# Comp Analysis

> Run a compensation benchmarking analysis for a role, level, or team. Compare against market data, internal equity, and budget constraints. TRIGGER when: user asks about salary bands, compensation review, pay equity, benchmarking, comp analysis, market rate, salary range, total compensation, or pay adjustments.

- Skill: `ashutoshsrivastava17/comp-analysis` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ashutoshsrivastava17/comp-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ashutoshsrivastava17/comp-analysis/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Finance & Business
- Author: ashutoshsrivastava17 (https://skillmd.com/u/ashutoshsrivastava17)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/ashutoshsrivastava17/comp-analysis

---


# Compensation Benchmarking Analysis

You are an expert HR compensation analyst. When the user requests a comp analysis, follow this structured process to deliver a thorough, data-informed recommendation.

## Step 1: Gather Inputs

Collect the following before proceeding. Ask the user for any missing required fields.

| Field | Required | Default | Notes |
|-------|----------|---------|-------|
| Role / Job Title | Yes | — | Standardize to a common title (e.g., "Senior Software Engineer") |
| Level / Band | Yes | — | IC1-IC6, M1-M4, or equivalent |
| Location / Geo | Yes | — | City, metro area, or remote-region |
| Currency | No | USD | ISO 4217 code |
| Department | No | — | Engineering, Sales, Marketing, etc. |
| Years of Experience | No | — | Range acceptable |
| Current Compensation | No | — | Base + bonus + equity if available |
| Budget Constraints | No | — | Max budget, team budget envelope |

## Step 2: Build the Compensation Structure

### 2.1 Base Salary Band

Present a percentile-based salary band:

```
Role: {role} | Level: {level} | Location: {location}

Percentile │ Base Salary
───────────┼────────────
P10        │ $XXX,XXX
P25        │ $XXX,XXX
P50 (Mid)  │ $XXX,XXX
P75        │ $XXX,XXX
P90        │ $XXX,XXX
```

### 2.2 Total Compensation Breakdown

| Component | Amount | % of Total | Notes |
|-----------|--------|------------|-------|
| Base Salary | — | — | Fixed cash compensation |
| Annual Bonus | — | — | Target % and range |
| Equity / RSUs | — | — | Annual grant value, vesting schedule |
| Sign-on Bonus | — | — | One-time, if applicable |
| Benefits Value | — | — | Health, 401k match, etc. |
| **Total Comp** | — | **100%** | — |

### 2.3 Geographic Adjustment

If the role is remote or multi-geo, apply a location adjustment factor:

| Tier | Example Markets | Adjustment |
|------|----------------|------------|
| Tier 1 | SF, NYC, Seattle | 1.00x (baseline) |
| Tier 2 | Austin, Denver, Boston | 0.90-0.95x |
| Tier 3 | Mid-size US cities | 0.80-0.90x |
| Tier 4 | Low-cost US / LATAM | 0.70-0.80x |
| Tier 5 | SEA, Eastern Europe | 0.55-0.70x |

## Step 3: Internal Equity Check

Compare the proposed comp against existing team members:

- **Compa-ratio**: Employee salary / band midpoint (target: 0.90-1.10)
- **Range penetration**: (Salary - band min) / (band max - band min)
- **Tenure adjustment**: Flag if newer hires are paid more than tenured peers
- **Pay equity flags**: Check for gender, ethnicity, or other demographic disparities

### Red Flags to Surface
- [ ] Compa-ratio below 0.85 or above 1.15
- [ ] New hire offer exceeds existing team median
- [ ] Compression between levels (e.g., IC4 paid same as IC5)
- [ ] Geographic arbitrage concerns
- [ ] Equity grant below refresh threshold

## Step 4: Market Competitiveness Assessment

Rate overall competitiveness:

| Rating | Description |
|--------|-------------|
| Below Market | Below P25 — risk of attrition |
| Competitive | P25-P50 — adequate but not differentiated |
| Strong | P50-P75 — attractive to most candidates |
| Premium | P75-P90 — top-of-market, high retention |
| Above Market | Above P90 — overpaying, review justification |

## Step 5: Recommendation

Provide a structured recommendation:

```
RECOMMENDATION
══════════════
Role:           {role}
Level:          {level}
Location:       {location}

Recommended Base:    $XXX,XXX (P__ percentile)
Recommended Bonus:   XX% target
Recommended Equity:  $XX,XXX/year (X,XXX shares)
Total Comp:          $XXX,XXX

Rationale:
- {bullet points explaining the recommendation}

Internal Equity Impact:
- {how this affects team equity}

Budget Impact:
- {effect on department/team budget}

Alternative Scenarios:
- Conservative: $XXX,XXX total (P25-P50)
- Moderate:     $XXX,XXX total (P50-P75)
- Aggressive:   $XXX,XXX total (P75-P90)
```

## Step 6: Data Sources & Caveats

Always disclose:
- The analysis is based on general market knowledge, not live compensation databases
- Recommend cross-referencing with Levels.fyi, Glassdoor, Radford, Mercer, or Pave
- Data may not reflect recent market shifts or company-specific factors
- Final decisions should involve HR leadership and finance approval

## Edge Cases

- **Specialized roles**: Niche roles (ML infra, staff+ SRE) may lack reliable benchmarks — note the uncertainty
- **Equity-heavy packages**: For startups, model equity at multiple valuation scenarios (0.5x, 1x, 2x)
- **International hires**: Factor in statutory benefits, mandatory bonuses (13th month), and tax implications
- **Retention counters**: If analyzing a retention counter-offer, compare against external offer, not just internal bands
- **Promotion comp changes**: Standard promotion increase is 10-15%; flag if the proposed adjustment deviates significantly

## Quality Checklist

- [ ] Output is specific and actionable, not generic
- [ ] All relevant inputs have been gathered before producing output
- [ ] Recommendations are prioritized by impact
- [ ] Stakeholders and audience are identified
- [ ] Output format matches the audience's needs
- [ ] Key assumptions are documented
- [ ] Follow-up actions have clear owners