Protocol Traffic Analysis logo

Protocol Traffic Analysis

Organization
zero-lab-ai
protocol-traffic-analysis

Analyzes supplied PCAP and packet-capture evidence into hosts, conversations, dependencies, authentication exposure, timing patterns, blind spots, and defensible follow-up. Use when reviewing network traffic without transmitting to the observed systems.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill nameprotocol-traffic-analysis
Stars
232
Forks
14
Bundled files
2
LicenseApache-2.0
Links
  • 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 zero-lab-ai on GitHub. Read the source before you install it.

Installation

Install the Protocol Traffic Analysis 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.
    https://github.com/zero-lab-ai/skills/tree/main/plugins/network-protocol-security/skills/protocol-traffic-analysis
  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/zero-lab-ai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/network-protocol-security/skills/protocol-traffic-analysis .claude/skills/protocol-traffic-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Protocol Traffic Analysis 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 Protocol Traffic Analysis 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 Protocol Traffic Analysis 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.

Protocol traffic analysis

A packet capture contains what reached one collection point during one time window under one filter. It does not contain every host, service, packet, or direction in the environment. Preserve that boundary while turning packets into an evidence-backed map.

This skill analyzes supplied captures. It does not authorize capture placement, active probing, credential cracking, replay, poisoning, exploitation, or target contact. Send active discovery to a reconnaissance workflow and active protocol validation to a separately authorized assessment.

When to Use

  • Reviewing network traffic without transmitting to the observed systems.
  • Use only within explicit authorization and the stated rules of engagement.

When NOT to Use

  • Do not treat this skill as permission to access, test, or change a target.
  • Do not convert missing evidence into a clean result or a stronger claim.

Workflow

  1. Hash each capture and record its source, authorization, collection point, time window, packet and byte counts, filter, snap length, directionality, clock basis, and reported packet drops.
  2. State handling rules before reading payloads. Keep credentials, tokens, personal data, and session material in restricted evidence storage. Put only redacted references in the analysis record.
  3. Build evidence locators from frame numbers, streams, fields, or deterministic aggregates. A narrative without a locator is not packet evidence.
  4. Inventory hosts from observed addresses and names. Record role and operating system as observed, inferred, or unknown, with evidence for each claim.
  5. Build directional conversations with endpoints, transport, time range, counts, service claim, authentication exposure, encryption state, and evidence. If the supplied facts do not include per-conversation counts or timestamps, mark those metrics unknown; never substitute 1 or the full capture window.
  6. Separate protocol identity from port convention. Mark a service observed only when dissection, payload, negotiation, or equivalent evidence supports it.
  7. Record dependencies, timing patterns, naming and broadcast intelligence, authentication exposure, and anomalies. Include alternative explanations for every inferred pattern.
  8. Cover capture integrity, host inventory, service dependencies, authentication, naming and broadcast, encrypted traffic, timing, blind spots, and data handling. Blocked or absent coverage remains visible.

Interpretation rules

  • No observed packet is not evidence that a host, service, or protocol is absent.
  • A filtered capture cannot answer questions about traffic excluded before storage.
  • Packet drops, truncation, one-sided capture, and clock uncertainty weaken timing and completeness claims; they do not disappear because analysis succeeded.
  • TTL, window size, user agent, naming, and protocol mix support hypotheses about host role or operating system. Preserve them as inferred unless direct evidence exists.
  • Encrypted traffic exposes metadata and behavior, not plaintext content.
  • Regular timing can indicate automation, monitoring, backup, or command and control. State the alternatives before recommending active follow-up.
  • A captured challenge-response proves exposure in the capture. It does not prove that the credential is crackable, relayable, privileged, or still valid.

Rationalizations to reject

  • "The capture is clean." A capture has coverage and limitations; it is not a verdict on traffic it could not see.
  • "Port 443 means HTTPS." Port convention is an inference until protocol evidence supports it.
  • "The periodic flow is command and control." Regular services and health checks produce the same shape. Preserve alternatives.
  • "We can paste the credential into the report for proof." Store the secret under access control and cite a redacted reference.
  • "Zero dropped packets means complete visibility." It says nothing about the capture point, filter, direction, or traffic outside the time window.
  • "The PCAP showed a relay target, so relay is authorized." Observation does not grant permission to crack, replay, poison, relay, or contact anything.

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 Protocol Traffic Analysis AI skill do?

Analyzes supplied PCAP and packet-capture evidence into hosts, conversations, dependencies, authentication exposure, timing patterns, blind spots, and defensible follow-up. Use when reviewing network traffic without transmitting to the observed systems.

Why use Protocol Traffic Analysis on TypingMind?

Because you install it once and use it with any model. Protocol Traffic Analysis 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 Protocol Traffic Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zero-lab-ai/skills/tree/main/plugins/network-protocol-security/skills/protocol-traffic-analysis. 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 Protocol Traffic Analysis?

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 Protocol Traffic Analysis?

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

Is the Protocol Traffic Analysis AI skill free?

Yes. It is published on GitHub by zero-lab-ai under the Apache-2.0 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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