Skip to main content

referral-abuse-detection-v1

Detects referral abuse violations by investigating accounts, analyzing traffic patterns, and scoring violation indicators to determine enforcement action.

Ir a la instalación

Datos de origen

Repositorio
vivekhaldar/proceda
Última actividad en el origen
22 de marzo de 2026 a las 20:38
Idioma detectado de SKILL.md
inglés
Estrellas
16
Forks
2

Opciones de instalación

De forma predeterminada está seleccionado el prompt que primero revisa el origen. Puedes cambiar a un comando directo o descargar una copia local.

Revisa los archivos de origen

Lee SKILL.md y los archivos complementarios que muestra SkillsMP antes de decidir si quieres instalarlo.

Explorador de archivos
2 archivos

Mostrando SKILL.md

SKILL.md
Instrucciones de origen · Vista previa de solo lectura
name
referral-abuse-detection-v1
description
Detects referral abuse violations by investigating accounts, analyzing traffic patterns, and scoring violation indicators to determine enforcement action.
required_tools
["investigate_account","analyze_traffic_patterns","determine_enforcement_action"]
output_fields
["enforcement_action"]
### Step 1: Investigate Account Call `investigate_account` with `account_id`. Record all returned fields: `address_validity`, `email_pattern_suspicious`, `website_verified`, `business_description`, `account_status`, `connected_accounts`, and `login_geographic_consistency`. ### Step 2: Analyze Traffic Patterns Call `analyze_traffic_patterns` with `account_id`. Record all returned fields: `revenue_amount`, `click_through_rate`, `page_views`, `device_distribution`, `referral_source_quality`, `payment_method_shared`, and `order_patterns_suspicious`. ### Step 3: Get Enforcement Guidelines Call `determine_enforcement_action` with `account_id` to retrieve enforcement action guidelines. ### Step 4: Calculate Violation Scores and Determine Enforcement Action Using the data from Steps 1 and 2, calculate four violation scores by counting how many indicators are TRUE for each category: **Abusive Account Creation Score** (threshold >= 3): - address_validity is false (+1) - email_pattern_suspicious is true (+1) - website_verified is false (+1) - connected_accounts >= 15 (+1) - login_geographic_consistency is false (+1) **Misleading Ad Copy Score** (threshold >= 3): - website_verified is false (+1) - referral_source_quality is "Low" or "Medium" (+1) - order_patterns_suspicious is true (+1) - click_through_rate > 0.4 (+1) **Personal Orders Score** (threshold >= 3): - payment_method_shared is true (+1) - connected_accounts > 0 AND connected_accounts < 15 (+1) - order_patterns_suspicious is true (+1) - referral_source_quality is "High" (+1) **No Violation Score** (threshold >= 4): - address_validity is true (+1) - email_pattern_suspicious is false (+1) - website_verified is true (+1) - login_geographic_consistency is true (+1) - payment_method_shared is false (+1) - order_patterns_suspicious is false (+1) Determine the violation type based on the HIGHEST score that meets its threshold. If multiple scores meet their thresholds, choose the highest score. If tied, use priority: Abusive Account Creation > Misleading Ad Copy > Personal Orders. If no score meets any threshold, the result is INCONCLUSIVE. Apply the enforcement action: - Abusive Account Creation → "Account Closure" - Misleading Ad Copy → "Account Closure" - Personal Orders (Related) → "No Action" - No Violation → "No Action" - INCONCLUSIVE → "Inconclusive"
Ver en GitHub