building-threat-actor-profile-from-osint
Lets your agent gather information about hacker groups. It helps understand who they are, what they do, and how they act, using public data.
What it does
This tool collects and analyzes public information about cyber threat groups. It gathers data from security reports, dark web forums, and social media to build detailed profiles of these groups.
When to use it
- ✓When investigating an unknown hacking group.
- ✓When trying to understand an adversary's tactics.
- ✓When creating a report on threat activities.
Example of what to ask your AI
“Build a profile for the threat group 'APT29' using publicly available information.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool searches for information across various online sources like security reports and forums. It then organizes this data to describe who the attackers are, what they do, and how they operate.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-threat-actor-profile-from-osintOr download just the file:
curl -L https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/building-threat-actor-profile-from-osint/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Building Threat Actor Profile from OSINT
Overview
Threat actor profiling using OSINT systematically gathers and analyzes publicly available information to build comprehensive profiles of adversary groups. This skill covers collecting intelligence from public sources (security vendor reports, paste sites, dark web forums, social media, code repositories), correlating indicators across platforms, mapping adversary infrastructure using tools like Maltego and SpiderFoot, and producing structured threat actor dossiers that inform defensive strategies and attribution assessments.
When to Use
- When deploying or configuring building threat actor profile from osint 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
shodan,requests,beautifulsoup4,maltego-trx,stix2libraries - SpiderFoot (https://github.com/smicallef/spiderfoot) or SpiderFoot HX
- Maltego CE or Maltego XL for link analysis
- API keys: Shodan, VirusTotal, AlienVault OTX, PassiveTotal/RiskIQ
- MITRE ATT&CK knowledge for TTP mapping
- Understanding of STIX 2.1 Intrusion Set, Threat Actor, and Identity SDOs
Key Concepts
OSINT Sources for Threat Actor Profiling
Primary intelligence sources include vendor threat reports (Mandiant, CrowdStrike, Recorded Future, Talos), government advisories (CISA, NSA, FBI joint advisories), academic research papers, malware repositories (VirusTotal, MalwareBazaar, Malpedia), paste sites (Pastebin, GitHub Gists), code repositories, social media accounts, dark web forums, and certificate transparency logs.
Structured Analytical Techniques
Profiling uses the Diamond Model (adversary, infrastructure, capability, victim), Analysis of Competing Hypotheses (ACH) for attribution confidence, and MITRE ATT&CK mapping for TTP documentation. Link analysis tools like Maltego visualize relationships between indicators, infrastructure, and actors.
Profile Components
A complete threat actor profile includes: aliases and naming conventions across vendors, suspected origin and sponsorship, motivation (espionage, financial, hacktivism, disruption), targeted sectors and geographies, known campaigns and operations, TTPs mapped to ATT&CK, toolset and malware families, infrastructure patterns, and historical timeline.
Workflow
Step 1: Collect Intelligence from Multiple Sources
import requests
import json
from datetime import datetime
class OSINTCollector:
def __init__(self, vt_key=None, otx_key=None, shodan_key=None):
self.vt_key = vt_key
self.otx_key = otx_key
self.shodan_key = shodan_key
self.collected_data = {"sources": [], "indicators": [], "reports": []}
def search_alienvault_otx(self, actor_name):
"""Search AlienVault OTX for threat actor pulses."""
headers = {"X-OTX-API-KEY": self.otx_key}
url = f"https://otx.alienvault.com/api/v1/search/pulses?q={actor_name}&limit=20"
resp = requests.get(url, headers=headers)
if resp.status_code == 200:
data = resp.json()
pulses = data.get("results", [])
for pulse in pulses:
self.collected_data["reports"].append({
"source": "AlienVault OTX",
"title": pulse.get("name", ""),
"created": pulse.get("created", ""),
"description": pulse.get("description", "")[:500],
"tags": pulse.get("tags", []),
"indicators_count": len(pulse.get("indicators", [])),
"pulse_id": pulse.get("id", ""),
})
for ioc in pulse.get("indicators", []):
self.collected_data["indicators"].append({
"type": ioc.get("type", ""),
"value": ioc.get("indicator", ""),
"source": "OTX",
"pulse": pulse.get("name", ""),
})
print(f"[+] OTX: Found {len(pulses)} pulses for '{actor_name}'")
return self.collected_data
def search_virustotal_collections(self, actor_name):
"""Search VirusTotal for threat actor collections."""
headers = {"x-apikey": self.vt_key}
…
FAQ
What are 'indicators'?
Indicators are clues or signals that point to malicious activity, such as IP addresses, domain names, or specific files.
What are 'TTPs'?
TTPs stands for Tactics, Techniques, and Procedures. They describe the methods attackers use to carry out their attacks.
What is 'OSINT'?
OSINT stands for Open-Source Intelligence, which is intelligence gathered from publicly available sources like news, reports, and social media.
Details
Original description by the author
Build threat actor profiles by collecting OSINT from vendor reports, paste sites, dark web forums, social media, and code repos, correlating indicators, mapping adversary infrastructure with tools like Maltego and SpiderFoot, and producing structured dossiers of motivations, capabilities, infrastructure, and TTPs. Use when performing attribution or building an adversary dossier from open-source in
Popularity
- GitHub stars
- 35k
- Ranking position
- #5552
- License
- Apache-2.0
- Updated
- 8/31/2026
Comments
- …
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