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.
$ npx promptcreek add comp-analysisAuto-detects your installed agents and installs the skill to each one.
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
Installation
$ npx promptcreek add comp-analysisAuto-detects your installed agents (Claude Code, Cursor, Codex, etc.) and installs the skill to each one.
View Full Skill Content
/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 $ARGUMENTSWhat 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.
Supported Agents
Attribution
Details
- License
- MIT
- Source
- admin
- Published
- 3/18/2026
Tags
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