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comp-analysis

by @anthropicsv
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報酬データを分析し、市場ベンチマーク、職位帯域評価、新入社員の報酬提案を行い、データ駆動型の報酬決定をサポートします。

compensationdata-analysishr-strategytalent-managementfinancial-analysisGitHub
インストール方法
npx skills add anthropics/knowledge-work-plugins --skill comp-analysis
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Before / After 効果比較

1
使用前

HR手动收集市场薪酬数据、在Excel中进行数据清洗和对标分析、计算职级带宽,一份完整的薪酬分析报告需要2-3天,容易出错

使用後

导入薪酬数据和市场基准,自动生成对标分析、带宽评估和新员工薪酬建议,包含可视化图表和风险提示,2小时完成全面分析

description SKILL.md

comp-analysis

/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

  • Location matters — Always specify location for benchmarking. SF vs. Austin vs. London are very different.

  • Total comp, not just base — Include equity, bonus, and benefits for a complete picture.

  • Keep data confidential — Comp data is sensitive. Results stay in your conversation.

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インストール数200
評価4.5 / 5.0
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更新日2026年3月30日
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作成2026年3月30日
最終更新2026年3月30日