PaperclipOrg
Claude Agent Skill · by Anthropics

Comp Analysis

Install Comp Analysis skill for Claude Code from anthropics/knowledge-work-plugins.

Install
Terminal · npx
$npx skills add https://github.com/obra/superpowers --skill brainstorming
Works with Paperclip

How Comp Analysis fits into a Paperclip company.

Comp Analysis drops into any Paperclip agent that handles this kind of work. Assign it to a specialist inside a pre-configured PaperclipOrg company and the skill becomes available on every heartbeat — no prompt engineering, no tool wiring.

S
SaaS FactoryPaired

Pre-configured AI company — 18 agents, 18 skills, one-time purchase.

$27$59
Explore pack
Source file
SKILL.md92 lines
Expand
---name: comp-analysisdescription: 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.argument-hint: "<role, level, or dataset>"--- # /comp-analysis > If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../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. ```markdown## 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 1. **Location matters** — Always specify location for benchmarking. SF vs. Austin vs. London are very different.2. **Total comp, not just base** — Include equity, bonus, and benefits for a complete picture.3. **Keep data confidential** — Comp data is sensitive. Results stay in your conversation.