From hr-people-ops
Analyze compensation data, create salary bands, and ensure pay equity. Use this skill when benchmarking salaries, building compensation structures, or analyzing pay equity. Activate when: compensation, salary, pay equity, salary bands, compensation analysis, total rewards, salary benchmark.
How this skill is triggered — by the user, by Claude, or both
Slash command
/hr-people-ops:compensation-analysisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
**Build fair, competitive compensation structures.**
Build fair, competitive compensation structures.
## Band Structure
| Level | Title Examples | Band Width | Typical Range |
|-------|---------------|------------|---------------|
| L1 | Associate, Junior | 20% | Entry level |
| L2 | Mid-level, Specialist | 25% | 2-4 years exp |
| L3 | Senior, Lead | 30% | 5-8 years exp |
| L4 | Staff, Principal | 35% | 8-12 years exp |
| L5 | Director, Senior Staff | 40% | 12+ years exp |
## Band Positioning
| Position | % of Midpoint | When to Use |
|----------|--------------|-------------|
| Below Min | <80% | Rarely, new to role |
| Min | 80% | New to level |
| Target | 90-100% | Fully competent |
| Midpoint | 100% | Market rate |
| Above Mid | 100-120% | High performer |
| Max | 120% | Exceptional, at cap |
## Step 1: Market Data
- Gather salary data from 3+ sources
- Sources: Radford, Mercer, Levels.fyi, Glassdoor, Payscale
- Match to job families and levels
## Step 2: Determine Positioning
| Strategy | Market Position | When to Use |
|----------|----------------|-------------|
| Lead | 75th percentile | Talent-competitive roles |
| Match | 50th percentile | Standard roles |
| Lag | 25th percentile | Budget constraints |
## Step 3: Set Band Width
- Narrower bands (20%): Entry-level, structured roles
- Wider bands (40%): Senior, variable roles
## Step 4: Calculate Ranges
Midpoint = Market rate at target percentile
Min = Midpoint × (1 - Band Width/2)
Max = Midpoint × (1 + Band Width/2)
Example (30% band, $100K midpoint):
- Min: $100K × 0.85 = $85,000
- Max: $100K × 1.15 = $115,000
## Step 1: Data Collection
Required fields:
- Base salary
- Job level/band
- Department
- Location
- Tenure
- Gender
- Race/ethnicity (where legally collected)
- Performance rating
## Step 2: Group Comparison
Compare pay within:
- Same job level
- Same department
- Same location
- Similar tenure
## Step 3: Statistical Analysis
- Calculate pay gap percentages
- Run regression analysis controlling for:
- Job level
- Experience
- Performance
- Location
- Education (if relevant)
## Step 4: Identify Outliers
Flag individuals who are:
- >5% below expected pay
- >10% above expected pay
- Unexplained by legitimate factors
## Raw Pay Gap
(Avg Male Salary - Avg Female Salary) / Avg Male Salary × 100
## Adjusted Pay Gap
Difference after controlling for:
- Job level
- Department
- Location
- Experience
- Performance
## Compa-Ratio
Individual Salary / Band Midpoint × 100
Target: 90-110%
## Base Salary
- Fixed annual pay
- Typically 60-80% of total comp
## Variable Pay
### Bonus
- Target %: [X]% of base
- Performance multiplier: 0-200%
- Payout timing: Annual/Quarterly
### Commission (Sales)
- On-target earnings (OTE)
- Split: [X]% base / [X]% variable
- Accelerators above quota
## Equity
### Stock Options
- Grant value at hire
- Vesting: Typically 4 years, 1-year cliff
- Refresh grants
### RSUs
- Restricted Stock Units
- Same vesting as options
- Value = shares × stock price
## Benefits Value
- Health insurance: $[X]/year
- 401(k) match: [X]% up to $[X]
- Other benefits: $[X]/year
## Total Rewards Statement
Base Salary: $XXX,XXX
Target Bonus: $XX,XXX
Equity (annual): $XX,XXX
Benefits: $XX,XXX
Total Compensation: $XXX,XXX
## Annual Review Cycle
### Timeline
| Month | Activity |
|-------|----------|
| Q3 | Budget planning, market data refresh |
| Q4 | Manager recommendations |
| Jan | Calibration sessions |
| Feb | Final approvals |
| Mar | Communication to employees |
| Apr | New compensation effective |
### Manager Worksheet
For each employee, consider:
1. Current compa-ratio
2. Performance rating
3. Time since last increase
4. Retention risk
5. Market movement
6. Budget constraints
### Calibration Questions
- Are increases proportional to performance?
- Are there pay equity concerns?
- Are high performers above midpoint?
- Are retention risks addressed?
## Location-Based Adjustments
| Tier | Example Locations | % of Base |
|------|-------------------|-----------|
| Tier 1 | SF, NYC, Seattle | 100% |
| Tier 2 | LA, Boston, Denver | 90-95% |
| Tier 3 | Austin, Chicago, Atlanta | 85-90% |
| Tier 4 | Other metro areas | 80-85% |
| Tier 5 | Rural / low COL | 75-80% |
## Relocation Considerations
- Moving to higher tier: May increase
- Moving to lower tier: Typically maintain (grandfathered)
- New hires: Paid at location rate
npx claudepluginhub latestaiagents/agent-skills --plugin hr-people-opsBuilds or redesigns salary band structures, total rewards frameworks, and compensation philosophies using market data and job architecture.
Benchmarks compensation for roles against market data, analyzes band placement and outliers from uploads, models equity grants for hiring and retention planning.