compensation-benchmarking
npx machina-cli add skill anthropics/knowledge-work-plugins/compensation-benchmarking --openclawCompensation Benchmarking
Help benchmark compensation against market data for hiring, retention, and equity planning.
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.
Source
git clone https://github.com/anthropics/knowledge-work-plugins/blob/main/human-resources/skills/compensation-benchmarking/SKILL.mdView on GitHub Overview
Compensation Benchmarking helps you compare base salary, equity, bonuses, and benefits against market data for hiring, retention, and equity planning. It accounts for role, level, location, company stage, and industry, and outputs percentile bands to guide pay decisions.
How This Skill Works
The tool uses either verified benchmarks ('With compensation data') or web/public data with user context ('Without'). It then normalizes inputs by role, level, location, and company stage, and generates percentile bands (25th, 50th, 75th, 90th) for base, equity, and total comp, including location adjustments and notes on data freshness.
When to Use It
- When negotiating an offer or setting a salary range (what should we pay)
- During role design to establish market-rate benchmarks (market rate for)
- For location-based adjustments and company-stage context
- In equity planning and retention reviews
- During annual compensation reviews to ensure competitiveness
Quick Start
- Step 1: Define inputs — specify role, level, location, and company stage; choose data source (with data or without data)
- Step 2: Gather data and normalize (currency, inflation, and industry adjustments) to a common frame
- Step 3: Generate percentile bands (25th, 50th, 75th, 90th) for base, equity, and total comp; add location and stage context
Best Practices
- Note data freshness and source limitations for all inputs
- Prefer verified benchmarks when available; document web sources when not
- Model by role, level, location, and company stage instead of using generic ranges
- Separate and present base salary, equity, and total compensation bands
- Communicate ranges with clear caveats and an agreed update cadence
Example Use Cases
- Senior Software Engineer in San Francisco (Series B): base and total comp bands shown with location adjustment and startup-stage context
- Data Scientist in New York (growth-stage): bands across base, equity, and bonus, aligned to market rate for the role and company stage
- Product Manager in London (early-stage): UK market-normalized bands with 50th/75th percentile emphasis and equity mix
- QA Engineer in Berlin (nonprofit): adjusted bands reflecting sector norms and local cost of living
- Backend Engineer in Toronto (public company): benchmark against 90th percentile for total comp with currency and regulatory considerations
