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Comp Analysis

anthropics/knowledge-work-pluginsOfficial

Handles compensation benchmarking and equity modeling when you're trying to figure out what to pay someone or whether your current bands make sense. You can ask about specific roles ("what should we pay a Senior SWE in SF"), upload your comp data to spot outliers and retention risks, or model equity grants with vesting schedules. It pulls from connected compensation databases if you have them, otherwise falls back to web research and public salary data. The output is straightforward percentile tables (25th through 90th) broken down by base, equity, and total comp, plus recommendations. Useful for offer negotiations, annual reviews, or when someone on your team gets a competing offer and you need market context fast.

installs
2.8K
GitHub stars
33.3K
last updated
Aug 21, 2026
View on GitHubSave

Install

$ npx -y skills add anthropics/knowledge-work-plugins --skill comp-analysis --agent claude-code

Installs into .claude/skills of the current project.

Files

/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

  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.
SKILL.md on GitHub

Sponsors

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Details

Repository
github.com/anthropics/knowledge-work-plugins
First seen
Apr 16, 2026

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