    • Note time-to-detect if an alert fired
  5. Record the verdict:

    • PASS: steady-state probe held throughout, service recovered within SLO
    • FAIL: probe failed, alert did not fire within expected window, or recovery exceeded SLO
  6. Output DORA impact summary:

    • Change failure rate delta: did this fault class cause failures in past deploys?
    • MTTR observed: time from fault injection to steady-state restoration
    • Recommendation: improve circuit breaker / HPA config / replica count / PDB

Mode: debug

If you need deeper context on troubleshooting, load references/chaos.md.

Diagnose a failed or stuck experiment.

Checklist:

  1. Is the controller running?

    bash
    kubectl get pods -n litmus          # Litmus
    kubectl get pods -n chaos-mesh      # Chaos Mesh
  2. Is the ChaosEngine/fault stuck?

    bash
    kubectl describe chaosengine <name> -n <namespace>   # Litmus
    kubectl describe podchaos <name> -n <namespace>       # Chaos Mesh
  3. Check the ChaosResult (Litmus only):

    bash
    kubectl get chaosresult -n <namespace>
    kubectl describe chaosresult <engine-name>-<experiment-name> -n <namespace>

    Look for status.experimentStatus.verdict and probe failure messages.

  4. Common causes:

    SymptomCauseFix
    Stuck InitializedMissing RBACkubectl get clusterrolebinding -l app.kubernetes.io/component=operator
    No pods targetedLabel selector wrongkubectl get pods -n <ns> -l <selector>
    Probe failingWrong URL or PromQLTest probe URL with curl from inside the namespace
    Experiment won't endMissing durationAdd duration: 60s (Chaos Mesh) or TOTAL_CHAOS_DURATION (Litmus)
    Controller crashloopingVersion mismatchRe-install matching chart version

Mode: report

Summarize experiment results after completion.

Steps:

  1. Collect ChaosResult:

    bash
    kubectl get chaosresult -n <namespace> -o yaml
  2. Report:

    • Fault injected: type, duration, affected pods
    • Steady-state probe: pass/fail timeline (from ChaosResult probe status)
    • Recovery time: time from fault injection (status.experimentStatus.passedRuns) to probe passing again
    • Verdict: PASS / FAIL with reason
  3. DORA impact:

    • Was error rate SLO breached? → contributes to change failure rate
    • Recovery time → MTTR observation
    • Feed into DORA benchmark mode: /platform-skills:dora benchmark

After completing this task, log errors and learnings via /platform-skills:self-improve log.

Frequently asked questions

What does the Chaos AI skill do?

Design, run, and debug Chaos Engineering experiments on Kubernetes using Litmus Chaos v3 and Chaos Mesh v2. Covers fault injection (pod-delete, network-loss, CPU stress, node-drain), steady-state hypothesis probes, GameDay runbooks, scheduled experiments, DORA feedback loop, and RBAC setup. Use when asked to "inject a pod fault", "run a GameDay", "schedule chaos experiments", or "debug why my ChaosEngine is stuck".

Why use Chaos on TypingMind?

Because you install it once and use it with any model. Chaos is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Chaos in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nitinjain999/platform-skills/tree/main/plugins/platform-skills/skills/chaos. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Chaos?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Chaos?

As many as you like. As long as a model supports skills, you can use Chaos with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Chaos AI skill free?

Yes. It is published on GitHub by nitinjain999 under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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