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在 OpenClaw 中安装
/install afrexai-compensation-planner
功能描述
Build competitive compensation plans using market data, salary bands, equity, bonuses, geographic pay adjustments, and retention risk scoring.
使用说明 (SKILL.md)
Compensation & Salary Benchmarking Planner
Build data-driven compensation structures that attract talent without overpaying. Covers base salary bands, equity/bonus frameworks, geographic differentials, and total rewards packaging.
When to Use
- Building or revising salary bands for any role
- Preparing for hiring sprints and need market-rate data
- Conducting annual compensation reviews
- Designing equity/bonus/commission structures
- Benchmarking against competitors to reduce turnover
How It Works
When asked to build a compensation plan, follow this framework:
1. Role Architecture
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%+ |
2. Geographic Differentials
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 |
3. Total Compensation Package
Break down total rewards:
Cash Compensation
- Base salary: 60-80% of total comp (varies by seniority)
- Performance bonus: 5-30% of base
- Commission (sales roles): 40-60% of OTE
Equity Compensation
- Startup (pre-Series B): 0.01%-2% based on level, 4-year vest, 1-year cliff
- Growth stage: RSUs, lower % but higher dollar value
- Public company: RSU grants refreshed annually
Benefits & Perks (typically 20-35% on top of base)
- Health insurance: $6K-$24K/yr employer cost per employee (US)
- 401(k)/pension match: 3-6% of salary
- PTO: 15-25 days (US), 25-33 days (UK/EU statutory + company)
- Learning budget: $1K-$5K/yr
- Remote stipend: $100-$250/mo
- Parental leave: 12-26 weeks (competitive)
4. Pay Equity Audit
Run these checks quarterly:
- Compa-ratio by role: Actual pay ÷ midpoint of band. Target: 0.90-1.10
- Gender pay gap: Compare median comp by gender within each level
- Tenure compression: Are new hires making more than 2-year veterans? Fix with retention adjustments
- Band penetration: % of employees above 1.0 compa-ratio (flag if >30%)
5. Annual Review Cycle
| 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 |
6. Offer Benchmarking Checklist
Before extending any offer:
- Check 3+ data sources (Levels.fyi, Glassdoor, Payscale, LinkedIn Salary)
- Confirm geographic tier and apply multiplier
- Calculate total comp (base + bonus + equity annualized + benefits value)
- Compare to internal peers at same level (±10% band)
- Document justification if above band midpoint
- Get sign-off from hiring manager + finance/HR
7. Retention Risk Scoring
| 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 \x3C 2.5 = monitor quarterly
8. Commission & Sales Comp
For revenue roles, design OTE (On-Target Earnings):
- Base:Variable split: 50:50 (hunters), 60:40 (farmers), 70:30 (CS/AM)
- Accelerators: 1.5-3x rate above quota (motivates overperformance)
- Decelerators: 0.5x rate below 80% quota (protects company)
- Clawback policy: Define for churned deals within 90 days
- SPIFs: Short-term incentives for strategic pushes ($500-$5K per qualifying action)
Key Metrics to Track
- Offer acceptance rate: Target >85% (below = comp is off-market)
- Regrettable attrition: Target \x3C10% (above = retention issue)
- Time to fill: If increasing, may signal comp competitiveness problem
- Cost per hire: Include recruiter fees, signing bonuses, relocation
- Revenue per employee: Benchmark against industry ($200K-$400K SaaS, $150K-$250K services)
Data Sources (2026)
- Levels.fyi — Best for tech roles, real verified data
- Glassdoor — Broad coverage, self-reported
- Payscale — Small business focus
- Radford (Aon) — Enterprise-grade, paid surveys
- Mercer — Global comp data, paid
- LinkedIn Salary Insights — Good for role-specific ranges
- BLS Occupational Employment Statistics — Government baseline
More Frameworks
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Bundles:
- Pick 3 packs — $97
- All 10 packs — $197
- Everything bundle — $247
安全使用建议
This skill appears coherent and low-risk because it is instruction-only and asks for no credentials or installs. Before installing or using it, consider: (1) avoid pasting sensitive employee PII or full payroll exports into the agent unless you trust its context and storage policies; (2) the framework references paid/third-party data sources — you will need to supply or fetch data manually (and may require subscriptions); (3) the README/SKILL.md contains promotional links to AfrexAI products — be cautious about following purchase links or sharing credit card/account info; (4) if you ask the agent to perform audits, confirm what data it will access, how it stores results, and who can see them. Overall, the skill is consistent with its stated purpose — treat it as a guidance/template tool rather than an automated data connector.
功能分析
Type: OpenClaw Skill
Name: afrexai-compensation-planner
Version: 1.0.0
The skill bundle provides a detailed, legitimate framework for compensation and salary planning. The `SKILL.md` contains extensive instructions and data for the AI agent, all aligned with the stated purpose. While external links are present in both `SKILL.md` and `README.md` (e.g., `https://afrexai-cto.github.io/context-packs/`), they are clearly presented as additional resources from the same developer ('AfrexAI') and do not contain any instructions for the agent to execute code, exfiltrate data, or perform other malicious actions. There is no evidence of prompt injection designed to subvert the agent's behavior or steal information.
能力评估
Purpose & Capability
Name, description, and runtime instructions all align: the skill provides frameworks, checklists, and metrics for building compensation plans and does not request unrelated access or tools.
Instruction Scope
SKILL.md is self-contained and describes how to build salary bands, audits, and retention scoring. It references external data sources (Levels.fyi, Glassdoor, Radford, Mercer, LinkedIn, BLS) and AfrexAI product links; this is expected for benchmarking but could lead an agent to request or aggregate employee data or to consult external services if the user supplies credentials or datasets.
Install Mechanism
No install spec or code files — this is instruction-only, so nothing is written to disk or fetched at install time.
Credentials
The skill requests no environment variables, credentials, or config paths. No disproportionate secret access is required for the stated functionality.
Persistence & Privilege
Skill is not always-on and is user-invocable; it does not request persistent agent-level privileges or modifications to other skills/configs.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install afrexai-compensation-planner - 安装完成后，直接呼叫该 Skill 的名称或使用
/afrexai-compensation-planner触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.0.0
Initial release: Compensation & Salary Benchmarking Planner.
- Provides frameworks to build data-driven compensation plans by job level and geography.
- Includes detailed tables for salary bands, equity, and bonus targets across 6 levels.
- Offers cost-of-labor geographic differentials for global hiring.
- Guides on structuring total rewards, auditing pay equity, and running annual review cycles.
- Contains benchmarking checklists and retention risk scoring tools.
- Addresses specialized comp plans for sales and revenue roles.
- Lists key compensation metrics and up-to-date market data sources.
元数据
常见问题
Compensation & Salary Benchmarking 是什么？
Build competitive compensation plans using market data, salary bands, equity, bonuses, geographic pay adjustments, and retention risk scoring. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 680 次。
如何安装 Compensation & Salary Benchmarking？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install afrexai-compensation-planner」即可一键安装，无需额外配置。
Compensation & Salary Benchmarking 是免费的吗？
是的，Compensation & Salary Benchmarking 完全免费（开源免费），可自由下载、安装和使用。
Compensation & Salary Benchmarking 支持哪些平台？
Compensation & Salary Benchmarking 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Compensation & Salary Benchmarking？
由 1kalin（@1kalin）开发并维护，当前版本 v1.0.0。
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