home / skills / a5c-ai / babysitter / comp-benchmarking
This skill analyzes market compensation data to establish competitive pay structures, enabling percentile positioning, range design, and scenario modeling.
npx playbooks add skill a5c-ai/babysitter --skill comp-benchmarkingReview the files below or copy the command above to add this skill to your agents.
---
name: comp-benchmarking
description: Analyze market compensation data and establish competitive pay structures
allowed-tools:
- Read
- Write
- Glob
- Grep
- Bash
metadata:
specialization: human-resources
domain: business
category: Compensation and Benefits
skill-id: SK-013
dependencies:
- Compensation survey data
- Market pricing tools
---
# Compensation Benchmarking Skill
## Overview
The Compensation Benchmarking skill provides capabilities for analyzing market compensation data and establishing competitive pay structures. This skill enables market percentile positioning, salary range development, and compensation competitiveness monitoring.
## Capabilities
### Survey Data Analysis
- Import and analyze salary survey data
- Blend multiple survey sources
- Age and trend data appropriately
- Handle different data cuts
- Validate data quality
### Market Positioning
- Calculate market percentiles and positioning
- Determine competitive positioning strategy
- Analyze positioning by job family
- Track positioning trends
- Compare against target percentile
### Salary Range Development
- Build salary range structures
- Calculate range spread and midpoint
- Design grade structures
- Create multiple range types (broad, narrow)
- Support geographic differentials
### Scenario Modeling
- Model compensation scenarios and costs
- Project budget impacts
- Analyze merit increase scenarios
- Model structure adjustments
- Calculate cost of living impacts
### Reporting
- Generate market pricing reports
- Create competitiveness summaries
- Build survey participation reports
- Document market data sources
- Track year-over-year trends
### Geographic Analysis
- Create geographic pay differentials
- Analyze location-based pay
- Support remote work pay strategies
- Map cost of labor differences
- Handle multi-location structures
## Usage
### Market Analysis
```javascript
const marketAnalysis = {
surveys: [
{ source: 'Radford', weight: 40, year: 2026 },
{ source: 'Mercer', weight: 35, year: 2026 },
{ source: 'Compensation Surveys Inc', weight: 25, year: 2025 }
],
aging: {
rate: 3.5,
targetDate: '2026-07-01'
},
cuts: {
industry: 'Technology',
companySize: '1000-5000',
geography: 'US National'
},
jobs: [
{
internal: 'Senior Software Engineer',
surveyMatch: 'Software Engineer IV',
matchQuality: 'strong'
}
],
positioning: {
targetPercentile: 50,
hotJobs: ['Machine Learning Engineer', 'Security Engineer'],
hotJobTarget: 75
}
};
```
### Range Structure Design
```javascript
const rangeStructure = {
type: 'traditional',
grades: 10,
midpointProgression: 12,
rangeSpread: {
byGrade: {
'1-3': 40,
'4-6': 45,
'7-10': 50
}
},
overlap: 35,
anchoring: {
method: 'market-midpoint',
targetPercentile: 50
},
differentials: {
geographic: {
enabled: true,
tiers: ['Tier 1', 'Tier 2', 'Tier 3']
}
}
};
```
## Process Integration
This skill integrates with the following HR processes:
| Process | Integration Points |
|---------|-------------------|
| salary-benchmarking.js | Full market pricing workflow |
| job-evaluation-leveling.js | Job matching |
| pay-equity-analysis.js | Market data input |
## Best Practices
1. **Multiple Sources**: Use at least 2-3 survey sources
2. **Quality Matching**: Ensure strong job matches to market data
3. **Regular Updates**: Refresh market data at least annually
4. **Consistent Methodology**: Apply aging and cuts consistently
5. **Documentation**: Document all assumptions and methodology
6. **Stakeholder Communication**: Explain positioning philosophy
## Metrics and KPIs
| Metric | Description | Target |
|--------|-------------|--------|
| Compa-Ratio | Employee pay vs. range midpoint | 95-105% |
| Market Position | Actual percentile vs. target | Within 5 points |
| Range Penetration | Distribution within ranges | Normal distribution |
| External Competitiveness | Offer acceptance rate | >85% |
| Survey Participation | Surveys participated in | >3 annually |
## Related Skills
- SK-012: Job Evaluation (job matching)
- SK-014: Pay Equity (equity analysis)
This skill analyzes market compensation data and helps establish competitive pay structures. It produces market percentiles, builds salary ranges, models compensation scenarios, and monitors competitiveness over time. The outputs support informed pay decisions across geographies and job families.
The skill ingests multiple salary surveys, applies aging and weighting, and validates match quality to create blended market datasets. It calculates market percentiles, range midpoints, spreads, and geographic differentials, then runs scenario models for merit budgets, structure changes, and cost projections. Reporting modules generate market pricing summaries, participation logs, and trend analyses for stakeholder communication.
How many survey sources should I use?
Use at least two to three reputable surveys and weight them based on relevance and recency.
How do I handle weak job matches?
Flag weak matches for manual review, consider broader job families, or exclude low-quality matches from blended calculations.