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comp-analysis

面向招聘、留任与股权规划的薪酬分析技能,可按角色、级别和地点给出市场分位基准,分析员工薪酬带宽位置与异常值,并给出总薪酬和股权建议;支持用户提供 CSV 或可选连接器数据。

anthropics
热度 0.00
最近更新 2026-10-19 02:06:09
分类:数据依赖:内部调用大模型 / 第三方 API应用场景:市场分析 / 报告撰写 / 财务 / 数据处理 ...

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add anthropics/knowledge-work-plugins/comp-analysis
Metadata
name
comp-analysis
description
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.
argument-hint
<role, level, or dataset>

/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.