comp-analysis
做薪酬對標、薪資帶定位和股權建模，找出異常與留任風險
中文翻譯
--- name: comp-analysis description: 分析薪酬：市場對標、薪資帶定位、股權建模。用「what should we pay a [role]」「is this offer competitive」「model this equity grant」觸發，或當使用者上傳薪酬資料想找出異常值與留任風險時使用。 argument-hint: "<role, level, or dataset>" --- # /comp-analysis > 如果看到不認識的 placeholder，或需要確認目前連了哪些工具，請看 [CONNECTORS.md](../../CONNECTORS.md)。 分析薪酬資料，用於市場對標、薪資帶定位和規劃。協助拿薪酬跟市場資料比對，支援招募、留任和股權規劃。 ## 用法 ``` /comp-analysis $ARGUMENTS ``` ## 我需要你提供什麼 **選項 A：單一職位分析** 「What should we pay a Senior Software Engineer in SF?」 **選項 B：上傳薪酬資料** 上傳 CSV，或貼上你的薪資帶。我會分析定位、找出異常值，並跟市場比較。 **選項 C：股權建模** 「Model a refresh grant of 10K shares over 4 years at a $50 stock price.」 ## 薪酬框架 ### 總薪酬的組成 - **Base salary**：現金薪酬 - **Equity**：RSU、股票選擇權或其他股權 - **Bonus**：年度目標獎金、簽約獎金 - **Benefits**：健康、退休、福利（比較難量化） ### 關鍵變數 - **Role**：職能與專業 - **Level**：IC 職級、管理職級 - **Location**：依地區做薪資調整 - **Company stage**：新創 vs. 成長期 vs. 上市公司 - **Industry**：科技 vs. 金融 vs. 醫療 ### 資料來源 - **有 ~~compensation data**：拉已驗證的對標資料 - **沒有**：用網路研究、公開薪資資料，以及使用者提供的背景 - 一定要註明資料的新鮮度和來源的限制 ## 輸出 提供 base、equity 和 total comp 的百分位區間（25th、50th、75th、90th）。要包含地區調整和公司階段的背景。 ```markdown ## 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] ``` ## 如果有連接器 如果有連上 **~~compensation data**： - 依職位、職級和地區拉已驗證的市場對標 - 拿你的薪資帶跟即時市場資料比較 如果有連上 **~~HRIS**： - 拉目前員工的薪酬資料來做薪資帶分析 - 自動找出異常值和留任風險 ## 小提醒 1. **地區很重要**：做對標一定要指定地區。SF、Austin、London 差很多。 2. **看總薪酬，不只 base**：要把股權、獎金和福利都算進去才完整。 3. **資料要保密**：薪酬資料很敏感，結果只會留在你的對話裡。
英文原文
--- 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](../../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. ```markdown ## 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.