Competition Firmware Layout logo

Competition Firmware Layout

CommunityPopular
zhaoxuya520
competition-firmware-layout

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for firmware images, partition tables, boot chains, update packages, extracted filesystems, embedded configs, and device-facing trust boundaries. Use when the user asks to unpack firmware, map partition layout, inspect bootloader or init chains, recover update keys or credentials, trace config loading, or explain how a device surface reaches the decisive artifact. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

Overview

Publisherzhaoxuya520
Repositoryreverse-skill
Skill namecompetition-firmware-layout
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 Firmware Layout 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-firmware-layout .claude/skills/competition-firmware-layout
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competition Firmware Layout 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 Firmware Layout 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 Firmware Layout 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 Firmware Layout

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 hard part is understanding how a firmware image is structured, booted, updated, and turned into reachable device behavior.

Reply in Simplified Chinese unless the user explicitly requests English.

Quick Start

  1. Preserve the original image, extracted partitions, unpacked filesystems, and patched copies as separate artifacts.
  2. Map outer container, partition table, bootloader, kernel, rootfs, config, and update metadata before editing anything.
  3. Track the boot or update chain in order instead of jumping straight to the most interesting file.
  4. Record keys, signatures, offsets, partition boundaries, and init entrypoints in one compact evidence chain.
  5. Reproduce the decisive secret, branch, or reachable service from the smallest extracted path.

Workflow

1. Establish Image Layout

  • Identify container type, partition headers, compression, filesystem type, and any appended or nested images.
  • Record offsets, sizes, hashes, mount points, and partition names before extraction mutates anything.
  • Separate bootloader, kernel, initramfs, rootfs, config blobs, and update metadata as different layers.

2. Trace Boot Or Update Flow

  • Map how control moves from bootloader to kernel to init to services, or from update package to verifier to installer.
  • Note which credentials, certificates, passwords, seeds, or config files are consumed at each stage.
  • Distinguish checked-in firmware intent from the live behavior the extracted files actually support.

3. Reduce To The Decisive Path

  • Show the smallest chain from image boundary to service exposure, auth bypass, debug interface, credential recovery, or flag artifact.
  • Keep extracted filesystems, derived configs, and patch experiments separate from pristine inputs.
  • If the challenge becomes mostly about native crash behavior or exploit primitives after extraction, switch back to the broader reverse skill.

Read This Reference

  • Load references/firmware-layout.md for the layout checklist, boot-chain checklist, and evidence packaging.

What To Preserve

  • Partition offsets, hashes, filesystem types, mount paths, boot entrypoints, and update metadata
  • Extracted secrets, config paths, init scripts, service units, and credentials tied to the stage that consumes them
  • Original images, extracted layers, mounted views, and patched copies as separate artifacts

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 Firmware Layout AI skill do?

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for firmware images, partition tables, boot chains, update packages, extracted filesystems, embedded configs, and device-facing trust boundaries. Use when the user asks to unpack firmware, map partition layout, inspect bootloader or init chains, recover update keys or credentials, trace config loading, or explain how a device surface reaches the decisive artifact. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

Why use Competition Firmware Layout on TypingMind?

Because you install it once and use it with any model. Competition Firmware Layout 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 Firmware Layout in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhaoxuya520/reverse-skill/tree/main/CTF-Sandbox-Orchestrator/competition-firmware-layout. 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 Firmware Layout?

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 Firmware Layout?

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

Is the Competition Firmware Layout 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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