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comp-analysis

by @anthropicsv
4.3(20)

Analyze compensation data for market benchmarking, salary band evaluation, and new hire compensation recommendations, supporting data-driven pay decisions.

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Installation
npx skills add anthropics/knowledge-work-plugins --skill comp-analysis
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Before / After Comparison

1
Before

HR manually collects market compensation data, performs data cleaning and benchmarking analysis in Excel, and calculates salary bands. A complete compensation analysis report takes 2-3 days and is prone to errors.

After

Import compensation data and market benchmarks to automatically generate benchmarking analysis, band evaluation, and new hire compensation recommendations, including visual charts and risk alerts. A comprehensive analysis is completed in 2 hours.

SKILL.md

comp-analysis

/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

  • Location matters — Always specify location for benchmarking. SF vs. Austin vs. London are very different.

  • Total comp, not just base — Include equity, bonus, and benefits for a complete picture.

  • Keep data confidential — Comp data is sensitive. Results stay in your conversation.

Weekly Installs310Repositoryanthropics/know…-pluginsGitHub Stars10.6KFirst SeenMar 13, 2026Security AuditsGen Agent Trust HubPassSocketPassSnykWarnInstalled oncodex297gemini-cli295opencode294cursor294github-copilot293amp293

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Statistics

Installs1.2K
Rating4.3 / 5.0
Version
Updated2026年5月22日
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Timeline

Created2026年3月30日
Last Updated2026年5月22日