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Competition Container Runtime

CommunityPopular
zhaoxuya520
competition-container-runtime

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for live container runtime analysis, mounted secrets, sidecars, namespaces, init containers, entrypoint drift, and route-to-container resolution. Use when the user asks why a live container differs from manifests, where a mounted secret is consumed, how a sidecar or init container changes runtime state, or which route resolves to which live container. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

Overview

Publisherzhaoxuya520
Repositoryreverse-skill
Skill namecompetition-container-runtime
Stars
36.3K
Forks
5K
Bundled files
2
LicenseMIT
Links
Skill sourceRepository
  • 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 Container Runtime 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-container-runtime .claude/skills/competition-container-runtime
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competition Container Runtime 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 Container Runtime 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 Container Runtime 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 Container Runtime

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 challenge is really about what the live container or pod is doing now, not what the checked-in manifest claims it should do.

Reply in Simplified Chinese unless the user explicitly requests English.

Quick Start

  1. Split intent from reality: manifest, image, startup, live mount, live route, live process.
  2. Map host -> proxy -> container or pod -> mounted volume -> consuming process.
  3. Keep secrets, rendered config, init output, and sidecar output separate from static manifests.
  4. Prove one minimal live path from mounted or injected state to reachable behavior.
  5. Reproduce the effect with the smallest runtime-specific chain.

Workflow

1. Map The Live Runtime

  • Compare compose or kube manifests against running containers, pods, mounted volumes, env, sidecars, init containers, and entrypoints.
  • Identify which process actually consumes the mounted secret, rendered config, or shared volume output.

2. Trace Route And Mount Boundaries

  • Map virtual host, reverse proxy, service, container port, filesystem mount, and runtime-generated file paths together.
  • Record whether the decisive state is image-baked, env-injected, mounted later, or written by an init/sidecar process.

3. Report The Runtime Deviation

  • State the earliest point where live runtime diverges from checked-in intent.
  • Keep one compact evidence chain from manifest or compose intent to live consumer behavior.

Read This Reference

  • Load references/container-runtime.md for the runtime checklist, mount-chain checklist, and common live-vs-static pitfalls.
  • If the hard part is kube API permissions, service-account trust, RBAC edges, admission mutations, or controller-created workload drift, prefer $competition-k8s-control-plane.
  • If the hard part is Host-header routing, path-prefix rewriting, or route-to-service mapping across nodes, prefer $competition-runtime-routing.
  • If the hard part is proving container-to-host crossover, kernel attack-surface preconditions, or stable escape primitives, prefer $competition-kernel-container-escape.
  • If the hard part is replaying Linux secrets, socket trust edges, or host-to-host pivots after container foothold, prefer $competition-linux-credential-pivot.

What To Preserve

  • Compose/Kubernetes fragments tied to live mounts or routes
  • Container IDs, pod names, mount paths, sidecar outputs, rendered config paths, and consuming processes
  • The exact route or file path that becomes reachable only at runtime

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 Container Runtime AI skill do?

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for live container runtime analysis, mounted secrets, sidecars, namespaces, init containers, entrypoint drift, and route-to-container resolution. Use when the user asks why a live container differs from manifests, where a mounted secret is consumed, how a sidecar or init container changes runtime state, or which route resolves to which live container. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

Why use Competition Container Runtime on TypingMind?

Because you install it once and use it with any model. Competition Container Runtime 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 Container Runtime in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhaoxuya520/reverse-skill/tree/main/CTF-Sandbox-Orchestrator/competition-container-runtime. 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 Container Runtime?

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 Container Runtime?

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

Is the Competition Container Runtime 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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