comp-analysis
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Chinese/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.
如果你看到不熟悉的占位符或需要查看已连接的工具，请参阅CONNECTORS.md。
分析薪酬数据以进行基准测试、职级薪酬定位和规划。帮助将薪酬与市场数据对标，用于招聘、员工留存和股权规划。
Usage
使用方法
/comp-analysis $ARGUMENTS/comp-analysis $ARGUMENTSWhat I Need From You
我需要你提供以下信息
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."
选项A：单一岗位分析
“我们应该给旧金山的高级软件工程师支付多少薪资？”
选项B：上传薪酬数据
上传CSV文件或粘贴你的薪酬职级表。我会分析薪酬定位、识别异常值，并与市场数据进行对比。
选项C：股权建模
“对10,000股、分4年归属、股价为50美元的股权激励刷新计划进行建模。”
Compensation Framework
薪酬框架
Components of Total Compensation
总薪酬构成
- Base salary: Cash compensation
- Equity: RSUs, stock options, or other equity
- Bonus: Annual target bonus, signing bonus
- Benefits: Health, retirement, perks (harder to quantify)
- 基本工资：现金薪酬
- 股权：RSUs、股票期权或其他股权形式
- 奖金：年度目标奖金、签约奖金
- 福利：健康保险、退休计划、额外福利（较难量化）
Key Variables
关键变量
- 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
- 岗位：职能与专业方向
- 职级：个人贡献者职级、管理职级
- 地区：地域薪资调整
- 公司阶段：初创公司、成长型公司、上市公司
- 行业：科技、金融、医疗保健等
Data Sources
数据来源
- With ~~compensation data: Pull verified benchmarks
- Without: Use web research, public salary data, and user-provided context
- Always note data freshness and source limitations
- 若已连接~~compensation data：获取经过验证的基准数据
- 若未连接：使用网络调研、公开薪资数据和用户提供的背景信息
- 始终标注数据的时效性和来源局限性
Output
输出内容
Provide percentile bands (25th, 50th, 75th, 90th) for base, equity, and total comp. Include location adjustments and company-stage context.
markdown
undefined提供基本工资、股权和总薪酬的百分位区间（25分位、50分位、75分位、90分位）。包含地域调整和公司阶段背景信息。
markdown
undefinedCompensation Analysis: [Role/Scope]
薪酬分析：[岗位/分析范围]
Market Benchmarks
市场基准数据
| 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]
| 百分位 | 基本工资 | 股权 | 总薪酬 |
|---|---|---|---|
| 25分位 | $[X] | $[X] | $[X] |
| 50分位 | $[X] | $[X] | $[X] |
| 75分位 | $[X] | $[X] | $[X] |
| 90分位 | $[X] | $[X] | $[X] |
数据来源： [网络调研、薪酬数据工具或用户提供的数据]
Band Analysis (if data provided)
职级薪酬分析（若提供数据）
| Employee | Current Base | Band Min | Band Mid | Band Max | Position |
|---|---|---|---|---|---|
| [Name] | $[X] | $[X] | $[X] | $[X] | [Below/At/Above] |
| 员工 | 当前基本工资 | 职级下限 | 职级中位值 | 职级上限 | 薪酬定位 |
|---|---|---|---|---|---|
| [姓名] | $[X] | $[X] | $[X] | $[X] | [低于/符合/高于] |
Recommendations
建议
- [Specific compensation recommendations]
- [Equity considerations]
- [Retention risks if applicable]
undefined- [具体薪酬建议]
- [股权相关考量]
- [若适用，留存风险提示]
undefinedIf Connectors Available
若已连接相关工具
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
如果已连接~~compensation data：
- 根据岗位、职级和地区获取经过验证的市场基准数据
- 将你的薪酬职级表与实时市场数据进行对比
如果已连接~~HRIS：
- 获取当前员工的薪酬数据以进行职级薪酬分析
- 自动识别异常值和留存风险
Tips
提示
- 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.
- 地区很重要——基准测试时务必指定地区。旧金山、奥斯汀和伦敦的薪资差异很大。
- 看总薪酬而非仅基本工资——要包含股权、奖金和福利以获取完整的薪酬图景。
- 数据保密——薪酬数据属于敏感信息。分析结果仅保留在你的对话中。