Competition Stego Media logo

Competition Stego Media

CommunityPopular
zhaoxuya520
competition-stego-media

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for image, audio, video, document, and container steganography. Use when the user asks to inspect metadata, alpha or palette channels, LSBs, thumbnails, appended trailers, QR fragments, transcoding artifacts, or recover a hidden payload from media without blind brute force. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

Overview

Publisherzhaoxuya520
Repositoryreverse-skill
Skill namecompetition-stego-media
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 Stego Media 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-stego-media .claude/skills/competition-stego-media
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competition Stego Media 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 Stego Media 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 Stego Media 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 Stego Media

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 lives inside a media container, hidden channel, or appended payload rather than a conventional crypto blob.

Reply in Simplified Chinese unless the user explicitly requests English.

Quick Start

  1. Confirm the real container type, dimensions, duration, codec, and chunk layout before guessing a hidden layer.
  2. Check metadata, thumbnails, sidecar files, and appended trailers before deeper signal-domain work.
  3. Rank candidate channels by evidence: alpha, palette, LSB, transform-domain residue, frame order, or container slack.
  4. Preserve each extracted layer separately so the transform chain stays reproducible.
  5. Stop when the hidden payload is reproduced, not merely suspected.

Workflow

1. Establish Container Truth

  • Inspect headers, chunk tables, EXIF or document metadata, container indexes, thumbnails, and file size anomalies.
  • Compare declared format against observed structure to catch polyglots, appended archives, or malformed trailers.
  • Record exact offsets, frame numbers, or channel boundaries that look promising.

2. Inspect Candidate Channels

  • Check alpha, palette order, RGB or YUV planes, LSBs, spectrogram features, document object streams, or video frame deltas.
  • Prefer evidence-driven attempts over brute forcing every transform.
  • Note whether the payload is plain bytes, another media layer, compressed data, or an encrypted blob.

3. Reconstruct The Hidden Payload Path

  • Keep the chain in order: container -> channel or carrier -> extraction -> decompression or decode -> final parse.
  • Separate extraction success from final interpretation; a channel hit is not the same as artifact recovery.
  • If the problem becomes primarily about cryptography after extraction, hand off to the broader crypto skill.

Read This Reference

  • Load references/stego-media.md for the media checklist, channel ranking guide, and evidence packaging.

What To Preserve

  • File structure facts: offsets, chunks, frame numbers, stream names, metadata keys, and trailer size
  • Intermediate extractions and the exact command or transform used to produce them
  • The final recovered payload and the channel that produced it

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 Stego Media AI skill do?

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for image, audio, video, document, and container steganography. Use when the user asks to inspect metadata, alpha or palette channels, LSBs, thumbnails, appended trailers, QR fragments, transcoding artifacts, or recover a hidden payload from media without blind brute force. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

Why use Competition Stego Media on TypingMind?

Because you install it once and use it with any model. Competition Stego Media 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 Stego Media in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhaoxuya520/reverse-skill/tree/main/CTF-Sandbox-Orchestrator/competition-stego-media. 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 Stego Media?

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 Stego Media?

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

Is the Competition Stego Media 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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