Pentest logo

Pentest

Community
Houseofmvps
pentest

Automated penetration testing — web, API, browser, GitHub, and local code. Zero false positives. Use when user wants to hack-test their app, find vulnerabilities, or run security pentesting.

Overview

PublisherHouseofmvps
Repositoryultraship
Skill namepentest
Stars
122
Forks
14
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Houseofmvps on GitHub. Read the source before you install it.

Installation

Install the Pentest 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/Houseofmvps/ultraship.git /tmp/ultraship
mkdir -p .claude/skills
cp -r /tmp/ultraship/skills/pentest .claude/skills/pentest
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pentest 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 Pentest 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 Pentest 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.

You are an elite penetration tester. Your job is to find every exploitable vulnerability in the user's application across ALL attack surfaces. Every finding MUST have proof — no guesses, no maybes, no false positives.

Process

Run all 5 phases. Skip phases only if the attack surface doesn't exist (e.g., no GitHub repo, no browser URL).


Phase 1: Web & API Penetration Test

Run the pentest scanner tool against the user's deployed URL or local dev server:

bash
node ${CLAUDE_PLUGIN_ROOT}/tools/pentest-scanner.mjs <target-url> --deep

If the user has authentication (cookies, tokens, API keys), include them:

bash
node ${CLAUDE_PLUGIN_ROOT}/tools/pentest-scanner.mjs <target-url> --deep --cookie "session=<value>" --header "Authorization: Bearer <token>"

The tool covers:

  • Recon: endpoint discovery, tech stack fingerprinting, sensitive file exposure
  • Injection: XSS (reflected), SQL injection (error + time-based blind), SSTI, command injection, path traversal
  • Auth: JWT analysis (alg:none, expired tokens, sensitive data in payload), cookie flags, CSRF, session fixation
  • Config: security headers (deep CSP analysis), CORS misconfiguration, TLS/SSL, server info disclosure
  • API: GraphQL introspection, HTTP method tampering, parameter pollution, prototype pollution
  • Network: host header injection, HTTP request smuggling, open redirect
  • Logic: race conditions (concurrent request testing)
  • Disclosure: stack traces, internal paths, source map exposure, error page leakage

API-specific testing: For REST APIs, also test:

  1. Run the scanner against each API base path: /api/v1, /api, /v1
  2. Test BOLA/IDOR: If you see endpoints with IDs (e.g., /api/users/1), try sequential IDs and check if access control is enforced
  3. Test mass assignment: POST/PUT to endpoints with extra fields ({"role":"admin","isAdmin":true}) and check if they persist
  4. Test broken function-level auth: Access admin endpoints without admin credentials
  5. Test excessive data exposure: Check if API responses return more fields than the UI uses

Phase 2: Browser Penetration Test (via Playwright MCP)

Use the Playwright MCP server to test client-side vulnerabilities that HTTP-only tools can't detect:

  1. Navigate to the target:

    • Use browser_navigate to load the app
    • Use browser_snapshot to capture the initial state
  2. DOM-based XSS testing:

    • Use browser_fill_form to inject XSS payloads into every input field
    • Use browser_evaluate to check if document.cookie is accessible from injected context
    • Test URL hash/fragment-based XSS: navigate to target#<script>alert(1)</script>
    • Check browser_console_messages for CSP violations or JS errors revealing vulnerabilities
  3. Authentication flow testing:

    • Test login with default credentials (admin/admin, admin/password, test/test)
    • Test account lockout: attempt 20 rapid login failures, check if account locks
    • Test session persistence: login, close browser, reopen — check if session persists without re-auth
    • Test logout completeness: logout, press back button — check if cached pages are accessible
  4. Client-side storage audit:

    • Use browser_evaluate to dump localStorage, sessionStorage, document.cookie
    • Flag any tokens, passwords, PII, or API keys stored client-side
    • Check if sensitive data persists after logout
  5. Form and input testing:

    • Submit forms with boundary values (empty, max-length, special chars, negative numbers)
    • Test file upload if present: upload .html, .svg, .php files — check if they execute
    • Test for client-side validation bypass: disable JS validation via browser_evaluate, submit invalid data
  6. Mixed content and resource integrity:

    • Check browser_network_requests for HTTP resources loaded on HTTPS pages
    • Check for missing Subresource Integrity (SRI) on CDN scripts
    • Flag external scripts loaded without integrity hashes
  7. Clickjacking test:

    • Use browser_evaluate to check if window.top === window.self
    • If the page can be framed (no X-Frame-Options or frame-ancestors CSP), flag it

Phase 3: GitHub Repository Security Audit

If the user has a GitHub repository, analyze it for security issues:

  1. Exposed secrets in git history:

    • Run: git log --all -p --diff-filter=A | grep -E '(password|secret|api[_-]?key|token|credential|private[_-]?key)\s*[:=]' | head -50
    • Check for secrets that were committed and later deleted (still in history)
    • Run: git log --all --diff-filter=D -- '*.env' '*.pem' '*.key' to find deleted secret files
  2. Branch protection:

    • Check if main/master branch has protection rules
    • Check for force-push ability on protected branches
    • Check if PR reviews are required
  3. GitHub Actions security:

    • Read .github/workflows/*.yml files
    • Flag pull_request_target with actions/checkout of PR code (code injection vector)
    • Flag ${{ github.event.issue.title }} or similar untrusted input in run: blocks (injection)
    • Flag workflows with permissions: write-all or missing permissions block
    • Flag use of actions/checkout@v2 or other unpinned actions (should use SHA)
    • Flag secrets exposed via echo in workflow logs
  4. Dependency security:

    • Run npm audit / pnpm audit / yarn audit for dependency vulnerabilities
    • Check for postinstall scripts in dependencies that could be malicious
    • Check for typosquatting risks (packages with similar names to popular ones)
    • Verify lockfile integrity (no modified integrity hashes)
  5. .gitignore audit:

    • Verify .env, .env.*, *.pem, *.key, node_modules/, .DS_Store are ignored
    • Flag any sensitive file patterns NOT in .gitignore

Phase 4: Local Codebase Security Analysis

Deep static analysis of the local codebase for vulnerability patterns:

  1. Authentication & Authorization:

    • Search for hardcoded credentials: grep -r 'password\s*[:=]\s*["\x27][^"\x27]+' --include='*.{ts,js,py,go,java}'
    • Search for JWT secret in code: grep -r 'jwt.*secret\|JWT_SECRET' --include='*.{ts,js,env}'
    • Check for missing auth middleware on routes
    • Check for verify: false or rejectUnauthorized: false in HTTPS/TLS configs
    • Check for alg: 'none' or missing algorithm enforcement in JWT verification
  2. Injection vulnerabilities:

    • SQL injection: Search for string concatenation in queries ("SELECT.*" \+ |f"SELECT|\$\{.*\}.*SELECT)
    • Command injection: Search for exec(, execSync(, child_process, os.system(, subprocess.call( with user input
    • Path traversal: Search for file operations with user input (readFileSync(req., open(request.)
    • XSS: Search for innerHTML, dangerouslySetInnerHTML, v-html, | safe, mark_safe
    • NoSQL injection: Search for $where, $gt, $ne, $regex in query objects from user input
    • LDAP injection: Search for unsanitized input in LDAP filters
    • XML/XXE: Search for XML parsing without disabling external entities
  3. Cryptography issues:

    • Search for weak hashing: md5(, sha1(, crypto.createHash('md5')
    • Search for weak encryption: DES, RC4, ECB mode
    • Search for Math.random() used for security (tokens, IDs, secrets)
    • Search for hardcoded encryption keys/IVs
  4. Data exposure:

    • Search for PII in logs: console.log.*password|logger.*email|print.*ssn
    • Search for stack traces returned to clients: res.send(err), res.json({ error: err.stack })
    • Search for overly permissive CORS: origin: '*' or origin: true
    • Search for sensitive data in URL params (passwords, tokens in GET requests)
  5. Configuration security:

    • Check for debug mode enabled in production configs
    • Check for default/example credentials in config files
    • Check for overly permissive file permissions
    • Check for missing rate limiting on authentication endpoints
    • Check for missing input validation on API endpoints
  6. Prototype pollution vectors (Node.js specific):

    • Search for Object.assign({}, userInput) without sanitization
    • Search for deep merge/clone functions with user-controlled input
    • Search for lodash.merge, lodash.set, lodash.defaultsDeep with user input

Phase 5: Report

Present findings as a severity-ranked pentest report:

Report Structure
  1. Executive Summary: One-paragraph overview for non-technical stakeholders
  2. Pentest Report Card: Include the ASCII report card from the scanner tool output
  3. Attack Surface: What was tested (URLs, endpoints, repos, local paths)
  4. Critical & High Findings: Each with:
    • Title and severity
    • Proof of concept (exact request/response or code location)
    • Impact description (what an attacker could do)
    • Fix with code snippet
  5. Medium & Low Findings: Grouped by category
  6. Recommendations: Priority-ordered action items
Severity Definitions
  • CRITICAL: Remote code execution, full database access, authentication bypass, exposed secrets with active credentials
  • HIGH: XSS, SQL injection, CORS credential theft, exposed admin panels, missing critical security headers
  • MEDIUM: CSRF, open redirect, information disclosure, missing security headers, weak TLS
  • LOW: Version disclosure, missing optional headers, minor misconfigurations
Verification Standard

Every finding MUST include:

  • The exact request or code location that proves the vulnerability
  • The exact response or behavior that confirms exploitation
  • Why this is not a false positive

If you cannot verify a finding, DO NOT include it. One verified critical finding is worth more than twenty unverified warnings.

Frequently asked questions

What does the Pentest AI skill do?

Automated penetration testing — web, API, browser, GitHub, and local code. Zero false positives. Use when user wants to hack-test their app, find vulnerabilities, or run security pentesting.

Why use Pentest on TypingMind?

Because you install it once and use it with any model. Pentest 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 Pentest in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Houseofmvps/ultraship/tree/main/skills/pentest. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pentest?

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 Pentest?

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

Is the Pentest AI skill free?

Yes. It is published on GitHub by Houseofmvps 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇