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Competition K8s Control Plane

CommunityPopular
zhaoxuya520
competition-k8s-control-plane

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for Kubernetes API analysis, service-account trust, RBAC edges, admission and controller behavior, cluster secrets, workload mutation, and namespace-scoped drift. Use when the user asks to inspect kube API permissions, service-account tokens, RoleBinding or ClusterRoleBinding edges, admission webhooks, controller-created pods, secret exposure, or why live workloads differ from manifests. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

Overview

Publisherzhaoxuya520
Repositoryreverse-skill
Skill namecompetition-k8s-control-plane
Stars
36.3K
Forks
5K
Bundled files
2
LicenseMIT
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by zhaoxuya520 on GitHub. Read the source before you install it.

Installation

Install the Competition K8s Control Plane 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.
  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/zhaoxuya520/reverse-skill.git /tmp/reverse-skill
mkdir -p .claude/skills
cp -r /tmp/reverse-skill/CTF-Sandbox-Orchestrator/competition-k8s-control-plane .claude/skills/competition-k8s-control-plane
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competition K8s Control Plane 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 Competition K8s Control Plane 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 Competition K8s Control Plane 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.

Competition K8s Control Plane

Use this skill only as a downstream specialization after $ctf-sandbox-orchestrator is already active and has established sandbox assumptions, node ownership, and evidence priorities. If that has not happened yet, return to $ctf-sandbox-orchestrator first.

Use this skill when the decisive path runs through Kubernetes control-plane state, API permissions, or controller behavior rather than a single container's runtime alone.

Reply in Simplified Chinese unless the user explicitly requests English.

Quick Start

  1. Separate manifest intent from live cluster state: API objects, mutations, controllers, secrets, and resulting workloads.
  2. Identify the active principal first: service account, kubeconfig identity, node credential, webhook, or controller.
  3. Map the smallest control-plane edge to its workload effect.
  4. Keep RBAC, service accounts, owner references, namespace boundaries, and secret consumers in compact evidence blocks.
  5. Reproduce the smallest cluster action that yields the decisive workload or secret effect.

Workflow

1. Map The API Trust Path

  • Record namespaces, service accounts, Roles, ClusterRoles, bindings, admission hooks, controllers, and the resources they can mutate.
  • Distinguish read access, create access, patch access, exec access, and secret access.
  • Keep principal, verb, resource, namespace, and resulting object in one chain.

2. Trace Mutation To Workload State

  • Show how an API action becomes a pod, volume mount, secret exposure, env injection, job run, or controller-created artifact.
  • Compare checked-in YAML against live objects after defaulting, admission mutation, or controller reconciliation.
  • Distinguish pod-runtime behavior from cluster-level mutation logic.

3. Reduce To The Decisive Cluster Path

  • Compress the result to the smallest chain: principal -> API permission -> mutated object -> resulting workload, secret, or route effect.
  • Keep kube objects, live describes, and consumed secret or config paths tied to the same namespace and controller.
  • If the problem narrows down to one container's mount or runtime deviation, switch back to the tighter container-runtime skill.

Read This Reference

  • Load references/k8s-control-plane.md for the RBAC checklist, controller checklist, and evidence packaging.
  • If the hard part is metadata-service reachability, workload identity, instance credentials, or metadata-derived privilege, prefer $competition-cloud-metadata-path.

What To Preserve

  • Namespace, service account, verb, resource kind, RoleBinding or ClusterRoleBinding, and owner reference chains
  • Admission mutations, generated workloads, mounted secrets, and controller-produced drift
  • The exact API action or object diff that creates the decisive effect

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Competition K8s Control Plane AI skill do?

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for Kubernetes API analysis, service-account trust, RBAC edges, admission and controller behavior, cluster secrets, workload mutation, and namespace-scoped drift. Use when the user asks to inspect kube API permissions, service-account tokens, RoleBinding or ClusterRoleBinding edges, admission webhooks, controller-created pods, secret exposure, or why live workloads differ from manifests. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

Why use Competition K8s Control Plane on TypingMind?

Because you install it once and use it with any model. Competition K8s Control Plane 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 Competition K8s Control Plane in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhaoxuya520/reverse-skill/tree/main/CTF-Sandbox-Orchestrator/competition-k8s-control-plane. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Competition K8s Control Plane?

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 Competition K8s Control Plane?

As many as you like. As long as a model supports skills, you can use Competition K8s Control Plane with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Competition K8s Control Plane AI skill free?

Yes. It is published on GitHub by zhaoxuya520 under the MIT 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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