Agent skill

Building Adversary Infrastructure Tracking System

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Build an automated adversary infrastructure tracking system in Python (dnspython, python-whois, shodan, networkx) that pivots across passive DNS, certificate transparency logs, WHOIS records, and IP…

Apache-2.0Auto-check passedSecurity

Install Building Adversary Infrastructure Tracking System

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-adversary-infrastructure-tracking-system -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills building-adversary-infrastructure-tracking-system --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/building-adversary-infrastructure-tracking-system .claude/skills/building-adversary-infrastructure-tracking-system && 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
building-adversary-infrastructure-tracking-system
GitHub stars
34k
Token cost
~3.7k tokens
SKILL.md length
390 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Build an automated adversary infrastructure tracking system in Python (dnspython, python-whois, shodan, networkx) that pivots across passive DNS, certificate transparency logs, WHOIS records, and IP…

  • Works in 3 steps: Passive DNS Infrastructure Discovery → Build Infrastructure Graph → Monitor for New Infrastructure
  • Pivoting from known indicators to discover related C2 infrastructure
  • SKILL.md covers Overview, When to Use, Prerequisites and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; reaches api.securitytrails.com

What it does

Building Adversary Infrastructure Tracking System is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Build an automated adversary infrastructure tracking system in Python (dnspython, python-whois, shodan, networkx) that pivots across passive DNS, certificate transparency logs, WHOIS records, and IP enrichment to map threat-actor C2 networks and flag newly registered domains matching known patterns. Use when pivoting from known indicators to discover related C2 infrastructure or maintaining a continuously updated map of a threat actor's network.

Its SKILL.md is about 3.7k 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. It works with Python and NetworkX. 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

  • Pivoting from known indicators to discover related C2 infrastructure
  • Maintaining a continuously updated map of a threat actors network

Example prompts

  • “/building-adversary-infrastructure-tracking-system”

Requirements

  • Python 3
  • A credential in YOUR_ST_KEY
  • A credential in YOUR_VT_KEY

Workflow steps

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

  1. Passive DNS Infrastructure Discovery
  2. Build Infrastructure Graph
  3. Monitor for New Infrastructure

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.

    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:

    • api.securitytrails.com

    Also links to:

    • blogs.juniper.net
    • censys.com
    • embeeresearch.io
    • validin.com
    • securitytrails.com
    • hunt.io

    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

Building Adversary Infrastructure Tracking System loads about 3.7k tokens when it runs, and up to ~4.1k if it reads all its reference files. Until then it costs about 125 tokens; SKILL.md has 390 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~125
When it runs · the whole SKILL.md, loaded when a task matches
~3.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.1k

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). 390 words, ~3,710 tokens.

Download SKILL.mdSave it as .claude/skills/building-adversary-infrastructure-tracking-system/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
building-adversary-infrastructure-tracking-system
description
Build an automated adversary infrastructure tracking system in Python (dnspython, python-whois, shodan, networkx) that pivots across passive DNS, certificate transparency logs, WHOIS records, and IP enrichment to map threat-actor C2 networks and flag newly registered domains matching known patterns. Use when pivoting from known indicators to discover related C2 infrastructure or maintaining a continuously updated map of a threat actor's network.
domain
cybersecurity
subdomain
threat-intelligence
tags
infrastructure-tracking, passive-dns, c2, whois, threat-actor, pivoting, threat-intelligence, domain-analysis
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
T1583.001, T1583.004, T1596.001, T1590.002, T1071.001

Building Adversary Infrastructure Tracking System

Overview

Adversary infrastructure tracking uses passive DNS records, certificate transparency logs, WHOIS registration data, and IP enrichment to discover, map, and monitor threat actor command-and-control (C2) networks. Attackers frequently reuse hosting providers, registrars, SSL certificates, and naming patterns across campaigns, enabling analysts to pivot from known indicators to discover new infrastructure. This skill covers building an automated tracking system that identifies infrastructure relationships, detects newly registered domains matching adversary patterns, and maintains a continuously updated map of threat actor networks.

When to Use

  • When deploying or configuring building adversary infrastructure tracking system capabilities in your environment
  • When establishing security controls aligned to compliance requirements
  • When building or improving security architecture for this domain
  • When conducting security assessments that require this implementation

Prerequisites

  • Python 3.9+ with requests, dnspython, python-whois, shodan, networkx libraries
  • API keys: SecurityTrails, PassiveTotal/RiskIQ, Shodan, VirusTotal
  • Access to passive DNS data sources
  • Understanding of DNS infrastructure, hosting, and domain registration
  • Graph database (Neo4j) or NetworkX for relationship visualization

Key Concepts

Passive DNS

Passive DNS captures historical DNS resolution data, recording which domains resolved to which IPs and when. Unlike active DNS queries, passive DNS preserves historical relationships even after records change, enabling analysts to track infrastructure changes, identify shared hosting patterns, and discover related domains that resolved to the same IP addresses over time.

Show full SKILL.md (170 more words)Show less
Infrastructure Pivoting

Pivoting identifies related infrastructure by following connections: IP pivot (find all domains on an IP), domain pivot (find all IPs a domain resolved to), WHOIS pivot (find domains with same registrant), certificate pivot (find hosts sharing SSL certificates), and NS/MX pivot (find domains using same name servers or mail servers).

Adversary Infrastructure Patterns

Threat actors exhibit patterns: preferred registrars (Namecheap, REG.RU, Tucows), preferred hosting (bulletproof hosting providers, cloud services), domain generation algorithms (DGA), consistent naming patterns, and certificate reuse across campaigns.

Workflow

Step 1: Passive DNS Infrastructure Discovery
python
import requests
import json
from collections import defaultdict
from datetime import datetime

class InfrastructureTracker:
    def __init__(self, securitytrails_key=None, vt_key=None, shodan_key=None):
        self.st_key = securitytrails_key
        self.vt_key = vt_key
        self.shodan_key = shodan_key
        self.infrastructure_graph = defaultdict(lambda: {"nodes": set(), "edges": []})

    def passive_dns_lookup(self, domain):
        """Query passive DNS for domain resolution history."""
        headers = {"apikey": self.st_key}
        url = f"https://api.securitytrails.com/v1/history/{domain}/dns/a"
        resp = requests.get(url, headers=headers, timeout=30)
        if resp.status_code == 200:
            records = resp.json().get("records", [])
            history = []
            for record in records:
                for value in record.get("values", []):
                    history.append({
                        "domain": domain,
                        "ip": value.get("ip", ""),
                        "first_seen": record.get("first_seen", ""),
                        "last_seen": record.get("last_seen", ""),
                        "type": record.get("type", "a"),
                    })
            print(f"[+] Passive DNS for {domain}: {len(history)} records")
            return history
        return []

    def reverse_ip_lookup(self, ip_address):
        """Find all domains hosted on an IP address."""
        headers = {"apikey": self.st_key}
        url = f"https://api.securitytrails.com/v1/ips/nearby/{ip_address}"
        resp = requests.get(url, headers=headers, timeout=30)
        if resp.status_code == 200:
            blocks = resp.json().get("blocks", [])
            domains = []
            for block in blocks:
                for site in block.get("sites", []):
                    domains.append(site)
            print(f"[+] Reverse IP for {ip_address}: {len(domains)} domains")
            return domains
        return []

    def whois_lookup(self, domain):
        """Get WHOIS registration data for pivoting."""
        headers = {"apikey": self.st_key}
        url = f"https://api.securitytrails.com/v1/domain/{domain}/whois"
        resp = requests.get(url, headers=headers, timeout=30)
        if resp.status_code == 200:
            data = resp.json()
            whois_data = {
                "domain": domain,
                "registrar": data.get("registrar", ""),
                "registrant_org": data.get("registrant_org", ""),
                "registrant_email": data.get("registrant_email", ""),
                "name_servers": data.get("nameServers", []),
                "created_date": data.get("createdDate", ""),
                "updated_date": data.get("updatedDate", ""),
                "expires_date": data.get("expiresDate", ""),
            }
            return whois_data
        return {}

    def pivot_from_seed(self, seed_indicator, indicator_type="domain", depth=2):
        """Recursively pivot from a seed indicator to discover infrastructure."""
        discovered = {"domains": set(), "ips": set(), "relationships": []}

        if indicator_type == "domain":
            discovered["domains"].add(seed_indicator)
            # Get IPs for domain
            pdns = self.passive_dns_lookup(seed_indicator)
            for record in pdns:
                ip = record["ip"]
                discovered["ips"].add(ip)
                discovered["relationships"].append({
                    "source": seed_indicator, "target": ip,
                    "type": "resolves_to",
                    "first_seen": record["first_seen"],
                    "last_seen": record["last_seen"],
                })

                if depth > 1:
                    # Reverse lookup on discovered IPs
                    reverse_domains = self.reverse_ip_lookup(ip)
                    for rd in reverse_domains[:20]:
                        discovered["domains"].add(rd)
                        discovered["relationships"].append({
                            "source": rd, "target": ip,
                            "type": "hosted_on",
                        })

        elif indicator_type == "ip":
            discovered["ips"].add(seed_indicator)
            domains = self.reverse_ip_lookup(seed_indicator)
            for domain in domains[:20]:
                discovered["domains"].add(domain)
                discovered["relationships"].append({
                    "source": domain, "target": seed_indicator,
                    "type": "hosted_on",
                })

        print(f"[+] Pivot from {seed_indicator}: "
              f"{len(discovered['domains'])} domains, "
              f"{len(discovered['ips'])} IPs, "
              f"{len(discovered['relationships'])} relationships")
        return discovered

tracker = InfrastructureTracker(
    securitytrails_key="YOUR_ST_KEY",
    vt_key="YOUR_VT_KEY",
)
Step 2: Build Infrastructure Graph
python
import networkx as nx

class InfrastructureGraph:
    def __init__(self):
        self.graph = nx.Graph()

    def add_discovery(self, discovery_data):
        """Add discovered infrastructure to graph."""
        for domain in discovery_data["domains"]:
            self.graph.add_node(domain, type="domain")
        for ip in discovery_data["ips"]:
            self.graph.add_node(ip, type="ip")
        for rel in discovery_data["relationships"]:
            self.graph.add_edge(
                rel["source"], rel["target"],
                relationship=rel["type"],
                first_seen=rel.get("first_seen", ""),
                last_seen=rel.get("last_seen", ""),
            )

    def find_clusters(self):
        """Identify infrastructure clusters."""
        components = list(nx.connected_components(self.graph))
        clusters = []
        for component in components:
            domains = [n for n in component if self.graph.nodes[n].get("type") == "domain"]
            ips = [n for n in component if self.graph.nodes[n].get("type") == "ip"]
            clusters.append({
                "size": len(component),
                "domains": sorted(domains),
                "ips": sorted(ips),
                "domain_count": len(domains),
                "ip_count": len(ips),
            })
        clusters.sort(key=lambda x: x["size"], reverse=True)
        print(f"[+] Infrastructure clusters: {len(clusters)}")
        return clusters

    def find_hub_nodes(self, top_n=10):
        """Find high-centrality nodes (shared infrastructure)."""
        centrality = nx.degree_centrality(self.graph)
        top_nodes = sorted(centrality.items(), key=lambda x: x[1], reverse=True)[:top_n]
        hubs = []
        for node, score in top_nodes:
            hubs.append({
                "node": node,
                "type": self.graph.nodes[node].get("type", "unknown"),
                "centrality": round(score, 4),
                "connections": self.graph.degree(node),
            })
        return hubs

    def export_graph(self, output_file="infrastructure_graph.json"):
        data = nx.node_link_data(self.graph)
        with open(output_file, "w") as f:
            json.dump(data, f, indent=2)
        print(f"[+] Graph exported: {self.graph.number_of_nodes()} nodes, "
              f"{self.graph.number_of_edges()} edges")

infra_graph = InfrastructureGraph()
discovery = tracker.pivot_from_seed("evil-domain.com", depth=2)
infra_graph.add_discovery(discovery)
clusters = infra_graph.find_clusters()
hubs = infra_graph.find_hub_nodes()
infra_graph.export_graph()
Step 3: Monitor for New Infrastructure
python
import time

class InfrastructureMonitor:
    def __init__(self, tracker, known_indicators):
        self.tracker = tracker
        self.known = set(known_indicators)
        self.alerts = []

    def check_new_registrations(self, patterns):
        """Check for newly registered domains matching adversary patterns."""
        import re
        new_domains = []
        for pattern in patterns:
            # Query SecurityTrails for new domains matching pattern
            headers = {"apikey": self.tracker.st_key}
            url = "https://api.securitytrails.com/v1/domains/list"
            params = {"include_ips": "true", "page": 1}
            body = {"filter": {"keyword": pattern}}
            resp = requests.post(url, headers=headers, json=body, timeout=30)
            if resp.status_code == 200:
                records = resp.json().get("records", [])
                for record in records:
                    domain = record.get("hostname", "")
                    if domain not in self.known:
                        new_domains.append({
                            "domain": domain,
                            "pattern_matched": pattern,
                            "first_seen": datetime.now().isoformat(),
                        })
                        self.known.add(domain)

        if new_domains:
            print(f"[ALERT] {len(new_domains)} new domains matching patterns")
            self.alerts.extend(new_domains)
        return new_domains

    def generate_infrastructure_report(self, clusters, hubs):
        report = f"""# Adversary Infrastructure Tracking Report
Generated: {datetime.now().isoformat()}

## Summary
- Infrastructure clusters identified: {len(clusters)}
- Total domains tracked: {sum(c['domain_count'] for c in clusters)}
- Total IPs tracked: {sum(c['ip_count'] for c in clusters)}
- New domains detected: {len(self.alerts)}

## Top Infrastructure Hubs
| Node | Type | Connections | Centrality |
|------|------|-------------|------------|
"""
        for hub in hubs[:10]:
            report += (f"| {hub['node']} | {hub['type']} "
                       f"| {hub['connections']} | {hub['centrality']} |\n")

        report += "\n## Infrastructure Clusters\n"
        for i, cluster in enumerate(clusters[:5], 1):
            report += f"\n### Cluster {i} ({cluster['size']} nodes)\n"
            report += f"- Domains: {', '.join(cluster['domains'][:5])}\n"
            report += f"- IPs: {', '.join(cluster['ips'][:5])}\n"

        with open("infrastructure_report.md", "w") as f:
            f.write(report)
        print("[+] Infrastructure report saved")

monitor = InfrastructureMonitor(tracker, known_indicators=set())

Validation Criteria

  • Passive DNS queries return historical resolution data
  • Reverse IP lookups discover co-hosted domains
  • Infrastructure pivoting expands from seed indicators
  • Graph analysis identifies clusters and hub nodes
  • New infrastructure detected through pattern monitoring
  • Reports generated with actionable recommendations

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/building-adversary-infrastructure-tracking-system 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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Works with

Categories

Questions about Building Adversary Infrastructure Tracking System

What does Building Adversary Infrastructure Tracking System do?

Build an automated adversary infrastructure tracking system in Python (dnspython, python-whois, shodan, networkx) that pivots across passive DNS, certificate transparency logs, WHOIS records, and IP…. Building Adversary Infrastructure Tracking System is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Build an automated adversary infrastructure tracking system in Python (dnspython, python-whois, shodan, networkx) that pivots across passive DNS, certificate transparency logs, WHOIS records, and IP enrichment to map threat-actor C2 networks and flag newly registered domains matching known patterns.

When should I use Building Adversary Infrastructure Tracking System?

Building Adversary Infrastructure Tracking System fits situations like: pivoting from known indicators to discover related C2 infrastructure; maintaining a continuously updated map of a threat actors network.

How do I install Building Adversary Infrastructure Tracking System in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-adversary-infrastructure-tracking-system -a claude-code`. Or copy the skill folder (skills/building-adversary-infrastructure-tracking-system in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/building-adversary-infrastructure-tracking-system in your project. Claude Code loads it when a task matches its description.

How do I install Building Adversary Infrastructure Tracking System in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-adversary-infrastructure-tracking-system -a codex`. Or copy the skill folder (skills/building-adversary-infrastructure-tracking-system in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/building-adversary-infrastructure-tracking-system in your project. Codex loads it when a task matches its description.

Can I use Building Adversary Infrastructure Tracking System 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 building-adversary-infrastructure-tracking-system -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/building-adversary-infrastructure-tracking-system, .gemini/skills/building-adversary-infrastructure-tracking-system, .github/skills/building-adversary-infrastructure-tracking-system and .opencode/skills/building-adversary-infrastructure-tracking-system in your project.

What does Building Adversary Infrastructure Tracking System need to run?

Going by SKILL.md and its folder, Building Adversary Infrastructure Tracking System needs Python for the scripts in its folder. Our summary lists: Python 3; A credential in YOUR_ST_KEY; A credential in YOUR_VT_KEY.

Does Building Adversary Infrastructure Tracking System access the network?

SKILL.md names 7 domains. In commands or code: api.securitytrails.com; the agent is likely to contact it when it follows the instructions. As links in the text: blogs.juniper.net, censys.com, embeeresearch.io, validin.com, securitytrails.com and hunt.io. This is read from the text; nothing was executed.

Is Building Adversary Infrastructure Tracking System 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 Building Adversary Infrastructure Tracking System use?

Building Adversary Infrastructure Tracking System 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 Building Adversary Infrastructure Tracking System use?

About 3.7k tokens (SKILL.md is roughly 15k 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 376 tokens, read only when the agent opens those files.

What are the alternatives to Building Adversary Infrastructure Tracking System?

Skills that share tags, products or a category with Building Adversary Infrastructure Tracking System: Osint Investigation (johnson7788/MultiUserClaw, 327 stars), Domain Intel (Tommy-yw/RunbookHermes, 546 stars), Flowsint Enricher Builder (reconurge/flowsint, 9.6k stars) and Scientific Schematics (jimmc414/Kosmos, 595 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Building Adversary Infrastructure Tracking System?

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.