Mobile Application Assessment logo

Mobile Application Assessment

Organization
zero-lab-ai
mobile-application-assessment

Tracks Android and iOS application assessment coverage across packages, platform components, local storage, device behavior, WebViews, native code, network trust, and delegated backend APIs. Use when planning, conducting, reviewing, or handing over an authorized mobile app penetration test.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill namemobile-application-assessment
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 Mobile Application Assessment 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/application-endpoint-security/skills/mobile-application-assessment
  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/application-endpoint-security/skills/mobile-application-assessment .claude/skills/mobile-application-assessment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mobile Application Assessment 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 Mobile Application Assessment 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 Mobile Application Assessment 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.

Mobile application assessment

A decompiled indicator is not a runtime exploit, a rooted-device observation is not ordinary-device reachability, and one denied backend request is not an API authorization assessment. Keep each claim tied to its build, layer, method, and evidence.

When to Use

  • Planning, conducting, reviewing, or handing over an authorized mobile app penetration test.
  • 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. Record the exact Android package or iOS bundle, platform, build kind, and SHA-256. Keep evidence from different builds in separate assessments.
  2. State allowed action classes, structured test devices and accounts, in-scope origins, and constraints. Prevent production and third-party traffic at the test boundary when the app contains out-of-scope destinations.
  3. Inventory one application surface per build, then add discovered storage, component, network endpoint, WebView, native library, cryptographic use, runtime defense, privacy channel, and backend API surfaces.
  4. Give every surface an evidence reference and disposition. Static reachability supports a conditional exposure; runtime effect needs runtime evidence.
  5. Cover all eight current MASVS categories for the application surface: storage, cryptography, authentication, network, platform interaction, code, resilience, and privacy. Add the categories derived for each detailed surface.
  6. Record each case as executed, planned, blocked, not-applicable, or delegated. Only executed cases can claim a finding, no issue, or an inconclusive result. Preserve blockers and technical rationales.
  7. Separate app, device, backend, and third-party evidence. A mobile client can expose an API route, but server-side authorization coverage belongs in the web assessment ledger. Preserve a bounded observed backend request as an executed case, then add a separate delegated case for the broader handoff.
  8. Treat public identifiers, private secrets, exported components, client-side state, pinning, backup settings, root detection, and native code according to demonstrated impact. Their presence alone does not set severity.
  9. Build category summaries from all recorded cases. Record the derived open case IDs so planned, blocked, delegated, and inconclusive work stays visible even when a confirmed finding takes precedence in the category claim.

Interpretation rules

  • App-private plaintext remains relevant under the stated compromised-device threat model; it does not mean another ordinary app can read the data.
  • exported=true proves reachability, not missing authorization or unsafe effect.
  • Pinning observed on one flow says nothing about unobserved endpoints. Bypassing it in a lab is a test-enablement action, not itself a finding.
  • A publishable key is not a private secret. Identify the key type and supported privilege before reporting exposure.
  • Changing local UI state proves client control of presentation. It proves a server authorization issue only when the server accepts the resulting action.
  • Root or jailbreak detection is threat-model dependent. Record what it protects and whether enforcement is local or server-side.
  • A native library is an analysis surface, not a vulnerability.

Rationalizations to reject

  • "The manifest proves exploitation." It proves configuration and potential reachability. Trace and test the effect before claiming impact.
  • "The emulator is rooted, so plaintext storage is automatically critical." Severity depends on data value, token lifetime, replay, device assumptions, and compensating server controls.
  • "We bypassed pinning, so pinning failed." An authorized instrumentation bypass proves tester capability, not a defect in the deployed control.
  • "The API denied one cross-user request, so authorization is sound." The result applies only to that actor, target, operation, identifier, and backend.
  • "The hostname is in the APK, so it is in scope." Scope comes from written authorization. Record an out-of-scope destination without contacting it.
  • "A scanner covered MASVS." Coverage requires explicit cases, evidence, blockers, and honest summaries across static and dynamic methods.

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 Mobile Application Assessment AI skill do?

Tracks Android and iOS application assessment coverage across packages, platform components, local storage, device behavior, WebViews, native code, network trust, and delegated backend APIs. Use when planning, conducting, reviewing, or handing over an authorized mobile app penetration test.

Why use Mobile Application Assessment on TypingMind?

Because you install it once and use it with any model. Mobile Application Assessment 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 Mobile Application Assessment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zero-lab-ai/skills/tree/main/plugins/application-endpoint-security/skills/mobile-application-assessment. 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 Mobile Application Assessment?

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 Mobile Application Assessment?

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

Is the Mobile Application Assessment 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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