performing-ip-reputation-analysis-with-shodan▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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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.
| 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 |
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
shodanlibrary (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.
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
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
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
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
How to use performing-ip-reputation-analysis-with-shodan on Cursor
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Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add performing-ip-reputation-analysis-with-shodan
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches performing-ip-reputation-analysis-with-shodan from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate performing-ip-reputation-analysis-with-shodan. Access the skill through slash commands (e.g., /performing-ip-reputation-analysis-with-shodan) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
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Use Cases▌
Exploratory Data Analysis
Quickly understand datasets, identify patterns, and generate insights
Example
Analyze CSV with 100K rows, identify outliers, visualize correlations, suggest hypotheses
Reduce EDA time from hours to minutes, uncover insights faster
Data Cleaning & Transformation
Write scripts to clean messy data, handle missing values, normalize formats
Example
Generate Python/SQL to fix date formats, impute missing values, remove duplicates
Automate 80% of data preprocessing work
Statistical Analysis
Perform hypothesis testing, regression, and statistical modeling
Example
Run A/B test analysis, calculate confidence intervals, interpret p-values
Get statistically sound analysis without PhD in statistics
Data Visualization
Create charts, dashboards, and visual reports
Example
Generate matplotlib/seaborn code for time series plots, distribution charts, heatmaps
Build presentation-ready visualizations 3x faster
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Python environment (pandas, numpy, matplotlib) or SQL database access
- ›Basic understanding of data analysis concepts
- ›Sample datasets for testing skill capabilities
Time Estimate
20-40 minutes to set up and run first analysis
Installation Steps
- 1.Install data analysis skill using provided command
- 2.Prepare a sample dataset (CSV, JSON, or database connection)
- 3.Start with descriptive statistics: 'Summarize this dataset'
- 4.Progress to visualization: 'Create a scatter plot of X vs Y'
- 5.Advanced analysis: 'Run linear regression and interpret results'
- 6.Validate outputs: check calculations, verify visualizations make sense
- 7.Document analysis workflow for reproducibility
Common Pitfalls
- ⚠Not validating statistical assumptions before applying tests
- ⚠Accepting visualizations without checking data accuracy
- ⚠Overlooking data quality issues (missing values, outliers)
- ⚠Misinterpreting correlation as causation
- ⚠Using wrong statistical test for data distribution
- ⚠Not considering sample size and statistical power
Best Practices▌
✓ Do
- +Always validate data quality before analysis
- +Check statistical assumptions (normality, independence, etc.)
- +Visualize data before running statistical tests
- +Document analysis steps for reproducibility
- +Cross-validate findings with domain experts
- +Use skill for initial exploration, then dive deeper manually
- +Save generated code for reuse on similar datasets
✗ Don't
- −Don't trust analysis without verifying data quality
- −Don't apply statistical tests without checking assumptions
- −Don't make business decisions solely on AI-generated analysis
- −Don't ignore outliers without investigating cause
- −Don't skip data validation and sanity checks
- −Don't use for mission-critical financial or medical analysis without expert review
💡 Pro Tips
- ★Describe data context: 'This is user behavior data from e-commerce site'
- ★Ask for interpretation: 'What does this correlation mean for business?'
- ★Request multiple approaches: 'Show 3 ways to handle missing data'
- ★Combine AI analysis with domain expertise for best insights
- ★Use for rapid prototyping, then refine analysis manually
When to Use This▌
✓ Use When
Use for exploratory data analysis, data cleaning, statistical testing, visualization prototyping, and learning new analysis techniques. Best for initial exploration and rapid insights.
✗ Avoid When
Avoid for mission-critical financial analysis, medical research requiring regulatory compliance, production ML models, or when deep statistical expertise is required for nuanced interpretation.
Learning Path▌
- 1Basic: descriptive statistics, data cleaning, simple visualizations
- 2Intermediate: hypothesis testing, regression, correlation analysis
- 3Advanced: time series analysis, clustering, predictive modeling
- 4Expert: causal inference, experimental design, advanced statistical methods
Discussion
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Ratings
4.6★★★★★46 reviews- ★★★★★Ama Johnson· Dec 28, 2024
Useful defaults in performing-ip-reputation-analysis-with-shodan — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Dhruvi Jain· Dec 24, 2024
We added performing-ip-reputation-analysis-with-shodan from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Yuki Johnson· Dec 24, 2024
I recommend performing-ip-reputation-analysis-with-shodan for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Henry Singh· Dec 16, 2024
performing-ip-reputation-analysis-with-shodan fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Benjamin Kapoor· Dec 12, 2024
Registry listing for performing-ip-reputation-analysis-with-shodan matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Omar Yang· Dec 4, 2024
Solid pick for teams standardizing on skills: performing-ip-reputation-analysis-with-shodan is focused, and the summary matches what you get after install.
- ★★★★★Kwame Verma· Nov 19, 2024
I recommend performing-ip-reputation-analysis-with-shodan for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Emma Liu· Nov 11, 2024
We added performing-ip-reputation-analysis-with-shodan from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Yusuf Gill· Nov 7, 2024
Registry listing for performing-ip-reputation-analysis-with-shodan matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Noor Ramirez· Nov 3, 2024
performing-ip-reputation-analysis-with-shodan fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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