osint-investigation

Name: osint-investigation
Availability: InStock
Author: Shea

Assemble public-record OSINT evidence chains from diverse sources into structured findings.

78|16|Updated Apr 23, 2026

Search Tags:#investigation #osint #evidence-chain #entity-resolution #public-records #cross-source

Ready to run

npx skills add https://github.com/sheawinkler/hermes-agent-ultra --skill osint-investigation-sheawinkler

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: osint-investigation
Source: https://github.com/sheawinkler/hermes-agent-ultra/tree/main/optional-skills/research/osint-investigation
Command: npx skills add https://github.com/sheawinkler/hermes-agent-ultra --skill osint-investigation-sheawinkler

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Public-record OSINT investigations often require pulling data from many disparate sources, normalizing identities, and linking evidence into a coherent narrative.

Core Features & Use Cases

Cross-source data collection from sources like SEC EDGAR, USAspending, CourtListener, NYC ACRIS, OFAC SDN, OpenCorporates, ICIJ Offshore Leaks, and Wikipedia/Wikidata.
Entity resolution across sources with explicit confidence levels and construction of evidence chains that map sources to claims.
Evidence-building workflow to produce findings.json with cross_links.csv and timing.json inputs for narrative reporting or investigative journalism.

Quick Start

Fetch source data with the provided scripts, then run the entity-resolution workflow and build the findings.json evidence chain.

Frequently Asked Questions about osint-investigation

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

FAQPage Schema

How do I build an evidence chain from public records across multiple sources?▼

Cross-source OSINT investigations assemble data from sources like SEC EDGAR and OpenCorporates, normalize entity names, and map sources to claims with confidence levels to produce a structured findings.json file.

What is entity resolution in OSINT investigations?▼

Entity resolution in OSINT investigations normalizes identities across disparate public records and links them with explicit confidence levels, mapping diverse source data into a coherent narrative for due diligence.

Can I use Python stdlib scripts to pull data from SEC EDGAR and CourtListener?▼

Yes, provided stdlib Python scripts fetch source data from SEC EDGAR, CourtListener, and other public records. After fetching, you run the entity-resolution workflow to build the structured evidence chain.

What is the best way to cross-link OFAC SDN data with ICIJ Offshore Leaks?▼

The best way to cross-link OFAC SDN data with ICIJ Offshore Leaks uses a cross-source resolution workflow that normalizes entity names and constructs evidence chains mapping sources to claims for verifiable reporting.

Does this OSINT workflow produce structured outputs for investigative journalism?▼

Yes, the OSINT workflow produces structured outputs including a findings.json file, cross_links.csv, and timing.json. These files provide provenance guarantees and timing analysis for narrative reporting and investigative journalism.