Osint Graph Analyzer

What This Skill Does

The OSINT Graph Analyzer, developed by orosha-ai, is a robust framework designed to ingest, process, and analyze Open Source Intelligence (OSINT) data through the power of graph theory. At its core, the skill utilizes Neo4j to store relational data as a knowledge graph, allowing users to map complex connections between entities such as accounts, persons, and organizations. By moving beyond traditional flat-file analysis, this tool enables the discovery of hidden patterns that would otherwise remain obscured. It leverages sophisticated algorithms like Louvain for community detection and PageRank for influence mapping, making it an essential utility for investigators, security researchers, and analysts who need to understand the structural dynamics of digital networks.

Installation

To integrate this skill into your OpenClaw environment, ensure you have a running Neo4j instance (v5.x or higher) and Python 3.9+. Run the installation via the OpenClaw CLI: clawhub install openclaw/skills/skills/orosha-ai/osint-graph-analyzer Following installation, ensure your environment variables point to your local Neo4j database credentials. You may need to install the required Python dependencies via pip install -r requirements.txt within the skill directory to ensure the driver communicates correctly with your database.

Use Cases

This skill is highly versatile, finding its primary application in:

• Threat Intelligence: Identifying central nodes within malicious actor networks and understanding the topology of command-and-control structures.
• Complex Investigations: Tracing indirect relationships between disparate entities to find links that human-led analysis might overlook.
• Counter-OSINT: Visualizing your own organizational footprint to understand how public data connects to sensitive internal information.
• Social Network Analysis: Mapping follower-influencer dynamics to detect bot clusters or coordinate influence operations across multiple platforms.

Example Prompts

1. "Analyze the imported CSV data for community clusters using the Louvain algorithm and provide a summary of the top three groups."
2. "Find the shortest path between '@user_a' and '@target_account' to see if there is a common link in our network graph."
3. "Identify the top 10 most influential entities in the network based on PageRank and export the final visualization to a JSON file."

Tips & Limitations

To get the best results, ensure your input data is cleaned for duplicate entities; proper entity resolution before ingestion is critical for graph accuracy. Note that the skill is highly memory-intensive when performing large-scale graph traversals; if analyzing networks with millions of nodes, ensure your Neo4j heap size is appropriately configured. The tool performs best with structured CSV or JSON inputs; raw API data might require preprocessing scripts to map correctly to the expected node-edge format. Always verify findings with secondary sources, as graph algorithms can sometimes detect noise as meaningful connections.