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osint-investigation

Resolve public-records investigation questions across heterogeneous OSINT sources.

Updated May 14, 2026
Search Tags:#investigation#osint#sanctions#entity-resolution#public-records#evidence-chains#corporate-filings
One-click install
npx skills add https://github.com/SethyPagna/Secretary-Jarvis --skill osint-investigation-sethypagna
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: osint-investigation
Source: https://github.com/SethyPagna/Secretary-Jarvis/tree/main/src/capabilities/optional-skills/research/osint-investigation
Command: npx skills add https://github.com/SethyPagna/Secretary-Jarvis --skill osint-investigation-sethypagna

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill turns messy public-records research into a repeatable investigation workflow, helping you connect people, companies, contracts, sanctions, property, litigation, archives, and news without losing source traceability.

Core Features & Use Cases

  • Public-records coverage: Search and normalize data from SEC EDGAR, USAspending, Senate lobbying, OFAC SDN, ICIJ Offshore Leaks, NYC ACRIS, OpenCorporates, CourtListener, Wayback, Wikipedia/Wikidata, and GDELT.
  • Entity resolution: Match names across sources with exact, fuzzy, and token-overlap confidence tiers so similar records become usable leads instead of guesswork.
  • Evidence chains: Produce structured findings with row-level citations, confidence levels, and cross-links that can be audited later.
  • Use cases: Due diligence, sanctions screening, follow-the-money research, litigation backgrounding, property tracing, and recovering historical context from archived or deleted pages.

Quick Start

Ask the skill to investigate a named person or organization and return the relevant public records, cross-source matches, and an evidence-backed summary.

Frequently Asked Questions about osint-investigation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I connect a person to shell companies and public records across different OSINT sources?▼

OSINT investigation resolves public-records questions by linking people, companies, filings, and sanctions across heterogeneous sources. It uses confidence-scored entity resolution to normalize data like SEC EDGAR and ICIJ Offshore Leaks into auditable evidence chains.

What's the best way to screen individuals against OFAC SDN and other sanctions lists?▼

Sanctions screening links named individuals or organizations to OFAC SDN records. The workflow applies confidence-scored matching across public records, producing structured findings with row-level citations and source-level traceability for compliance.

How do I trace corporate filings and government contracts for follow-the-money analysis?▼

Follow-the-money analysis connects corporate filings from SEC EDGAR to USAspending contracts and Senate lobbying records. The process normalizes these heterogeneous public records into CSV outputs, applying timing correlation to build structured evidence chains.

Can I recover deleted web pages and historical context for litigation backgrounding?▼

Historical web recovery uses archived sources like Wayback to retrieve deleted web pages. For litigation backgrounding, it correlates archived context with CourtListener records to produce confidence-scored findings with source-level traceability.

Do I need external APIs to extract property records and corporate intelligence?▼

No external APIs are needed; the skill requires stdlib-only acquisition scripts to extract property records like NYC ACRIS and corporate intelligence from OpenCorporates. It outputs normalized CSV files with confidence-scored entity resolution.

How does entity resolution handle fuzzy name matches across investigation data?▼

Entity resolution matches names across public records using exact, fuzzy, and token-overlap confidence tiers. This prevents similar records from becoming guesswork, transforming them into usable leads with row-level citations and evidence-chain findings.

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