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comp-analysis

Analyze compensation data to benchmark market pay and compute percentile bands.

704|58|Updated Mar 20, 2026
Search Tags:#benchmark#compensation#market data#equity-modeling#pay bands#hr-analytics
One-click install
npx skills add https://github.com/openyak/desktop --skill comp-analysis-openyak
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: comp-analysis
Source: https://github.com/openyak/desktop/tree/main/backend/app/data/plugins/human-resources/skills/comp-analysis
Command: npx skills add https://github.com/openyak/desktop --skill comp-analysis-openyak

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analyze compensation data for benchmarking, band placement, and planning. Helps benchmark compensation against market data for hiring, retention, and equity planning.

Core Features & Use Cases

  • Benchmark compensation against market data by role, level, and location to inform pay decisions.
  • Compute percentile bands (25th, 50th, 75th, 90th) for base, equity, and total compensation, including location and company-stage context.
  • Model equity grants and identify outliers or retention risks to guide offers and promotions.

Quick Start

Analyze compensation data for a Senior Software Engineer in SF to generate market benchmarks.

Frequently Asked Questions about comp-analysis

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

FAQPage Schema
How do I benchmark compensation against market data for a specific role and location?▼

Equity modeling calculates percentile distributions for equity grants across company stages and locations, identifying retention risks and outliers to guide offer structures and promotion adjustments.

Can I set pay bands using percentile calculations for different company stages?▼

Yes, you can set pay bands by computing 25th, 50th, 75th, and 90th percentiles for base and total compensation, factoring in company-stage context to align internal bands with current market data.

What compensation data do I need to model equity grants and identify retention risks?▼

Modeling equity grants and identifying retention risks requires compensation datasets including role, level, location, and company stage to detect outliers and generate structured equity planning recommendations.

Does this approach work for benchmarking total compensation across multiple geographic locations?▼

Yes, benchmarking total compensation works across multiple locations by applying location context to the dataset, allowing you to compute accurate percentile bands for base, equity, and total pay geographically.

What are the limitations of using percentile bands for compensation benchmarking?▼

Percentile band benchmarking relies heavily on dataset quality and completeness; sparse data for niche roles or specific locations may skew percentile calculations, limiting accuracy for highly specialized compensation planning.

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