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.

3,891 stars
Complexity: easy

About this skill

The Compensation & Salary Benchmarking Planner is an AI agent skill designed to help users create or refine robust compensation strategies. It provides a structured framework that guides the AI in defining job levels, establishing base salary ranges, outlining equity and bonus targets, and applying geographic differentials based on cost-of-labor. The skill also advises on structuring total compensation packages, including cash, equity, and benefits. This skill is particularly useful for HR professionals, recruiters, startup founders, and managers needing to establish fair, competitive, and budget-conscious pay structures. It ensures consistency in compensation practices while adapting to market rates and regional economic factors. By following its defined tables and guidelines, the AI agent can generate comprehensive proposals that help attract and retain top talent. Users would leverage this skill to standardize compensation across different roles and locations, benchmark against industry standards, and make informed decisions on total rewards. It helps in developing clear compensation philosophies that support organizational growth and talent management objectives.

Best use case

The primary use case for this skill is to assist HR professionals, recruiters, and business leaders in quickly drafting or refining compensation plans. It enables the creation of equitable and competitive pay structures that align with market rates and geographic cost-of-labor, ensuring that companies can attract and retain talent effectively without overspending.

Build data-driven compensation structures that attract talent without overpaying. Covers base salary bands, equity/bonus frameworks, geographic differentials, and total rewards packaging.

A structured, data-driven compensation plan proposal, including salary bands, equity, bonus targets, and geographic adjustments for a specified role or company.

Practical example

Example input

Draft a compensation plan for a Senior Software Engineer at a Series A startup in Austin, TX, including base salary, equity, and bonus targets.

Example output

**Role: Senior Software Engineer (L3)**
*   **Base Salary (Austin):** $88,000 - $136,000 (Based on US L3 range $110K-$160K, multiplied by Tier 3 factor 0.80-0.85x)
*   **Equity:** 0.05% - 0.15% (4-year vest, 1-year cliff, typical for L3 at Series A startup)
*   **Bonus Target:** 10-15% of base salary

When to use this skill

  • When building or revising salary bands for any role within an organization.
  • When preparing for hiring sprints and needing market-rate compensation data.
  • When conducting annual compensation reviews to ensure competitive pay.
  • When designing new equity, bonus, or commission structures.

When not to use this skill

  • If seeking real-time, highly granular compensation data for extremely niche roles or regions not covered by the framework's tiers.
  • If requiring legal advice on compensation compliance, as this skill provides a strategic framework, not legal counsel.
  • For direct, individual salary negotiation without further specific market research or company budget context.
  • If your organization's compensation philosophy is entirely outside standard market-based structures.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/afrexai-compensation-planner/SKILL.md --create-dirs "https://raw.githubusercontent.com/openclaw/skills/main/skills/1kalin/afrexai-compensation-planner/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/afrexai-compensation-planner/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How Compensation & Salary Benchmarking Planner Compares

Feature / AgentCompensation & Salary Benchmarking PlannerStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityeasyN/A

Frequently Asked Questions

What does this skill do?

Build data-driven compensation structures that attract talent without overpaying. Covers base salary bands, equity/bonus frameworks, geographic differentials, and total rewards packaging.

How difficult is it to install?

The installation complexity is rated as easy. You can find the installation instructions above.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# 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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