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Compensation Analysis

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Benchmark and model compensation with ease.

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Free · Opens the source repo

What Compensation Analysis does

The Compensation Analysis skill provides a structured approach to evaluating compensation data for various roles within an organization. It allows users to benchmark salaries against market data, analyze band placements, and model equity grants, making it a valuable tool for HR professionals and hiring managers. By utilizing this skill, users can ensure that their compensation packages are competitive and aligned with industry standards.

To use the skill, users can either inquire about specific roles, upload compensation data in CSV format, or request equity modeling for stock grants. The skill processes this information and outputs detailed compensation analyses, including percentile bands for base salary, equity, and total compensation. This data is crucial for making informed decisions regarding hiring, employee retention, and overall compensation strategy.

The skill takes into account various factors that influence compensation, such as role specialization, level of experience, geographic location, company stage, and industry. This comprehensive approach ensures that the recommendations provided are relevant and tailored to the specific context of the organization. Additionally, if connected to compensation data sources, the skill can pull real-time benchmarks, enhancing the accuracy of the analysis.

Overall, Compensation Analysis is designed for HR professionals, hiring managers, and business leaders who need to make data-driven decisions about employee compensation. Its ability to analyze and model compensation data helps organizations attract and retain top talent while maintaining competitive pay structures.

When to use it

Use this skill when you need to benchmark salaries, analyze compensation data, or model equity grants for specific roles.

When not to use it

This skill may not be suitable for organizations that do not have access to reliable compensation data or those looking for a simple salary calculator without in-depth analysis.

What you can build with it

Role-Specific Salary Inquiry

Ask about the competitive salary for a specific role, such as a Senior Software Engineer in San Francisco.

Compensation Data Upload

Upload your company's compensation data to identify outliers and compare against market benchmarks.

Equity Grant Modeling

Request a model for an equity grant to understand its value over time based on stock price.

How to install Compensation Analysis

View source

1. Install with the skills CLI

npx skills add anthropics/knowledge-work-plugins/comp-analysis --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by anthropics

/comp-analysis

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

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

Usage

/comp-analysis $ARGUMENTS

What I Need From You

Option A: Single role analysis "What should we pay a Senior Software Engineer in SF?"

Option B: Upload comp data Upload a CSV or paste your comp bands. I'll analyze placement, identify outliers, and compare to market.

Option C: Equity modeling "Model a refresh grant of 10K shares over 4 years at a $50 stock price."

Compensation Framework

Components of Total Compensation

  • Base salary: Cash compensation
  • Equity: RSUs, stock options, or other equity
  • Bonus: Annual target bonus, signing bonus
  • Benefits: Health, retirement, perks (harder to quantify)

Key Variables

  • Role: Function and specialization
  • Level: IC levels, management levels
  • Location: Geographic pay adjustments
  • Company stage: Startup vs. growth vs. public
  • Industry: Tech vs. finance vs. healthcare

Data Sources

  • With ~~compensation data: Pull verified benchmarks
  • Without: Use web research, public salary data, and user-provided context
  • Always note data freshness and source limitations

Output

Provide percentile bands (25th, 50th, 75th, 90th) for base, equity, and total comp. Include location adjustments and company-stage context.

## Compensation Analysis: [Role/Scope]

### Market Benchmarks
| Percentile | Base | Equity | Total Comp |
|------------|------|--------|------------|
| 25th | $[X] | $[X] | $[X] |
| 50th | $[X] | $[X] | $[X] |
| 75th | $[X] | $[X] | $[X] |
| 90th | $[X] | $[X] | $[X] |

**Sources:** [Web research, compensation data tools, or user-provided data]

### Band Analysis (if data provided)
| Employee | Current Base | Band Min | Band Mid | Band Max | Position |
|----------|-------------|----------|----------|----------|----------|
| [Name] | $[X] | $[X] | $[X] | $[X] | [Below/At/Above] |

### Recommendations
- [Specific compensation recommendations]
- [Equity considerations]
- [Retention risks if applicable]

If Connectors Available

If ~~compensation data is connected:

  • Pull verified market benchmarks by role, level, and location
  • Compare your bands against real-time market data

If ~~HRIS is connected:

  • Pull current employee comp data for band analysis
  • Identify outliers and retention risks automatically

Tips

  1. Location matters — Always specify location for benchmarking. SF vs. Austin vs. London are very different.
  2. Total comp, not just base — Include equity, bonus, and benefits for a complete picture.
  3. Keep data confidential — Comp data is sensitive. Results stay in your conversation.

Frequently asked questions about Compensation Analysis

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