Osint Scanner logo

Osint Scanner

Organization
zero-lab-ai
osint-scanner

Maps a target's public footprint — infrastructure, tech stack, leaked credentials, and people — using third-party data so no packet reaches the target. Use when scoping an engagement, planning a phishing pretext, or doing reconnaissance where the target must not detect it, and when deciding whether a recon action actually touches the target or not.

Overview

Publisherzero-lab-ai
Repositoryskills
Skill nameosint-scanner
Stars
232
Forks
14
Bundled files
5
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.

  • 5 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 Osint Scanner 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/recon-operations/skills/osint-scanner
  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/recon-operations/skills/osint-scanner .claude/skills/osint-scanner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Osint Scanner 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 Osint Scanner 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 Osint Scanner 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.

OSINT scanner

The stealthiest scan is the one you never send. OSINT reaches a target's footprint by asking third parties who already looked — so done correctly it generates zero packets to the target and nothing to detect.

The single discipline that makes this red-team-safe: know, for every action, whether it touches the target. A query to Shodan reads a scan Shodan already ran. Pointing a tool at the target's own web host or authoritative nameserver is contact, and contact is what a defender sees. The line between them is the skill; blur it and OSINT stops being passive.

When to Use

  • Scoping an engagement, planning a phishing pretext, or doing reconnaissance where the target must not detect it, and when deciding whether a recon action actually touches the target or not.
  • 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.

Classify contact before you run anything

sh
uv run {baseDir}/scripts/check_passive.py plan.json

Takes a plan of intended actions and reports each one's contact level — none (third party only), target (reaches the target's infrastructure), or unknown — and fails a passive plan that contains any target or unknown action. An unexamined "maybe" is treated as contact, because on a red team op an unexamined maybe is how you get caught. See references/contact-levels.md.

What you can learn without touching the target

Infrastructure and DNS

Map servers, subdomains and cloud without a packet to their firewall.

ToolGets youContact
amass (passive)subdomains from hundreds of passive sourcesnone
crt.shevery TLS cert ever issued — exposes dev/staging subdomainsnone
Shodan / Censysopen ports, software versions, exposures from global scansnone
dig TXT/MX (public resolver)mail provider and security stack (Proofpoint, Mimecast, Google)none

⚠ amass and theHarvester also have active modes that resolve and brute-force against the target. Those are contact. Declare the mode.

Defensive stack

Know what you are up against before planning anything.

  • BuiltWith / Wappalyzer — frameworks, CMS, trackers (programmatic Wappalyzer fetches the pages — that is contact; the catalog lookup is not).
  • SPF / DMARC records — reveal the mail-security vendor, so you learn their phishing defenses before writing a pretext.

Credentials and exposure

The initial foothold that needs no exploit.

  • DeHashed — historical breach corpus; search the domain for reused passwords.
  • TruffleHog — scans the target's public repos for leaked keys and secrets in commit history (the repo is public; you are reading what they published).
  • Google dorking — site:target.com filetype:pdf "confidential".

People — the phishing/vishing surface

  • theHarvester (passive sources) — emails, names, subdomains from search engines.
  • Hunter.io — corporate email format, to turn a name into an address.

This is red-team recon, not a pentest sweep

If you want completeness and do not care about noise, you want the network-recon skill (penetration-testing). This skill assumes the opposite: the target must not know you looked. Thoroughness that generates detectable contact is a loss here, not a win.

Rationalizations to reject

ThoughtReality
"It's just recon, it's harmless."Recon that touches the target is detectable and attributable. Contact is the thing to avoid, not the payload.
"dig is passive."dig against the target's authoritative nameserver is contact. Only a public-resolver cache read is not. State which.
"amass is a passive tool."Only in passive mode. Active/brute resolves against the target. Declare the mode or the script fails the plan.
"I'll run a quick httpx to confirm the hosts are live."That is the first packet you send the target, and it undoes the point of everything passive that came before.
"The tool isn't in the list, probably fine."Unknown is treated as contact and fails a passive plan. Classify it first.
"More thorough is better."On a red team op, more contact is worse. The quiet result that no one noticed beats the complete one that tripped an alert.
"TruffleHog touches their repo."It reads a repo they published publicly. Reading public data is not contact with their production infrastructure.

References

  • Contact levels — what none / target / unknown mean and how the checker decides
  • Sources — the aggregators, what each exposes, and its contact level

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 Osint Scanner AI skill do?

Maps a target's public footprint — infrastructure, tech stack, leaked credentials, and people — using third-party data so no packet reaches the target. Use when scoping an engagement, planning a phishing pretext, or doing reconnaissance where the target must not detect it, and when deciding whether a recon action actually touches the target or not.

Why use Osint Scanner on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zero-lab-ai/skills/tree/main/plugins/recon-operations/skills/osint-scanner. 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 Osint Scanner?

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 Osint Scanner?

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

Is the Osint Scanner 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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