Comp Analysis
This skill analyzes compensation data for benchmarking, band placement, and planning, helping to benchmark compensation against market data for hiring, reten...
Install
npx promptshop add comp-analysisDetails
What This Skill Does
This skill analyzes compensation data for benchmarking, band placement, and planning, helping to benchmark compensation against market data for hiring, retention, and equity planning. It is designed for HR professionals, recruiters, and compensation specialists.
When to Use
Benchmark compensation for a Senior Software Engineer in SF. Analyze compensation band placement and identify outliers. Model equity refresh grants based on stock price. Determine percentile bands for base, equity, and total compensation. Adjust compensation based on location and company stage. Compare current compensation to market benchmarks.
Key Features
Analyzes compensation data for benchmarking. Provides percentile bands (25th, 50th, 75th, 90th) for compensation. Adjusts compensation based on location and company stage. Models equity refresh grants. Identifies outliers in compensation data. Uses web research and compensation data tools for benchmarks.
Manual Installation
- Manual installation.
- View Full Skill Content.
- The complete markdown content that gets installed/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
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] |
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./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.
/comp-analysis $ARGUMENTS
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."
Base salary: Cash compensation Equity: RSUs, stock options, or other equity Bonus: Annual target bonus, signing bonus Benefits: Health, retirement, perks (harder to quantify)
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
With ~~compensation data: Pull verified benchmarks Without: Use web research, public salary data, and user-provided context Always note data freshness and source limitations
Provide percentile bands (25th, 50th, 75th, 90th) for base, equity, and total comp. Include location adjustments and company-stage context.
| Percentile | Base | Equity | Total Comp |
|---|---|---|---|
| 25th | $[X] | $[X] | $[X] |
| 50th | $[X] | $[X] | $[X] |
| 75th | $[X] | $[X] | $[X] |
| 90th | $[X] | $[X] | $[X] |
| Employee | Current Base | Band Min | Band Mid | Band Max | Position |
|---|---|---|---|---|---|
| [Name] | $[X] | $[X] | $[X] | $[X] | [Below/At/Above] |
[Specific compensation recommendations] [Equity considerations] [Retention risks if applicable]
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
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.