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comp-analysis

Analyze compensation — benchmarking, band placement, and equity modeling. Trigger with "what should we pay a [role]", "is this offer competitive", "model this equity grant", or when uploading comp data to find outliers and retention risks.

22,281stars2,609forksUpdated 7/2/2026
Data Science & ML#benchmarking#equity#human-resources#compensation#data-analysis

Security Assessment

Safe(100/100)
Security Score100/100

About comp-analysis

A slash command for analyzing compensation across three needs: benchmarking pay for a role, placing employees within bands, and modeling equity. It triggers on prompts like what to pay a given role, whether an offer is competitive, or a request to model an equity grant, and it accepts three input modes: a single-role question, an uploaded CSV or pasted comp bands for placement and outlier analysis, or an equity modeling scenario such as a refresh grant over several years at a given stock price.

Its framework treats total compensation as base salary, equity, bonus, and benefits, and weighs the key variables that move pay: role and specialization, level, location, company stage, and industry. Data sources are handled flexibly. With a connected compensation-data tool it pulls verified market benchmarks by role, level, and location; with a connected HRIS it pulls current employee comp for band analysis and can identify outliers and retention risks automatically; and without either it relies on web research, public salary data, and user-provided context while noting data freshness and source limitations.

The output centers on percentile bands at the 25th, 50th, 75th, and 90th percentiles for base, equity, and total compensation, with location adjustments and company-stage context. When employee data is supplied it adds a band analysis table showing each person's current base against band minimum, midpoint, and maximum with a below, at, or above position, followed by concrete recommendations covering equity considerations and any retention risks. Practical tips stress specifying location because geographies differ sharply, always reasoning about total comp rather than base alone, and keeping sensitive compensation data confidential within the conversation.

FAQ

What kinds of analysis does it perform?

Single-role market benchmarking, band placement from uploaded comp data with outlier detection, and equity modeling such as a refresh grant over several years at a given stock price.

What does the output include?

Percentile bands at the 25th, 50th, 75th, and 90th percentiles for base, equity, and total comp, plus an optional band analysis table and specific recommendations.

Does it need connected data sources?

No. It can use web research and user-provided context; a connected compensation-data tool adds verified benchmarks and a connected HRIS adds employee data with automatic outlier and retention-risk detection.

Why does it insist on location?

Because pay varies sharply by geography, for example SF versus Austin versus London, so location should always be specified for accurate benchmarking.

How is sensitive compensation data handled?

The skill stresses confidentiality and notes that results stay within your conversation.

Install comp-analysis

Download and extract the skill files to your .claude/skills/ directory.

Quick Setup:

  1. Copy the skill folder to .claude/skills/
  2. Claude will automatically detect and use the skill

Repository

anthropics/knowledge-work-plugins

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