external/anthropic-cybersecurity-skills/skills/performing-open-source-intelligence-gathering/SKILL.md
Open Source Intelligence (OSINT) gathering is the first active phase of a red team engagement, where operators collect publicly available information about the target organization to identify attack s
npx skillsauth add seikaikyo/dash-skills performing-open-source-intelligence-gatheringInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Legal Notice: This skill is for authorized security testing and educational purposes only. Unauthorized use against systems you do not own or have written permission to test is illegal and may violate computer fraud laws.
Open Source Intelligence (OSINT) gathering is the first active phase of a red team engagement, where operators collect publicly available information about the target organization to identify attack surfaces, potential targets for social engineering, technology stacks, and credential exposures. Effective OSINT directly shapes initial access strategies and reduces operational risk.
| Category | Sources | Value | |----------|---------|-------| | Domain Intelligence | DNS records, WHOIS, CT logs, subdomain enumeration | Network attack surface | | Personnel Intelligence | LinkedIn, social media, conference talks, publications | Social engineering targets | | Credential Intelligence | Breach databases, paste sites, GitHub leaks | Valid credential discovery | | Technology Intelligence | Job postings, Wappalyzer, Shodan, Censys | Vulnerability identification | | Physical Intelligence | Google Maps, social media photos, Glassdoor | Physical access planning | | Document Intelligence | SEC filings, public documents, metadata extraction | Organizational structure |
| Tool | Purpose | Type | |------|---------|------| | Amass | Subdomain enumeration and network mapping | Open Source | | Subfinder | Passive subdomain discovery | Open Source | | theHarvester | Email, subdomain, and name harvesting | Open Source | | Maltego | Visual link analysis and data correlation | Commercial | | SpiderFoot | Automated OSINT collection | Open Source | | Shodan | Internet-connected device search | Commercial | | Censys | Internet asset discovery | Commercial | | Recon-ng | Web reconnaissance framework | Open Source | | GitDorker | GitHub secret scanning | Open Source | | Photon | Web crawler for OSINT | Open Source |
tools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.