Wireless Assessment Plan logo

Wireless Assessment Plan

Organization
zero-lab-ai
wireless-assessment-plan

Plans authorized Wi-Fi, Bluetooth, RFID/NFC, SDR, and other radio assessments with explicit physical scope, transmission authority, disruption budgets, capture handling, coverage, and handoffs. Use before conducting or reviewing a wireless penetration test.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill namewireless-assessment-plan
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 Wireless Assessment Plan 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/wireless-assessment-plan
  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/wireless-assessment-plan .claude/skills/wireless-assessment-plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wireless Assessment Plan 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 Wireless Assessment Plan 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 Wireless Assessment Plan 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.

Wireless assessment plan

Radio signals cross walls, property lines, and engagement boundaries. Receiving a signal can be passive while retaining its content, targeting its source, or transmitting a response creates separate scope, privacy, safety, and regulatory questions. Model those decisions before activating test equipment.

When to Use

  • Plan wireless assessment for authorized security work.
  • 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. State the objective, written authorization references, approved locations, technologies, actions, transmission bands, disruption budget, and data rules. Bind every location to the authorization that names it; do not invent a survey, staging, or observation area to make existing evidence fit.
  2. Inventory exact radio targets. Separate in-scope systems from excluded and unattributed signals. An observed identifier is not proof of ownership.
  3. Inventory equipment by receive, transmit, emulate, and write capability. Keep transmit-capable equipment disabled until the plan authorizes its use.
  4. Start with passive observation. Bind identifiers, channel or frequency, location, time, and signal evidence before proposing active work.
  5. Record every proposed action against one in-scope target, one location, one device, one authorization reference, a stop condition, and recovery steps.
  6. Treat protocol reads, association, active enumeration, authentication, credential testing, impersonation, protocol writes, disruption, emulation, and replay as RF emission. Verify written transmission authority and the approved band.
  7. Give disruptive work an explicit duration and affected-device ceiling. Availability impact includes nearby clients and safety systems, not only the named access point or reader.
  8. Cover each declared technology across scope, passive inventory, attribution, trust, confidentiality, integration, client behavior, availability, monitoring, and data safety as applicable. Preserve blocked and delegated work.
  9. Register captures containing credentials, personal data, or bystander traffic and assign a restricted, redacted, destroyed, or other approved disposition.
  10. Delegate wired segmentation, identity infrastructure, companion apps, backend services, and physical-access consequences with an owner and status.

Interpretation rules

  • A visible SSID, device address, tag identifier, or signal is an observation, not attribution or authorization.
  • A capture proves only what its location, time, receiver, and recorded band support. Signal strength alone does not prove physical location.
  • A handshake or challenge capture does not prove a weak credential. An offline credential test and its result are separate evidence.
  • Failure to disrupt does not prove protected management or resilient service; range, channel, equipment, client state, and rate can explain the result.
  • A successful association does not prove segmentation failure. The downstream path needs its own authorized evidence or handoff.
  • Receive-only authority does not permit active protocol reads, replay, emulation, jamming, cloning, enumeration, or writes.

Rationalizations to reject

  • "The signal reaches us, so it is in scope." Propagation does not establish ownership, attribution, or authority.
  • "It is an unlicensed band, so transmission is allowed." Band rules, power limits, engagement authority, interference, and local law still apply.
  • "One short deauthentication or jam will not hurt anything." Disruption needs an explicit budget and can affect bystanders or safety-relevant systems.
  • "We only captured traffic; we did not attack it." Captures can contain credentials, personal data, and unrelated communications that require handling.
  • "The SSID and company name match, so this is their access point." Names are forgeable. Preserve attribution as unresolved until evidence supports it.
  • "a complete record means we can transmit." It validates the document. Written authorization and the operator still govern execution.

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 Wireless Assessment Plan AI skill do?

Plans authorized Wi-Fi, Bluetooth, RFID/NFC, SDR, and other radio assessments with explicit physical scope, transmission authority, disruption budgets, capture handling, coverage, and handoffs. Use before conducting or reviewing a wireless penetration test.

Why use Wireless Assessment Plan on TypingMind?

Because you install it once and use it with any model. Wireless Assessment Plan 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 Wireless Assessment Plan 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/wireless-assessment-plan. 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 Wireless Assessment Plan?

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 Wireless Assessment Plan?

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

Is the Wireless Assessment Plan 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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