Employee Turnover Prediction

Employee Turnover Prediction is an AI Engineering skill for Claude Code, published by asgard-ai-platform in skills.

228 stars28 forkson asgard-ai-platform/skillsAdded 2026/09/03Repository updated 2026/06/06

ai-agentanthropicclaudeclaude-agent-skillsclaude-codecoding-agentknowledge-basemcpmethodologyopen-sourceprompt-engineeringskillstaiwan

Install in seconds

Install Employee Turnover Prediction

Copy Employee Turnover Prediction into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/asgard-ai-platform/skills/tree/main/algo-hr-turnover ~/.claude/skills/algo-hr-turnover

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/asgard-ai-platform/skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: algo-hr-turnover/SKILL.md in asgard-ai-platform/skills
Installs to: ~/.claude/skills/algo-hr-turnover
Collection: One of 25 skills cataloged from this repository
Category: AI Engineering — 2793 skills

What Employee Turnover Prediction does

Employee Turnover Prediction builds models that estimate the probability an employee will leave and surfaces likely retention drivers. Use it for attrition prediction, flight-risk scoring, and prioritizing HR interventions.

Employee Turnover Prediction is cataloged under AI Engineering on DirSkills. Employee Turnover Prediction comes from a repository tagged ai-agent, anthropic, claude, claude-agent-skills and claude-code.

Documentation

README

Excerpt

Employee Turnover Prediction

Overview

Turnover prediction uses classification models (logistic regression, random forest, XGBoost) to estimate the probability an employee will leave within a defined period (typically 6-12 months). Features include tenure, compensation, performance, promotion history, and engagement signals.

When to Use

Trigger conditions:

Identifying employees at high risk of voluntary departure
Quantifying which factors drive turnover for targeted interventions
Prioritizing retention budgets toward highest-impact employees

When NOT to use:

For involuntary termination planning (different process and ethics)
When headcount is < 200 (insufficient data for reliable modeling)

Algorithm

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Employee Turnover Prediction

What else does asgard-ai-platform publish alongside Employee Turnover Prediction?
Employee Turnover Prediction is one of 25 skills that DirSkills catalogs from asgard-ai-platform/skills, the repository it ships in. Its siblings there include ARIMA Time Series Model, Ad Bidding Strategies and Ad Budget Allocation. Each one is a separate skill with its own page in this directory, installs the same way Employee Turnover Prediction does, and is maintained by asgard-ai-platform in that same repository. The rest of the collection is listed on the asgard-ai-platform/skills page.
How does Employee Turnover Prediction compare to other AI Engineering skills?
Employee Turnover Prediction ranks #2501 by stars among the 2793 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Employee Turnover Prediction against them. Open each page to compare what they document and how they install.