predictive-attrition-modeling

Name: predictive-attrition-modeling
Availability: InStock
Author: rancapoly

Predict voluntary attrition probabilities with confidence intervals and SHAP-style factor attribution.

Updated Apr 21, 2026

Search Tags:#HR analytics #confidence intervals #retention strategy #SHAP explainability #attrition prediction #probability estimation

Ready to run

npx skills add https://github.com/rancapoly/vault --skill predictive-attrition-modeling

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: predictive-attrition-modeling
Source: https://github.com/rancapoly/vault/tree/main/p2-w4-predictive-attrition-modeler
Command: npx skills add https://github.com/rancapoly/vault --skill predictive-attrition-modeling

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides predictive attrition modeling capabilities, enabling organizations to forecast voluntary attrition across various scopes and take proactive retention measures.

Core Features & Use Cases

Multi-Scope Probability Estimation: Compute attrition probabilities at ORGANIZATION, BU, COHORT, and INDIVIDUAL_CONSENTED granularity.
Dual Confidence-Interval Reporting: Provide both 80% and 95% confidence intervals for every prediction.
Risk Tier Classification: Classify probabilities into LOW, MEDIUM, HIGH, or CRITICAL tiers.
Cohort Distribution Decomposition: Report distribution percentages for aggregated scopes.
SHAP-Style Factor Attribution: Explain predictions with ranked factors and signed contributions.
Human-Interpretable Translation: Translate technical factors into HRBP-readable language.
Permitted-Feature Discipline: Ingest only permitted feature categories.
Protected-Attribute Architectural Block: Block ingestion of protected attributes.
Consent Token Validation: Validate consent tokens for individual predictions.
Bias Audit Status Verification: Ensure bias audit currency before delivery.
P2 Guardrail Submission: Submit outputs to the Ethical Guardrail Sentinel.
Retention Action Mapping: Route actions based on probability and factors.
Workforce Planning Feed: Push predictions to the Workforce Forecaster.
Individual Delivery Discipline: Route individual predictions to the ESS portal or HRBP with consent.
Quarterly Retraining Orchestration: Trigger quarterly evaluations.
Model Versioning Discipline: Tag predictions with immutable model versions.
Append-Only Audit Logging: Log full prediction traces.
Use Case: Utilize this Skill to identify high-risk employee cohorts and implement targeted retention strategies.

Quick Start

Use the predictive-attrition-modeling skill to generate attrition forecasts for the next 12 months for the ORGANIZATION scope.

Frequently Asked Questions about predictive-attrition-modeling

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

FAQPage Schema

How does SHAP explainability work for attrition prediction?▼

Attrition prediction models estimate voluntary turnover probability across multiple organizational scopes, from entire departments to individual consented employees. They use machine learning algorithms to generate calibrated confidence intervals and classify risk tiers.

How do I forecast voluntary attrition risk for a specific employee cohort?▼

You forecast voluntary attrition risk by computing multi-scope probability estimations at the COHORT level and generating distribution decompositions. The model applies permitted features only and tags every prediction with an immutable model version.

Can I use attrition prediction models for individual workforce planning without consent?▼

No, you cannot generate individual attrition predictions without validating consent tokens. The system enforces a strict consent token validation protocol and blocks protected attribute ingestion before routing individual predictions to the ESS portal or HRBP.

What is the best way to classify employee retention risk tiers using HR analytics?▼

The best way to classify retention risk tiers is to map calibrated attrition probabilities into LOW, MEDIUM, HIGH, or CRITICAL categories. This classification supports targeted retention strategies and routes actions based on probability and factor attribution.

What are the limitations of using probability estimation for retention strategy?▼

Limitations include strict adherence to permitted-feature ingestion and a protected-attribute architectural block. The model requires active bias audit status verification, quarterly retraining orchestration, and submission to the Ethical Guardrail Sentinel before delivery.