Osint Investigation
johnson7788/MultiUserClaw
Public-records OSINT investigation framework — SEC EDGAR filings, USAspending contracts, Senate lobbying, OFAC sanctions, ICIJ offshore leaks, NYC property records (ACRIS), OpenCorporates…
Agent skill
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…
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-adversary-infrastructure-tracking-system -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills building-adversary-infrastructure-tracking-system --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "building-adversary-infrastructure-tracking-system" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/building-adversary-infrastructure-tracking-system into .claude/skills/building-adversary-infrastructure-tracking-system/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-adversary-infrastructure-tracking-system", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/building-adversary-infrastructure-tracking-systemType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-adversary-infrastructure-tracking-system -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills building-adversary-infrastructure-tracking-system --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/building-adversary-infrastructure-tracking-system .agents/skills/building-adversary-infrastructure-tracking-system && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "building-adversary-infrastructure-tracking-system" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/building-adversary-infrastructure-tracking-system into .agents/skills/building-adversary-infrastructure-tracking-system/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-adversary-infrastructure-tracking-system", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-adversary-infrastructure-tracking-system -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills building-adversary-infrastructure-tracking-system --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/building-adversary-infrastructure-tracking-system .cursor/skills/building-adversary-infrastructure-tracking-system && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "building-adversary-infrastructure-tracking-system" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/building-adversary-infrastructure-tracking-system into .cursor/skills/building-adversary-infrastructure-tracking-system/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-adversary-infrastructure-tracking-system", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git --path skills/building-adversary-infrastructure-tracking-system--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-adversary-infrastructure-tracking-system -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills building-adversary-infrastructure-tracking-system --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/building-adversary-infrastructure-tracking-system .gemini/skills/building-adversary-infrastructure-tracking-system && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "building-adversary-infrastructure-tracking-system" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/building-adversary-infrastructure-tracking-system into .gemini/skills/building-adversary-infrastructure-tracking-system/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-adversary-infrastructure-tracking-system", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills building-adversary-infrastructure-tracking-systemInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-adversary-infrastructure-tracking-system -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/building-adversary-infrastructure-tracking-system .github/skills/building-adversary-infrastructure-tracking-system && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "building-adversary-infrastructure-tracking-system" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/building-adversary-infrastructure-tracking-system into .github/skills/building-adversary-infrastructure-tracking-system/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-adversary-infrastructure-tracking-system", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-adversary-infrastructure-tracking-system -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills building-adversary-infrastructure-tracking-system --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/building-adversary-infrastructure-tracking-system .opencode/skills/building-adversary-infrastructure-tracking-system && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "building-adversary-infrastructure-tracking-system" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/building-adversary-infrastructure-tracking-system into .opencode/skills/building-adversary-infrastructure-tracking-system/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-adversary-infrastructure-tracking-system", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
building-adversary-infrastructure-tracking-systemBuild 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. 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.
3 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.
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.
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.
Hosts in commands or code, which the agent is likely to contact:
api.securitytrails.comAlso links to:
blogs.juniper.netcensys.comembeeresearch.iovalidin.comsecuritytrails.comhunt.ioFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 390 words, ~3,710 tokens.
.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.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.
requests, dnspython, python-whois, shodan, networkx librariesPassive 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.
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).
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.
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",
)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()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())© 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
SKILL.md and 3 other files (scripts, references) in skills/building-adversary-infrastructure-tracking-system of mukul975/Anthropic-Cybersecurity-Skills.
Open the folder on GitHubat commit 54a7988
Building Adversary Infrastructure Tracking System next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Building Adversary Infrastructure Tracking System this skillmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~3.7k | Automated safety check: Pass | Apache-2.0 | |
| Osint Investigationjohnson7788/MultiUserClaw | 327 | — | ~3k | Automated safety check: Pass | MIT | |
| Domain IntelTommy-yw/RunbookHermes | 546 | 1 repos | ~1.1k | Automated safety check: Pass | MIT | |
| Flowsint Enricher Builderreconurge/flowsint | 9.6k | — | ~2.6k | Automated safety check: Pass | Apache-2.0 | |
| Scientific Schematicsjimmc414/Kosmos | 595 | — | ~16k | Automated safety check: Notes | None | |
| Security Auditoreigent-ai/eigent | 15k | — | ~1.8k | Automated safety check: Notes | Apache-2.0 |
johnson7788/MultiUserClaw
Public-records OSINT investigation framework — SEC EDGAR filings, USAspending contracts, Senate lobbying, OFAC sanctions, ICIJ offshore leaks, NYC property records (ACRIS), OpenCorporates…
Tommy-yw/RunbookHermes
Passive domain reconnaissance using Python stdlib. An agent skill from Tommy-yw/RunbookHermes.
reconurge/flowsint
Guides building Flowsint enrichers and types: where definitions live, how the base class and vault work, and when a new type is warranted.
jimmc414/Kosmos
Create publication-quality scientific diagrams, flowcharts, and schematics using Python (graphviz, matplotlib, schemdraw, networkx).
eigent-ai/eigent
Audits source code, dependencies and config files for vulnerabilities and hardcoded secrets, using two bundled Python scanners and an OWASP Top 10 checklist.
zLanqing/codex-claude-academic-skills
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python.
mukul975/Anthropic-Cybersecurity-Skills
Weighs infrastructure, TTP, malware code and timing evidence with the Diamond Model and competing hypotheses to reach a confidence-rated attribution.
mukul975/Anthropic-Cybersecurity-Skills
Walks through reverse engineering Go-compiled malware in Ghidra: parsing buildinfo and pclntab, recovering stripped function names and extracting dependencies.
mukul975/Anthropic-Cybersecurity-Skills
Guides forensic analysis of Windows LNK shortcut files and Jump Lists with LECmd, JLECmd and manual parsing to show file access and program execution.
mukul975/Anthropic-Cybersecurity-Skills
Hunts Windows malware persistence with Sysinternals Autoruns, covering run keys, services, scheduled tasks and drivers, with baseline comparison.
mukul975/Anthropic-Cybersecurity-Skills
Guides a Windows forensic examination of the NTFS Master File Table to recover deleted-file evidence, build timelines and spot timestomping.
mukul975/Anthropic-Cybersecurity-Skills
Detects DNS tunneling, ICMP exfiltration and HTTP-based covert channels in packet captures and DNS logs when hunting for hidden command-and-control traffic.
Categories
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.