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Agents42k · #4705

competition-agent-cloud

Helps your agent analyze security challenges in AI and cloud systems. It investigates how prompts, tools, and the cloud can be exploited.

What it does

This tool helps analyze security challenges in cloud and container systems. It focuses on how instructions (prompts) lead to actions, how data is used, and how software deployments function.

When to use it

  • ✓When you want to understand how a command given to the AI could be used to exploit a system.
  • ✓When you need to check if what's running on a server matches what it's supposed to be.
  • ✓When you want to trace the origin of software, from its code to its execution.

Example of what to ask your AI

“Analyze how this prompt could lead to a security flaw in the cloud system.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/zhaoxuya520/reverse-skill/blob/main/CTF-Sandbox-Orchestrator/competition-agent-cloud/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/zhaoxuya520/reverse-skill/blob/main/CTF-Sandbox-Orchestrator/competition-agent-cloud/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

It examines the sequence of steps: the initial instruction, how the AI interprets it, which tools it uses, and the final outcome in the system. This helps find flaws in how instructions are processed or how software is deployed.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add zhaoxuya520/reverse-skill --skill competition-agent-cloud

Or download just the file:

curl -L https://raw.githubusercontent.com/zhaoxuya520/reverse-skill/main/CTF-Sandbox-Orchestrator/competition-agent-cloud/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

Competition Agent Cloud

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 path is driven by prompt-to-tool execution, retrieval and memory boundaries, deployment drift, or build and release provenance.

Reply in Simplified Chinese unless the user explicitly requests English.

Quick Start

  1. Decide whether the dominant path is agentic or infrastructure-driven.
  2. Map one minimal control chain: untrusted input -> visible context -> tool or deployment side effect.
  3. Distinguish checked-in intent from live runtime truth.
  4. Keep prompts, tool args, manifests, mounts, and provenance steps in compact evidence blocks.
  5. Reproduce the exploit or misconfiguration with minimal context and minimal instrumentation.

Workflow

1. Agent And Prompt Injection

  • Treat prompts, tool schemas, retrieved chunks, planner notes, memory files, and handoffs as challenge artifacts.
  • Prove one minimal chain from untrusted content to model-visible instruction to tool side effect.
  • Distinguish claimed capability from runtime-exposed capability.

2. Cloud, Containers, And CI/CD

  • Split build-time, deploy-time, and runtime.
  • Reconcile compose or kube manifests with live mounts, env, logs, and traffic.
  • Trace provenance from source to dependency resolution to build to publish to runtime consumer.

Read This Reference

  • Load references/agent-cloud.md for the control-stack checklist, deployment-truth checklist, and evidence packaging.
  • If the task is specifically about prompt-boundary abuse or retrieved-content-to-tool drift, prefer $competition-prompt-injection.
  • If the task is specifically about CI, dependency provenance, registry drift, or shipped artifacts, prefer $competition-supply-chain.
  • If the task is specifically about queue payloads, async worker drift, retries, or worker-only runtime state, prefer $competition-queue-worker-drift.
  • If the task is specifically about SSRF to internal control surfaces, metadata endpoints, or metadata-derived token pivots, prefer $competition-ssrf-metadata-pivot.
  • If the task is specifically about proxy-upstream parse differentials, ambiguous headers, path normalization drift, or request smuggling behavior, prefer $competition-request-normalization-smuggling.
  • If the task is specifically about metadata-service access, instance or workload identity, link-local token paths, or metadata-derived privilege, prefer $competition-cloud-metadata-path.
  • If the task is specifically about kube API permissions, service-account trust, admission behavior, controller drift, or cluster secret exposure, prefer $competition-k8s-control-plane.
  • If the task is specifically about live mounts, sidecars, init containers, or runtime-only secret exposure, prefer $competition-container-runtime.
  • If the task is specifically about container-to-host boundary crossing, kernel-surface prerequisites, or escape primitive verification, prefer $competition-kernel-container-escape.

What To Preserve

  • Prompt snippets, retrieved chunks, planner transitions, and final tool args
  • Compose or Kubernetes fragments tied to live mounts or routes
  • Artifact hashes, dependency drift, CI steps, and the resulting runtime consumer
FAQ

What are 'sandbox assumptions'?

These are the security rules and boundaries defined for an isolated environment where tests are performed, ensuring actions don't affect the main system.

What is a 'prompt-to-tool flow'?

It's the path an instruction (prompt) takes from user input to the tool the AI uses to perform an action.

What does 'deployment drift' mean?

This occurs when what's actually running in a system (runtime) no longer matches the original configuration that should be there (manifest).

Details

Original description by the author

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for AI-agent, prompt-injection, MCP or toolchain, cloud, container, CI/CD, and supply-chain challenges. Use when the user asks to analyze prompt-to-tool flows, retrieval poisoning, mounted secrets, deployment drift, runtime-vs-manifest mismatches, registry provenance, or CI-produced artifacts under sandbox assumptions. Us

repo: zhaoxuya520/reverse-skill

Popularity

GitHub stars
42k
Ranking position
#4705
License
MIT
Updated
9/22/2026

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