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group-risk-analysis

Analyze phone number groups for risk indicators and relationship patterns.

Updated Jun 2, 2026
Search Tags:#data aggregation#risk analysis#phone network#phone number analysis#group profiling
One-click install
npx skills add https://github.com/bettercallfan/deerflow --skill group-risk-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: group-risk-analysis
Source: https://github.com/bettercallfan/deerflow/tree/main/skills/custom/phone-network-analysis/group-risk-analysis
Command: npx skills add https://github.com/bettercallfan/deerflow --skill group-risk-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires duckdb, pandas, json, math, and includes scripts (resource) components.

What problem does it solve?

This Skill analyzes groups of phone numbers for risk indicators, such as high call volume, abnormal behavior patterns, shared devices, and common contacts. It is suitable for identifying potential risks in phone networks and conducting detailed group profiling.

Core Features & Use Cases

  • Group-Level Risk Analysis: Analyze the risk of a group of phone numbers collectively.
  • Risk Patterns Identification: Identify patterns such as high call volume, night-time abnormal activity, broad contact network, and shared devices.
  • Evidence Aggregation: Aggregate evidence of shared devices, internal calls, and common contacts into a relationship matrix.
  • Use Case: Suppose you have a list of phone numbers that you suspect may be involved in fraudulent activity. Use this Skill to analyze the group's risk, identify key members, and gather evidence.

Quick Start

Run the 'group-risk-analysis' skill with the phone numbers '1234567890,0987654321'.

Frequently Asked Questions about group-risk-analysis

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

FAQPage Schema
How do I conduct risk analysis on a group of phone numbers?▼

Risk analysis on a group of phone numbers identifies risk indicators by evaluating call records, shared devices, and contact networks to provide comprehensive group profiling insights.

What risk patterns can be identified through phone network analysis?▼

Phone network analysis identifies risk patterns including high call volume, night-time abnormal activity, broad contact networks, and shared devices across the analyzed phone numbers.

How do I aggregate evidence of shared devices and common contacts for suspected fraudulent phone numbers?▼

Evidence aggregation combines shared devices, internal calls, and common contacts into a relationship matrix to highlight connections and key members within a potentially fraudulent group.

Can I use pandas and duckdb for phone number data aggregation and profiling?▼

Yes, data aggregation and group profiling for phone networks require duckdb and pandas libraries to process call records, filter shared devices, and visualize risk indicators.

Does group risk analysis work for identifying key members in a phone network?▼

Group risk analysis pinpoints key members within a phone network by aggregating relationship evidence and analyzing abnormal behavior patterns from call records and shared devices.

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