Competition Custom Protocol Replay

CommunityPopular

zhaoxuya520

competition-custom-protocol-replay

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for custom binary or text protocol recovery, handshake reconstruction, framing, sequence control, checksums, stateful replay, and accepted-session reproduction. Use when the user asks to decode an unknown protocol, recover custom framing, build a replay harness, satisfy sequence or checksum rules, replay a captured session, or prove the smallest message order that reaches an accepted branch. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

Overview

Publisher	zhaoxuya520
Repository	`reverse-skill`
Skill name	`competition-custom-protocol-replay`
Stars	36.3K
Forks	5K
Bundled files	2
License	MIT
Links	Skill source Repository

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 Custom Protocol Replay AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/zhaoxuya520/reverse-skill/tree/main/CTF-Sandbox-Orchestrator/competition-custom-protocol-replay

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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-custom-protocol-replay .claude/skills/competition-custom-protocol-replay

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competition Custom Protocol Replay 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 Custom Protocol Replay 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 Custom Protocol Replay 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 Custom Protocol Replay

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 not merely naming the protocol, but reproducing the exact message order and state needed for acceptance.

Reply in Simplified Chinese unless the user explicitly requests English.

Quick Start

Identify client and server roles, session boundaries, and reset conditions before decoding field semantics.
Recover framing, lengths, delimiters, sequence numbers, checksums, nonces, and state transitions before broad replay attempts.
Keep one canonical transcript of a successful exchange.
Change one field or one message at a time while replaying.
Reproduce the smallest accepted conversation that proves the decisive branch.

Workflow

1. Map The Session State Machine

Identify handshake, negotiation, authentication, keepalive, command, and teardown phases.
Record which fields are static, which are derived, and which depend on prior messages.
Keep message order, direction, and timing tied to the same session identity.

2. Recover Framing And Integrity

Reconstruct lengths, delimiters, type bytes, checksums, MACs, counters, compression, or encryption boundaries.
Distinguish transport framing from application-level framing.
Note exactly where server acceptance changes when one field or step is mutated.

3. Build The Minimal Replay Harness

Reduce the path to the smallest transcript that reaches the accepted state, parser branch, command effect, or artifact.
Preserve both the original captured sequence and the replayed minimal sequence.
If the problem is mainly generic PCAP or stream decoding with no stateful replay requirement, switch back to the broader PCAP skill.

Read This Reference

Load references/custom-protocol-replay.md for the state-machine checklist, transcript checklist, and evidence packaging.

What To Preserve

Canonical transcript, message types, field boundaries, checksums, counters, and session identifiers
Original capture slices and the replay harness inputs that produce acceptance
The exact mutation that flips the protocol from rejected to accepted, or vice versa

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 Custom Protocol Replay AI skill do?

Why use Competition Custom Protocol Replay on TypingMind?

Because you install it once and use it with any model. Competition Custom Protocol Replay 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 Custom Protocol Replay in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zhaoxuya520/reverse-skill/tree/main/CTF-Sandbox-Orchestrator/competition-custom-protocol-replay. 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 Custom Protocol Replay?

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 Custom Protocol Replay?

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

Is the Competition Custom Protocol Replay 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Competition Custom Protocol Replay

Quick Start

Workflow

1. Map The Session State Machine

2. Recover Framing And Integrity

3. Build The Minimal Replay Harness

Read This Reference

What To Preserve

Bundled files

Frequently asked questions

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