topn-high-risk-discovery

Rank high-risk phone numbers using graph analysis and risk scoring.

Updated Jun 2, 2026

Search Tags:#security analysis #risk scoring #risk discovery #telecommunications #phone network

Ready to run

npx skills add https://github.com/bettercallfan/deerflow --skill topn-high-risk-discovery

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: topn-high-risk-discovery
Source: https://github.com/bettercallfan/deerflow/tree/main/skills/custom/phone-network-analysis/topn-high-risk-discovery
Command: npx skills add https://github.com/bettercallfan/deerflow --skill topn-high-risk-discovery

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, networkx, numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of discovering and ranking high-risk phone numbers in a network, saving time and effort in security analysis.

Core Features & Use Cases

Automatic Risk Discovery: Automatically discovers high-risk phone numbers based on a predefined risk scoring model.
Ranking and Reporting: Generates a TopN ranking of high-risk numbers, along with detailed reports.
Use Case: When analyzing large-scale phone network data, use this Skill to quickly identify the most suspicious numbers and prioritize them for further investigation.

Quick Start

Use the topn-high-risk-discovery skill to identify the top 20 most risky phone numbers in your dataset.

Frequently Asked Questions about topn-high-risk-discovery

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

FAQPage Schema

How do I identify high-risk phone numbers in a large telecommunications network?▼

To identify high-risk phone numbers in a large telecommunications network, you can use graph analysis and risk scoring to automatically discover and rank suspicious numbers for fraud detection.

What is the best way to rank suspicious phone numbers for security analysis?▼

The best way to rank suspicious phone numbers for security analysis is applying a predefined risk scoring model that generates a TopN list, prioritizing the most risky numbers for further investigation.

Can I use pandas and networkx to discover high-risk numbers in phone network data?▼

Yes, you can use pandas and networkx to process phone network data and perform graph analysis to discover high-risk numbers, leveraging numpy for the underlying risk scoring calculations.

How do I generate a Top 20 ranking of risky phone numbers from my dataset?▼

To generate a Top 20 ranking of risky phone numbers from your dataset, you apply the risk discovery skill to your phone network data, which outputs a prioritized list along with detailed reports.

Does automated risk discovery work for fraud detection in telecommunications?▼

Automated risk discovery works for fraud detection in telecommunications by analyzing large-scale phone network graphs to quickly identify and rank the most suspicious numbers requiring investigation.