Competition Pcap Protocol logo

Competition Pcap Protocol

CommunityPopular
zhaoxuya520
competition-pcap-protocol

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for packet capture analysis, session reconstruction, application-protocol decoding, stream reassembly, beacon timing, and packet-to-process correlation. Use when the user asks to analyze a PCAP, rebuild TCP or UDP sessions, decode HTTP, WebSocket, DNS, custom C2, or binary protocols, extract transferred artifacts, or tie packet sequences to host or malware behavior. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

Overview

Publisherzhaoxuya520
Repositoryreverse-skill
Skill namecompetition-pcap-protocol
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 Pcap Protocol 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-pcap-protocol .claude/skills/competition-pcap-protocol
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competition Pcap Protocol 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 Pcap Protocol 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 Pcap Protocol 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 PCAP Protocol

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 decisive evidence sits inside packet order, protocol framing, or stream reconstruction rather than a single IOC or host log.

Reply in Simplified Chinese unless the user explicitly requests English.

Quick Start

  1. Establish the capture boundaries first: hosts, time span, interfaces, missing packets, retransmits, and stream count.
  2. Group traffic into sessions before decoding payload semantics.
  3. Record protocol framing, sequence, timing, and transferred artifacts together instead of as isolated packets.
  4. Correlate packet evidence with host, malware, or app behavior only after the session is reconstructed.
  5. Reproduce the smallest decoded stream or transferred artifact that proves the challenge path.

Workflow

1. Build The Session Map

  • Identify endpoints, protocols, ports, TLS handshakes, DNS lookups, websocket upgrades, and long-lived streams.
  • Note missing capture coverage, asymmetric routing, packet loss, or reassembly issues before drawing conclusions.
  • Separate control channels, bulk transfers, keepalives, and noise.

2. Decode The Protocol Boundary

  • Reassemble TCP streams or UDP conversations before interpreting fields.
  • Recover framing, message order, custom headers, binary fields, compression, encryption boundaries, and object transfers.
  • Keep payload direction, timing, and session state aligned with each decoded message.

3. Tie Packets To Behavior

  • Show which packet sequence maps to which host event, malware branch, login flow, upload, exfiltration step, or command channel.
  • Distinguish protocol recognition from artifact recovery: naming HTTP, DNS, or a custom C2 is not enough without decoded content or proven downstream effect.
  • If the task becomes mostly a host timeline problem after decode, switch to the tighter forensic timeline skill.

Read This Reference

  • Load references/pcap-protocol.md for the session checklist, decode checklist, and evidence packaging.
  • If the hard part is a WebSocket or SSE handshake, subscription flow, realtime frames, or frame-driven state, prefer $competition-websocket-runtime.
  • If the hard part is a custom handshake, framing, checksum, sequence dependency, or deterministic replay harness, prefer $competition-custom-protocol-replay.

What To Preserve

  • Stream IDs, endpoint pairs, packet ranges, timestamps, protocol framing, and object boundaries
  • Decoded requests, responses, commands, transferred files, and the session that carried them
  • The exact packet sequence or reconstructed stream that proves the challenge behavior

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 Pcap Protocol AI skill do?

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for packet capture analysis, session reconstruction, application-protocol decoding, stream reassembly, beacon timing, and packet-to-process correlation. Use when the user asks to analyze a PCAP, rebuild TCP or UDP sessions, decode HTTP, WebSocket, DNS, custom C2, or binary protocols, extract transferred artifacts, or tie packet sequences to host or malware behavior. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

Why use Competition Pcap Protocol on TypingMind?

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

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

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 Pcap Protocol?

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

Is the Competition Pcap Protocol 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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