comp-analysis
anthropics/knowledge-work-plugins
Benchmark compensation, analyze pay bands, and model equity grants for hiring and retention.
What is comp-analysis?
Analyze compensation data for benchmarking, band placement, and equity planning. Use it to benchmark roles against market data, evaluate offer competitiveness, identify pay band outliers, and model equity grants for hiring and retention decisions.
- Benchmark single roles against market percentiles (25th, 50th, 75th, 90th) for base, equity, and total compensation
- Analyze uploaded compensation datasets to identify outliers and misaligned pay bands
- Model equity grants with vesting schedules and stock price scenarios
- Compare compensation against market data by role, level, location, and company stage
- Identify retention risks and pay equity issues in existing comp data
How to install comp-analysis
npx skills add https://github.com/anthropics/knowledge-work-plugins --skill comp-analysisHow to use comp-analysis
- 1.Provide a role, level, and location (e.g., 'Senior Software Engineer in San Francisco')
- 2.Or upload a CSV with employee compensation data (names, roles, levels, current base, equity)
- 3.Or describe an equity grant scenario (share count, vesting period, stock price)
- 4.Review the percentile bands and band analysis output
- 5.Use recommendations to adjust offers, bands, or identify retention actions
Use cases
- Determine competitive offer for a new hire in a specific role and location
- Evaluate whether a candidate's offer is competitive relative to market benchmarks
- Analyze internal pay bands to find employees paid below or above market range
- Model the value of a refresh equity grant over a vesting period
- Identify which employees are at retention risk due to below-market compensation
- Hiring managers and recruiters
- People/HR leaders and compensation teams
- Finance and business operations teams
- Startup founders and executives planning headcount budgets
comp-analysis FAQ
It uses web research, public salary data, and user-provided context. If a compensation data connector is available, it pulls verified benchmarks. Always note data freshness and source limitations.
Yes. Compensation data is sensitive and results stay in your conversation. The skill does not store or share data externally.
You can still benchmark a single role by providing the role title, level, and location. The skill will research market data and provide percentile bands.
Location significantly impacts pay. The skill adjusts benchmarks by geography (e.g., SF vs. Austin vs. London). Always specify location for accurate analysis.
Yes. If you upload comp data and a connector to HRIS is available, the skill can identify outliers and employees paid below market range who may be at risk.
Full instructions (SKILL.md)
Source of truth, from anthropics/knowledge-work-plugins.
name: comp-analysis description: 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. argument-hint: "<role, level, or dataset>"
/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.
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