osint-investigation

Cross-reference public records to analyze corporate relationships with confidence scoring.

Updated Jun 17, 2026

Search Tags:#investigation #data-analysis #osint #sanctions #due-diligence #public-records

Ready to run

npx skills add https://github.com/cxnaive/hermes-agent-llbot --skill osint-investigation-cxnaive

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill solves the challenge of manually cross-referencing fragmented public records to identify corporate relationships, financial ties, and litigation history.

Core Features & Use Cases

Multi-Source Investigation: Aggregates data from SEC filings, federal contracts, lobbying disclosures, sanctions lists, and court records.
Entity Resolution: Normalizes and links disparate entity names across heterogeneous datasets using tiered confidence levels.
Statistical Analysis: Performs timing correlation tests to identify suspicious patterns between events like lobbying filings and contract awards.

Quick Start

Use the osint-investigation skill to research the corporate and litigation history of a specific entity by running the provided fetch scripts for the relevant data sources.

Frequently Asked Questions about osint-investigation

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

FAQPage Schema

How do I cross-reference public records and sanctions lists for corporate due diligence?▼

Cross-referencing public records for due diligence involves aggregating SEC filings, federal contracts, and sanctions lists, then normalizing entity names across heterogeneous datasets to identify corporate relationships with tiered confidence levels.

What is entity resolution in open source intelligence investigations?▼

Entity resolution in OSINT investigations is the process of normalizing and linking disparate entity names across heterogeneous datasets like court records and corporate filings to accurately map financial ties and corporate relationships.

Can I use Python standard library to analyze government contracts and lobbying disclosures?▼

Yes, you can use Python standard library execution to normalize, link, and analyze entity relationships across government contracts and lobbying disclosures, applying timing correlation tests to identify suspicious patterns.

How does statistical timing correlation work for financial monitoring and litigation history?▼

Statistical timing correlation for financial monitoring performs tests to identify suspicious patterns between events like lobbying filings and contract awards, constructing an evidence-chain across global datasets with explicit confidence scoring.

What are the limitations of manual cross-referencing fragmented public records?▼

Manually cross-referencing fragmented public records limits the ability to accurately identify corporate relationships and financial ties across global datasets, requiring automated entity resolution and statistical analysis to construct reliable evidence-chains.

Does osint-investigation work with global datasets for evidence-chain construction?▼

Yes, osint-investigation works with global datasets for evidence-chain construction by cross-referencing public records including corporate filings, sanctions, and government contracts to link entity relationships with explicit confidence scoring.