claude-skills/

Anthropic公式スキル・プラグインの日本語ディレクトリ

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スキルKnowledge Work

💰comp-analysis

プラグイン
Human Resources
引数
<role, level, or dataset>

説明

補償分析 — ベンチマーキング、給与帯設定、および株式モデリングを実施します。 次のような場合に使用: - 「[職種]にはいくら支払うべきか」という質問 - 「この給与提示は競争力があるか」という質問 - 「この株式付与をモデリングしてほしい」という依頼 - 補償データをアップロードして、異常値と離職リスクを特定する場合

原文を表示

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.

ユースケース

  • 職種の適切な給与額を決定するとき
  • 給与提示の競争力を評価するとき
  • 株式付与をモデリングするとき
  • 補償データから異常値を特定するとき
  • 給与と離職リスクの関連を分析するとき

本文

/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.

原文・著作権は Anthropic および各プラグイン作者に帰属します。日本語訳は Claude API による自動翻訳です。