Comp analysis

Skill nota-america/forgecat-agent-profiles/profiles/anthropics/knowledge-work-plugins/anthropics_knowledge-work-plugins_human-resources/for-cursor/.cursor/skills/comp-analysis

Production-ready AI agent profiles for Claude Code, Cursor, and Codex. Search, install, and contribute ForgeCat packages.

Install

npx -y skills add nota-america/forgecat-agent-profiles --skill comp-analysis

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What its author says it does

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Analyze compensation — benchmarking, band placement, and equity modeling. Trigger with "what should we pay a [role]", "is this offer competitive", "model this equity grant", or when uploading comp data to find outliers and retention risks.

SKILL.md

3.2 KB, 721 tokens by cl100k_base, as published. Nobody here has run it

/comp-analysis

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Analyze compensation data for benchmarking, band placement, and planning. Helps benchmark compensation against market data for hiring, retention, and equity planning.

Usage

/comp-analysis $ARGUMENTS

What I Need From You

Option A: Single role analysis "What should we pay a Senior Software Engineer in SF?"

Option B: Upload comp data Upload a CSV or paste your comp bands. I'll analyze placement, identify outliers, and compare to market.

Option C: Equity modeling "Model a refresh grant of 10K shares over 4 years at a $50 stock price."

Compensation Framework

Components of Total Compensation

Base salary: Cash compensation
Equity: RSUs, stock options, or other equity
Bonus: Annual target bonus, signing bonus
Benefits: Health, retirement, perks (harder to quantify)

Key Variables

Role: Function and specialization
Level: IC levels, management levels
Location: Geographic pay adjustments
Company stage: Startup vs. growth vs. public
Industry: Tech vs. finance vs. healthcare

Data Sources

With ~~compensation data: Pull verified benchmarks
Without: Use web research, public salary data, and user-provided context
Always note data freshness and source limitations

Output

Provide percentile bands (25th, 50th, 75th, 90th) for base, equity, and total comp. Include location adjustments and company-stage context.

## Compensation Analysis: [Role/Scope]

### Market Benchmarks
| Percentile | Base | Equity | Total Comp |
|------------|------|--------|------------|
| 25th | $[X] | $[X] | $[X] |
| 50th | $[X] | $[X] | $[X] |
| 75th | $[X] | $[X] | $[X] |
| 90th | $[X] | $[X] | $[X] |

**Sources:** [Web research, compensation data tools, or user-provided data]

### Band Analysis (if data provided)
| Employee | Current Base | Band Min | Band Mid | Band Max | Position |
|----------|-------------|----------|----------|----------|----------|
| [Name] | $[X] | $[X] | $[X] | $[X] | [Below/At/Above] |

### Recommendations
- [Specific compensation recommendations]
- [Equity considerations]
- [Retention risks if applicable]

If Connectors Available

If ~~compensation data is connected:

Pull verified market benchmarks by role, level, and location
Compare your bands against real-time market data

If ~~HRIS is connected:

Pull current employee comp data for band analysis
Identify outliers and retention risks automatically

Tips

Location matters — Always specify location for benchmarking. SF vs. Austin vs. London are very different.
Total comp, not just base — Include equity, bonus, and benefits for a complete picture.
Keep data confidential — Comp data is sensitive. Results stay in your conversation.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.