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Comp Analysis

AnthropicOfficialPublished by Anthropic

  • Skill
  • Research and analysis
  • Business and productivity
  • Apache-2.0
  • No issues found

From the author

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 description by Anthropic

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The comp-analysis skill provides a structured framework for evaluating compensation data. It enables users to benchmark specific roles against market data, assess internal band placement, and model equity grants. By processing user-provided datasets or performing research based on role, level, and location, the skill generates detailed reports including percentile bands, outlier identification, and actionable recommendations for compensation strategy.

What it does

  • Benchmarks compensation for specific roles, levels, and geographic locations.
  • Models equity grants including vesting schedules and stock price projections.
  • Analyzes internal compensation data to identify outliers and band placement.
  • Generates comparative tables for base, equity, and total compensation.
  • Provides strategic recommendations for hiring and employee retention.
  • Integrates with HRIS or compensation data tools if available to automate analysis.

When to use it

  • When determining competitive salary offers for new hires.
  • When evaluating existing employee compensation against market standards.
  • When planning equity refresh grants or stock option packages.
  • When auditing internal pay bands to identify potential retention risks.

Advantages

  • Standardizes compensation evaluation across the organization.
  • Provides clear visibility into total compensation components beyond base salary.
  • Helps mitigate retention risks by identifying pay outliers.
  • Supports data-driven decision making for equity and salary planning.

Usage examples

“What should we pay a Senior Software Engineer in SF?”
The agent provides a market benchmark table showing 25th to 90th percentile ranges for base, equity, and total compensation for the specified role and location.
“Model a refresh grant of 10K shares over 4 years at a $50 stock price.”
The agent calculates the total value and annual impact of the equity grant based on the provided parameters.
“Is this offer competitive?”
The agent compares the provided offer details against current market benchmarks and provides a summary of how the package aligns with industry standards.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/anthropics/comp-analysis/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip comp-analysis.zip -d .agents/skills

Documentation by Anthropic

/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.

Files

Requirements and compatibility

Written for: Claude Code, Claude apps

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Claude Code frontmatter

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Anthropic
Publisher
Anthropic (Official)
Source
anthropics/knowledge-work-plugins · human-resources/skills/comp-analysis · commit e050c2e
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Anthropic or Anthropic.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download comp-analysis.zip