Agent skill · business finance · anthropics
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.
Why this skill is useful
Provides domain-specific knowledge for compensation benchmarking and equity modeling that the AI wouldn't reliably generate on its own.
What it needs
About 2k tokens when loaded. Last updated 2026-08-07. 23,345 stars on the source repository.
What this skill does
/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 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. 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 1. Location matters — Always specify location for benchmarking. SF vs. Austin vs. London are very different. 2. Total comp, not just base — Include equity, bonus, and benefits for a complete picture. 3. Keep data confidential — Comp data is sensitive. Results stay in your conversation.
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills anthropics/comp-analysis