Build competitive compensation plans using market data, salary bands, equity, bonuses, geographic pay adjustments, and retention risk scoring.
数据来源:ClawHub。 在 ClawSkills 查看
选择你使用的 Agent
方法一:命令行安装(推荐)
推荐(无需提前安装 clawhub)
npx clawhub@latest --dir ~/.claude/skills install afrexai-compensation-planner或使用 clawhub CLI(需提前安装)
clawhub --dir ~/.claude/skills install afrexai-compensation-planner⚠️ 需要 Node.js 18+,没有 Node?请使用下方方法二直接下载 ZIP。 安装 Node.js →
方法二:手动下载安装(无需 Node)
下载 ZIP,解压后将文件夹放到以下路径,重启 Agent 即可:
安装路径
~/.claude/skills/afrexai-compensation-planner/💡解压后将文件夹放到上方路径,重启 Agent 即可生效
Build data-driven compensation structures that attract talent without overpaying. Covers base salary bands, equity/bonus frameworks, geographic differentials, and total rewards packaging.
When asked to build a compensation plan, follow this framework:
Define job levels and salary bands:
| Level | Title Pattern | Base Range (US) | Equity % | Bonus Target | |-------|--------------|-----------------|----------|--------------| | L1 | Associate / Junior | $45K-$70K | 0-0.01% | 0-5% | | L2 | Mid-level | $70K-$110K | 0.01-0.05% | 5-10% | | L3 | Senior | $110K-$160K | 0.05-0.15% | 10-15% | | L4 | Staff / Lead | $150K-$210K | 0.1-0.3% | 15-20% | | L5 | Principal / Director | $190K-$280K | 0.2-0.5% | 20-30% | | L6 | VP / C-level | $250K-$400K+ | 0.5-2%+ | 30-50%+ |
Apply cost-of-labor multipliers (not cost-of-living):
| Tier | Markets | Multiplier | |------|---------|------------| | Tier 1 | SF Bay, NYC, London | 1.0x (baseline) | | Tier 2 | Seattle, Boston, LA, Chicago | 0.90-0.95x | | Tier 3 | Austin, Denver, Manchester, Berlin | 0.80-0.85x | | Tier 4 | Remote US/UK secondary markets | 0.70-0.80x | | Tier 5 | Eastern Europe, LATAM, SEA | 0.40-0.60x |
Break down total rewards:
Cash Compensation
Equity Compensation
Benefits & Perks (typically 20-35% on top of base)
Run these checks quarterly:
| Month | Action | |-------|--------| | Jan | Market data refresh (Levels.fyi, Glassdoor, Radford, Mercer) | | Feb | Manager calibration sessions | | Mar | Budget allocation (typically 3-5% of payroll for merit increases) | | Apr | Communicate adjustments, effective date | | Jul | Mid-year equity refresh grants | | Oct | Prepare next year's comp budget proposal |
Before extending any offer:
| Factor | Weight | Score (1-5) | |--------|--------|-------------| | Below market rate (>10% under) | 25% | | | Time since last raise (>18 months) | 20% | | | Flight risk signals (LinkedIn active, disengaged) | 20% | | | Critical role / hard to replace | 20% | | | Tenure > 3 years with no promotion | 15% | |
Score > 3.5 = immediate retention conversation needed Score 2.5-3.5 = include in next review cycle, prioritize Score < 2.5 = monitor quarterly
For revenue roles, design OTE (On-Target Earnings):
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安装 Compensation & Salary Benchmarking 后,可以对 AI 说这些话来触发它
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将技能文件夹放到 ~/.claude/skills/afrexai-compensation-planner/ 目录(个人级,所有项目可用),或 .claude/skills/afrexai-compensation-planner/(项目级)。重启 AI 客户端后,用 /afrexai-compensation-planner 主动调用,或让 AI 根据上下文自动发现并使用。
Compensation & Salary Benchmarking 支持 Claude、Cursor、OpenClaw,可与这些 AI 平台无缝集成,扩展其能力。
Compensation & Salary Benchmarking 可免费安装使用。请查阅仓库了解许可证信息。
Build competitive compensation plans using market data, salary bands, equity, bonuses, geographic pay adjustments, and retention risk scoring.
Automate my data & analytics tasks using Compensation & Salary Benchmarking
Identifies repetitive steps in your workflow and sets up Compensation & Salary Benchmarking to handle them automatically
Compensation & Salary Benchmarking 属于「Data & Analytics」分类,该分类的技能帮助 AI 智能体在此领域执行专业任务。