技能详情（站内镜像，无评论）
许可证：MIT-0
MIT-0 ·免费使用、修改和重新分发。无需归因。
版本：v1.0.0
统计：⭐ 0 · 475 · 0 current installs · 0 all-time installs
⭐ 0
安装量（当前） 0
🛡 VirusTotal ：良性 · OpenClaw ：良性
Package：1kalin/afrexai-compensation-planner
安全扫描（ClawHub）
- VirusTotal ：良性
- OpenClaw ：良性
OpenClaw 评估
The skill is an instruction-only compensation planning framework that is internally consistent with its stated purpose and requests no credentials or installs.
目的
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.
说明范围
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.
安装机制
No install spec or code files — this is instruction-only, so nothing is written to disk or fetched at install time.
证书
The skill requests no environment variables, credentials, or config paths. No disproportionate secret access is required for the stated functionality.
持久
Skill is not always-on and is user-invocable; it does not request persistent agent-level privileges or modifications to other skills/configs.
综合结论
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.…
安装（复制给龙虾 AI）
将下方整段复制到龙虾中文库对话中，由龙虾按 SKILL.md 完成安装。
请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Compensation & Salary Benchmarking」。简介：Build competitive compensation plans using market data, salary bands, equity, b…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/1kalin/afrexai-compensation-planner/SKILL.md
（来源：yingzhi8.cn 技能库）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:
1. **Compa-ratio by role**: Actual pay ÷ midpoint of band. Target: 0.90-1.10
2. **Gender pay gap**: Compare median comp by gender within each level
3. **Tenure compression**: Are new hires making more than 2-year veterans? Fix with retention adjustments
4. **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 < 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 <10% (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
---
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