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Assess Talent Retention

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SKILL.md

Skill: Assess Talent Retention

Domain

human_resources

Description

Assesses employee retention risk using engagement signals, compensation analysis, and flight risk modeling for workforce planning.

Tags

HR, retention, talent, workforce, analytics, engagement

Use Cases

  • •Flight risk identification
  • •Retention program targeting
  • •Compensation benchmarking
  • •Engagement analysis

Proprietary Business Rules

Rule 1: Flight Risk Scoring

Multi-factor employee attrition risk model.

Rule 2: Compensation Gap Analysis

Market compensation comparison and internal equity.

Rule 3: Engagement Signal Analysis

Behavioral indicators of disengagement.

Rule 4: Intervention Prioritization

High-value employee retention prioritization.

Input Parameters

  • •employee_id (string): Employee identifier
  • •employee_profile (dict): Employee information
  • •compensation_data (dict): Current compensation
  • •engagement_metrics (dict): Engagement indicators
  • •market_data (dict): Market compensation data
  • •performance_history (list): Performance records

Output

  • •retention_risk_score (float): Flight risk probability
  • •compensation_analysis (dict): Compensation gap assessment
  • •engagement_assessment (dict): Engagement evaluation
  • •risk_factors (list): Contributing factors
  • •retention_recommendations (list): Recommended actions

Implementation

The assessment logic is implemented in retention_assessor.py and references data from CSV files:

  • •retention_risk_factors.csv - Reference data
  • •engagement_indicators.csv - Reference data
  • •turnover_benchmarks.csv - Reference data
  • •replacement_cost_multipliers.csv - Reference data
  • •intervention_strategies.csv - Reference data
  • •flight_risk_thresholds.csv - Reference data
  • •tenure_risk_curve.csv - Reference data
  • •parameters.csv - Reference data.

Usage Example

python
from retention_assessor import assess_retention

result = assess_retention(
    employee_id="EMP-001",
    employee_profile={"tenure_years": 3, "role": "engineer", "department": "technology"},
    compensation_data={"base_salary": 120000, "bonus_target": 0.15, "equity_value": 50000},
    engagement_metrics={"survey_score": 3.5, "pto_usage": 0.40, "training_hours": 10},
    market_data={"role_median": 130000, "role_p75": 150000},
    performance_history=[{"year": 2024, "rating": "exceeds"}, {"year": 2025, "rating": "exceeds"}]
)

print(f"Retention Risk: {result['retention_risk_score']}%")

Test Execution

python
from retention_assessor import assess_retention

result = assess_retention(
    employee_id=input_data.get('employee_id'),
    employee_profile=input_data.get('employee_profile', {}),
    compensation_data=input_data.get('compensation_data', {}),
    engagement_metrics=input_data.get('engagement_metrics', {}),
    market_data=input_data.get('market_data', {}),
    performance_history=input_data.get('performance_history', [])
)