Agent skill

Performing Ip Reputation Analysis With Shodan

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Analyze IP address reputation using the Shodan API to identify open ports, running services, known vulnerabilities, and hosting context for threat intelligence enrichment and incident triage.

Apache-2.0Auto-check passedSecurity

Install Performing Ip Reputation Analysis With Shodan

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-ip-reputation-analysis-with-shodan -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills performing-ip-reputation-analysis-with-shodan --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/performing-ip-reputation-analysis-with-shodan .claude/skills/performing-ip-reputation-analysis-with-shodan && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
performing-ip-reputation-analysis-with-shodan
GitHub stars
34k
Token cost
~3k tokens
SKILL.md length
384 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Analyze IP address reputation using the Shodan API to identify open ports, running services, known vulnerabilities, and hosting context for threat intelligence enrichment and incident triage.

  • Works in 3 steps: Basic IP Enrichment with Shodan API → Batch IP Reputation Check → Infrastructure Correlation
  • Tasks that involve OSINT
  • SKILL.md covers Overview, When to Use, Prerequisites and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; calls pip; reaches internetdb.shodan.io

What it does

Performing Ip Reputation Analysis With Shodan is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Analyze IP address reputation using the Shodan API to identify open ports, running services, known vulnerabilities, and hosting context for threat intelligence enrichment and incident triage.

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).

It sits in Security, covering OSINT. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve OSINT

Example prompts

  • “/performing-ip-reputation-analysis-with-shodan”

Requirements

  • Python 3
  • A credential in YOUR_SHODAN_API_KEY

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Basic IP Enrichment with Shodan API
  2. Batch IP Reputation Check
  3. Infrastructure Correlation

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • internetdb.shodan.io

    Also links to:

    • developer.shodan.io
    • github.com
    • query.ai
    • kb.torq.io
    • support.recordedfuture.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Performing Ip Reputation Analysis With Shodan loads about 3k tokens when it runs, and up to ~3.2k if it reads all its reference files. Until then it costs about 59 tokens; SKILL.md has 384 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~59
When it runs · the whole SKILL.md, loaded when a task matches
~3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 384 words, ~2,954 tokens.

Download SKILL.mdSave it as .claude/skills/performing-ip-reputation-analysis-with-shodan/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
performing-ip-reputation-analysis-with-shodan
description
Analyze IP address reputation using the Shodan API to identify open ports, running services, known vulnerabilities, and hosting context for threat intelligence enrichment and incident triage.
domain
cybersecurity
subdomain
threat-intelligence
tags
shodan, ip-reputation, enrichment, threat-intelligence, reconnaissance, vulnerability, api, internet-scanning
version
1.0
author
mahipal
license
Apache-2.0
nist_csf
ID.RA-01, ID.RA-05, DE.CM-01, DE.AE-02
mitre_attack
T1591, T1592, T1593, T1589, T1595

Performing IP Reputation Analysis with Shodan

Overview

Shodan is the world's first search engine for internet-connected devices, continuously scanning the IPv4 and IPv6 address space to catalog open ports, running services, SSL certificates, and known vulnerabilities. This skill covers using the Shodan API and InternetDB free API to enrich IP addresses from security alerts, assess threat levels based on exposed services and vulnerabilities, identify hosting infrastructure patterns, and integrate IP reputation data into SOC triage and threat intelligence workflows.

When to Use

  • When conducting security assessments that involve performing ip reputation analysis with shodan
  • When following incident response procedures for related security events
  • When performing scheduled security testing or auditing activities
  • When validating security controls through hands-on testing

Prerequisites

  • Python 3.9+ with shodan library (pip install shodan)
  • Shodan API key (free tier: limited queries; paid plans for higher limits and streaming)
  • Understanding of TCP/UDP ports, common services, and CVE identifiers
  • Familiarity with ASN, CIDR notation, and IP geolocation concepts
  • Network security knowledge for interpreting scan results

Key Concepts

Shodan Data Model

Each IP record in Shodan contains: open ports and protocols, banner data (service responses), SSL/TLS certificate details, known CVE vulnerabilities, hostname(s) and reverse DNS, ASN and ISP information, geographic location, operating system fingerprint, and historical scan data showing changes over time.

Show full SKILL.md (172 more words)Show less
InternetDB API

Shodan's free InternetDB API (internetdb.shodan.io) provides quick IP lookups without authentication, returning open ports, hostnames, tags, CPEs, and known vulnerabilities. This is useful for high-volume enrichment where the full Shodan API would hit rate limits.

Reputation Scoring

IP reputation is assessed by combining: number and type of open ports (unusual ports indicate compromise), vulnerable services (unpatched software with known CVEs), hosting type (residential, cloud, VPN/proxy, bulletproof hosting), historical activity (past associations with malware, scanning, spam), and geographic context (countries known for specific threat activity).

Workflow

Step 1: Basic IP Enrichment with Shodan API
python
import shodan
import json
from datetime import datetime

class ShodanEnricher:
    def __init__(self, api_key):
        self.api = shodan.Shodan(api_key)
        self.info = self.api.info()
        print(f"[+] Shodan API initialized. Credits: {self.info.get('scan_credits', 0)}")

    def enrich_ip(self, ip_address):
        """Full enrichment of an IP address via Shodan."""
        try:
            host = self.api.host(ip_address)
            enrichment = {
                "ip": ip_address,
                "organization": host.get("org", ""),
                "asn": host.get("asn", ""),
                "isp": host.get("isp", ""),
                "country": host.get("country_name", ""),
                "country_code": host.get("country_code", ""),
                "city": host.get("city", ""),
                "latitude": host.get("latitude"),
                "longitude": host.get("longitude"),
                "os": host.get("os", ""),
                "ports": host.get("ports", []),
                "hostnames": host.get("hostnames", []),
                "domains": host.get("domains", []),
                "vulns": host.get("vulns", []),
                "tags": host.get("tags", []),
                "last_update": host.get("last_update", ""),
                "services": [],
            }

            for service in host.get("data", []):
                svc = {
                    "port": service.get("port", 0),
                    "transport": service.get("transport", "tcp"),
                    "product": service.get("product", ""),
                    "version": service.get("version", ""),
                    "module": service.get("_shodan", {}).get("module", ""),
                    "banner": service.get("data", "")[:200],
                }
                if "ssl" in service:
                    svc["ssl_subject"] = service["ssl"].get("cert", {}).get("subject", {})
                    svc["ssl_issuer"] = service["ssl"].get("cert", {}).get("issuer", {})
                    svc["ssl_expires"] = service["ssl"].get("cert", {}).get("expires", "")
                enrichment["services"].append(svc)

            # Calculate reputation score
            enrichment["reputation"] = self._calculate_reputation(enrichment)
            print(f"[+] {ip_address}: {len(enrichment['ports'])} ports, "
                  f"{len(enrichment['vulns'])} vulns, "
                  f"reputation: {enrichment['reputation']['level']}")
            return enrichment

        except shodan.APIError as e:
            print(f"[-] Shodan error for {ip_address}: {e}")
            return None

    def _calculate_reputation(self, data):
        """Calculate IP reputation score based on Shodan data."""
        score = 0
        factors = []

        # Vulnerability assessment
        vuln_count = len(data.get("vulns", []))
        if vuln_count > 10:
            score += 40
            factors.append(f"{vuln_count} known vulnerabilities")
        elif vuln_count > 5:
            score += 25
            factors.append(f"{vuln_count} known vulnerabilities")
        elif vuln_count > 0:
            score += 10
            factors.append(f"{vuln_count} known vulnerabilities")

        # Suspicious port analysis
        suspicious_ports = {4444, 5555, 6666, 8888, 9090, 1234, 31337,
                           6667, 6697, 8080, 8443, 3128, 1080}
        open_ports = set(data.get("ports", []))
        sus_found = open_ports.intersection(suspicious_ports)
        if sus_found:
            score += 15
            factors.append(f"suspicious ports: {sus_found}")

        # Tag-based assessment
        malicious_tags = {"self-signed", "cloud", "vpn", "proxy", "tor"}
        tags = set(data.get("tags", []))
        mal_tags = tags.intersection(malicious_tags)
        if mal_tags:
            score += 10
            factors.append(f"tags: {mal_tags}")

        # Too many open ports
        port_count = len(data.get("ports", []))
        if port_count > 20:
            score += 15
            factors.append(f"excessive open ports ({port_count})")

        level = (
            "critical" if score >= 50
            else "high" if score >= 35
            else "medium" if score >= 15
            else "low"
        )

        return {"score": score, "level": level, "factors": factors}

    def enrich_ip_free(self, ip_address):
        """Quick IP enrichment using free InternetDB API."""
        import requests
        resp = requests.get(f"https://internetdb.shodan.io/{ip_address}", timeout=10)
        if resp.status_code == 200:
            data = resp.json()
            print(f"[+] InternetDB: {ip_address} -> "
                  f"{len(data.get('ports', []))} ports, "
                  f"{len(data.get('vulns', []))} vulns")
            return data
        return None

enricher = ShodanEnricher("YOUR_SHODAN_API_KEY")
result = enricher.enrich_ip("8.8.8.8")
print(json.dumps(result, indent=2, default=str))
Step 2: Batch IP Reputation Check
python
import time

def batch_ip_reputation(enricher, ip_list, output_file="ip_reputation.json"):
    """Check reputation for a list of IP addresses."""
    results = []
    for i, ip in enumerate(ip_list):
        result = enricher.enrich_ip(ip)
        if result:
            results.append(result)
        if (i + 1) % 10 == 0:
            print(f"  [{i+1}/{len(ip_list)}] Processed")
            time.sleep(1)  # Rate limiting

    # Sort by reputation score (highest risk first)
    results.sort(key=lambda x: x.get("reputation", {}).get("score", 0), reverse=True)

    with open(output_file, "w") as f:
        json.dump(results, f, indent=2, default=str)

    # Summary
    levels = {"critical": 0, "high": 0, "medium": 0, "low": 0}
    for r in results:
        level = r.get("reputation", {}).get("level", "low")
        levels[level] += 1

    print(f"\n=== Batch Reputation Summary ===")
    print(f"Total IPs: {len(results)}")
    for level, count in levels.items():
        print(f"  {level.upper()}: {count}")

    return results

suspicious_ips = ["203.0.113.1", "198.51.100.5", "192.0.2.100"]
results = batch_ip_reputation(enricher, suspicious_ips)
Step 3: Infrastructure Correlation
python
def correlate_infrastructure(enricher, ip_address):
    """Find related infrastructure based on shared attributes."""
    host_data = enricher.enrich_ip(ip_address)
    if not host_data:
        return {}

    correlations = {
        "same_org": [],
        "same_asn": [],
        "shared_ssl": [],
    }

    # Search for same organization
    org = host_data.get("organization", "")
    if org:
        try:
            results = enricher.api.search(f'org:"{org}"', limit=20)
            for match in results.get("matches", []):
                correlations["same_org"].append({
                    "ip": match.get("ip_str", ""),
                    "port": match.get("port", 0),
                    "product": match.get("product", ""),
                })
        except shodan.APIError:
            pass

    # Search for same SSL certificate
    for service in host_data.get("services", []):
        ssl_subject = service.get("ssl_subject", {})
        if ssl_subject:
            cn = ssl_subject.get("CN", "")
            if cn:
                try:
                    results = enricher.api.search(f'ssl.cert.subject.CN:"{cn}"', limit=20)
                    for match in results.get("matches", []):
                        correlations["shared_ssl"].append({
                            "ip": match.get("ip_str", ""),
                            "cn": cn,
                        })
                except shodan.APIError:
                    pass

    print(f"[+] Infrastructure correlations for {ip_address}:")
    print(f"  Same org: {len(correlations['same_org'])} hosts")
    print(f"  Shared SSL: {len(correlations['shared_ssl'])} hosts")
    return correlations

Validation Criteria

  • Shodan API queried successfully with proper authentication
  • IP enrichment returns ports, services, vulnerabilities, and geolocation
  • Reputation scoring classifies IPs by threat level
  • Batch enrichment handles rate limiting correctly
  • Infrastructure correlation identifies related hosts
  • InternetDB free API used for high-volume lookups

References

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/performing-ip-reputation-analysis-with-shodan of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

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Categories

Questions about Performing Ip Reputation Analysis With Shodan

What does Performing Ip Reputation Analysis With Shodan do?

Analyze IP address reputation using the Shodan API to identify open ports, running services, known vulnerabilities, and hosting context for threat intelligence enrichment and incident triage. Performing Ip Reputation Analysis With Shodan is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Analyze IP address reputation using the Shodan API to identify open ports, running services, known vulnerabilities, and hosting context for threat intelligence enrichment and incident triage.

When should I use Performing Ip Reputation Analysis With Shodan?

Performing Ip Reputation Analysis With Shodan fits situations like: tasks that involve OSINT.

How do I install Performing Ip Reputation Analysis With Shodan in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-ip-reputation-analysis-with-shodan -a claude-code`. Or copy the skill folder (skills/performing-ip-reputation-analysis-with-shodan in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/performing-ip-reputation-analysis-with-shodan in your project. Claude Code loads it when a task matches its description.

How do I install Performing Ip Reputation Analysis With Shodan in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-ip-reputation-analysis-with-shodan -a codex`. Or copy the skill folder (skills/performing-ip-reputation-analysis-with-shodan in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/performing-ip-reputation-analysis-with-shodan in your project. Codex loads it when a task matches its description.

Can I use Performing Ip Reputation Analysis With Shodan in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill performing-ip-reputation-analysis-with-shodan -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/performing-ip-reputation-analysis-with-shodan, .gemini/skills/performing-ip-reputation-analysis-with-shodan, .github/skills/performing-ip-reputation-analysis-with-shodan and .opencode/skills/performing-ip-reputation-analysis-with-shodan in your project.

What does Performing Ip Reputation Analysis With Shodan need to run?

Going by SKILL.md and its folder, Performing Ip Reputation Analysis With Shodan needs Python for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: Python 3; A credential in YOUR_SHODAN_API_KEY.

Does Performing Ip Reputation Analysis With Shodan access the network?

SKILL.md names 6 domains. In commands or code: internetdb.shodan.io; the agent is likely to contact it when it follows the instructions. As links in the text: developer.shodan.io, github.com, query.ai, kb.torq.io and support.recordedfuture.com. This is read from the text; nothing was executed.

Is Performing Ip Reputation Analysis With Shodan safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Performing Ip Reputation Analysis With Shodan use?

Performing Ip Reputation Analysis With Shodan is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Performing Ip Reputation Analysis With Shodan use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 290 tokens, read only when the agent opens those files.

What are the alternatives to Performing Ip Reputation Analysis With Shodan?

Skills that share tags, products or a category with Performing Ip Reputation Analysis With Shodan: Metabigor OSINT Recon (j3ssie/metabigor, 1.9k stars), Ctf Osint (ljagiello/ctf-skills, 3.4k stars), ShadowBroker Intelligence Client (BigBodyCobain/Shadowbroker, 11k stars) and Awesome Osint Operator (shoyann/RZK-The-Hunter, 141 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Performing Ip Reputation Analysis With Shodan?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 34,116 GitHub stars. The repository holds 644 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.