Comp Analysis

by anthropicsae1513ea94dcNo license27K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated today

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.

FeaturedInstructions onlyBusiness & Finance

AI-generated overview

Analyzes compensation data for market benchmarking, salary band placement, and equity grant modeling.

What it does: This skill produces compensation analyses covering market benchmarks, band placement, and equity modeling. It outputs percentile bands (25th, 50th, 75th, 90th) for base, equity, and total compensation, with location adjustments and company-stage context. When employee data is supplied, it also produces band analysis tables showing each employee's position relative to band minimum, midpoint, and maximum, plus recommendations and retention risks.
When to use it: Use it when deciding what to pay for a role, checking whether an offer is competitive, modeling an equity grant or refresh, or reviewing uploaded compensation data for outliers and retention risks. It suits hiring, retention, and compensation planning questions.
Requirements: Instructions only; no scripts are shipped. It works without external tools using web research, public salary data, and user-provided context, and can optionally use connected compensation data and HRIS sources if available. Input may be a role question, a CSV or pasted compensation bands, or equity grant parameters.

/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

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]

## 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.

Source and attribution

Source:anthropics/knowledge-work-pluginsinhuman-resources/skills/comp-analysisat commitae1513e

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

Report or request removal

More from anthropics/knowledge-work-plugins

Ticket Deflector

anthropics

Featured

Reads a forwarded customer email or ticket, pulls order and refund status from a payments connector (PayPal, Square, or Stripe) or Shopify, account history from the CRM, and open tickets from a support desk (Zoho Desk), drafts a tone-matched reply in the owner's writing voice, and can issue a refund through the payments connector with explicit owner approval. With Shopify connected it also runs a proactive order-triage mode that surfaces orders needing attention — unfulfilled past the promised window, payment problems, pending refunds, stuck shipments — and drafts the next action for each before the customer has to ask. Use when the user says "draft a response," "answer this customer," "where's my order," "I want a refund," "check my orders," or "anything about to blow up."

Awaiting classification27Kupdated today

Tax Season Organizer

anthropics

Featured

Prepares tax-season materials for the owner's accountant, not tax advice. US federal tax; a non-US business gets its closed-books packet instead. Two modes: (1) quarterly estimated tax from YTD net income in the ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books); (2) year-end 1099 prep, scanning the ledger, PayPal, and Stripe for contractors paid over USD 600 into a 1099-NEC list with missing W-9 flags. Any tax request routes first to /tax-prep, which confirms the books are closed and reconciled before running this skill. Use this skill directly only when the owner says the period's books are already closed: "books are closed, now do the 1099s," "run the quarterly estimate off the closed numbers," or "just the contractor W-9 list."

Awaiting classification27Kupdated today

Tax Prep

anthropics

Featured

Prepares tax materials from closed books: a quarterly estimated payment breakdown or a year-end 1099-NEC list and accountant packet.

Business & Finance27Kupdated today

Smb Onboard

anthropics

Featured

Guides a small-business owner through first-time setup: connecting tools, running a value-proof recipe, capturing business context, and setting a weekly…

Productivity & Workflow27Kupdated today

Smb Router

anthropics

Featured

Routes a small-business owner's request to the right plugin skill or command and explains what is available.

Productivity & Workflow27Kupdated today

Month End Prep

anthropics

Featured

Reconciles the accounting ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books) against PayPal, Shopify, Square, and Stripe settlements, flags transactions that need attention, suspicious duplicates, and missing receipts, then writes a plain-English P&L narrative and exports a close packet (xlsx + one-page PDF). This is the first link of the /close-month command; a request to close the month or the books routes there, and the command runs this skill before refreshing the forecast and distributing the packet. Use this skill directly only when the owner wants the reconciliation alone, with no forecast refresh and no distribution: "just reconcile, no packet," "what's missing from the books," "flag the duplicates and missing receipts," or "write the P&L narrative for this month."

Awaiting classification27Kupdated today

Comp Analysis

/comp-analysis

Usage

What I Need From You

Compensation Framework

Components of Total Compensation

Key Variables

Data Sources

Output

If Connectors Available

Tips

Source and attribution

More from anthropics/knowledge-work-plugins

Ticket Deflector

Tax Season Organizer

Tax Prep

Smb Onboard

Smb Router

Month End Prep

More in Business & Finance

Plan Payroll

Job Post Builder

Contract Review

Close Month

Business Pulse

Cash Flow Snapshot