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Skills/anthropics/knowledge-work-plugins/comp-analysis
comp-analysis logo

comp-analysis

anthropics/knowledge-work-plugins
2K installs21K stars
Run it on Hostinger →up to 70% off + an extra 10% with code ZACAARON10Free API →

Installation

npx skills add https://github.com/anthropics/knowledge-work-plugins --skill comp-analysis

Summary

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.

SKILL.md

/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.

Score

0–100
75/ 100

Grade

B

Popularity17/30

1,572 installs — growing adoption. Source repo has 20,653 GitHub stars.

Completeness27/30

Documented: full SKILL.md body, description, one-line install. Missing: category/license metadata.

Trust25/25

Published by anthropics — an official/recognized organization.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

Proud of your score? Add this badge to your README.

Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

Comp Analysis skill score badge previewScore badge

Markdown

[![Comp Analysis skill](https://www.claudemarket.ai/skills/anthropics/knowledge-work-plugins/comp-analysis/badges/score.svg)](https://www.claudemarket.ai/skills/anthropics/knowledge-work-plugins/comp-analysis)

HTML

<a href="https://www.claudemarket.ai/skills/anthropics/knowledge-work-plugins/comp-analysis"><img src="https://www.claudemarket.ai/skills/anthropics/knowledge-work-plugins/comp-analysis/badges/score.svg" alt="Comp Analysis skill"/></a>

Comp Analysis FAQ

How do I install the Comp Analysis skill?

Run “npx skills add https://github.com/anthropics/knowledge-work-plugins --skill comp-analysis” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

What does the Comp Analysis skill do?

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. The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Comp Analysis skill free?

Yes. Comp Analysis is a free, open-source skill published from anthropics/knowledge-work-plugins. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Comp Analysis work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Comp Analysis works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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