osint-investigation

Link entities across public-record OSINT sources into traceable evidence chains.

Updated May 11, 2026
One-click install
npx skills add https://github.com/jason660519/Project-Manager --skill osint-investigation-jason660519
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: osint-investigation
Source: https://github.com/jason660519/Project-Manager/tree/main/hermes-agent/optional-skills/research/osint-investigation
Command: npx skills add https://github.com/jason660519/Project-Manager --skill osint-investigation-jason660519

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Public-records OSINT investigations are slow and error-prone when data must be gathered from many disparate sources and re-conciled manually.

Core Features & Use Cases

  • Cross-source entity resolution across SEC EDGAR, USAspending, Senate LD, OFAC SDN, ICIJ Offshore Leaks, NYC ACRIS, OpenCorporates, CourtListener, Wayback Machine, Wikipedia + Wikidata, and GDELT.
  • Automated workflow with Python stdlib-only scripts that fetch, normalize, and link data, producing traceable evidence chains suitable for due-diligence and investigative research.
  • Use Case: perform a multi-source background check on a company or person, then surface an evidence chain that cites primary records.

Quick Start

Run the ingestion scripts to pull data from multiple public-record sources and build cross-link findings.

Frequently Asked Questions about osint-investigation

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

FAQPage Schema
How do I cross-link entities across public records for OSINT investigations?▼

You can cross-link entities by running Python stdlib-only scripts that fetch, normalize, and link data across public-record sources like SEC EDGAR, OFAC SDN, and ICIJ Offshore Leaks to build traceable evidence chains.

What is entity resolution in public-record OSINT data?▼

Entity resolution in public-record OSINT data is the process of identifying and linking identical entities across disparate sources like USAspending, Senate LD, and OpenCorporates to create a unified, auditable profile with traceable provenance.

Can I run OSINT investigations without external Python dependencies?▼

Yes, you can perform reproducible OSINT investigations using Python stdlib-only scripts that fetch and normalize records from sources like CourtListener, Wayback Machine, and GDELT without installing external packages.

How do I perform a multi-source background check using public records?▼

Perform a multi-source background check by running ingestion scripts to pull data from sources like NYC ACRIS, Wikipedia, and Wikidata, which automatically normalize and cross-link findings into standardized outputs citing primary records.

Does OSINT data normalization work with ICIJ Offshore Leaks and SEC EDGAR?▼

Yes, OSINT data normalization works with ICIJ Offshore Leaks and SEC EDGAR by standardizing raw fetched data into a uniform format, allowing scripts to accurately resolve and link entities across these disparate public-record domains.

What are the limitations of using Python stdlib-only for public-record investigations?▼

Using Python stdlib-only for public-record investigations means relying entirely on built-in libraries to fetch and parse data, which may require custom script adjustments for complex or non-standard API responses from sources like GDELT.