Comp Analysis
- 2.1k installs
- 23.1k repo stars
- Updated July 28, 2026
- anthropics/knowledge-work-plugins
comp-analysis is an agent skill that Analyze compensation — benchmarking, band placement, and equity modeling. Trigger with "what should we pay a [role]", "i.
About
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 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 The comp analysis agent skill provides documented workflows prerequisites triggers and safety guidance from its SKILL md source Agents load it when user requests match the description and follow step by step instructions without inventing capabilities It integrates with standard agent tooling for the tasks inputs outputs and failure modes described in the repository documentation
- description: Analyze compensation — benchmarking, band placement, and equity modeling. Trigger with "what should we pay
- argument-hint: "<role, level, or dataset>"
- > If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md
- Follow comp-analysis SKILL.md steps and documented constraints.
- Follow comp-analysis SKILL.md steps and documented constraints.
Comp Analysis by the numbers
- 2,128 all-time installs (skills.sh)
- +95 installs in the week ending Jul 28, 2026 (Skillselion tracking)
- Ranked #501 of 16,659 AI & Agent Building skills by installs in the Skillselion catalog
- Security screen: MEDIUM risk (skills.sh audit)
- Data as of Jul 28, 2026 (Skillselion catalog sync)
comp-analysis capabilities & compatibility
- Capabilities
- description: analyze compensation — benchmarking · argument hint: "<role, level, or dataset>" · > if you see unfamiliar placeholders or need to · follow comp analysis skill.md steps and document
- Use cases
- orchestration
What comp-analysis says it does
description: 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 uploa
argument-hint: "<role, level, or dataset>"
> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md).
npx skills add https://github.com/anthropics/knowledge-work-plugins --skill comp-analysisAdd your badge
Show developers this skill is listed on Skillselion. Paste this into your README.
| Installs | 2.1k |
|---|---|
| repo stars | ★ 23.1k |
| Security audit | 2 / 3 scanners passed |
| Last updated | July 28, 2026 |
| Repository | anthropics/knowledge-work-plugins ↗ |
When should an agent use comp-analysis and what problem does it solve?
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 dat
Who is it for?
Developers invoking comp-analysis as documented in the skill source.
Skip if: Skip when requirements fall outside comp-analysis documented scope.
When should I use this skill?
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 dat
What you get
Outputs aligned with the comp-analysis SKILL.md workflow and stated deliverables.
Files
/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 $ARGUMENTSWhat 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
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.
Related skills
Setup Matt Pocock SkillsScaffold the per-repo configuration that Matt Pocock’s engineering agent skills rely on so they understand the issue tracker, triage labels, and domain documentation la462k185k
Lark Skill MakerQuickly turn any Lark/Feishu OpenAPI call or multi-step workflow into a reusable agent skill with its own SKILL.md.379k15.8k
CavemanSlash token usage by roughly 75% while keeping every technical detail intact when working with Claude Code, Cursor or similar agents.378k92.5k
Lark AppsConnect Claude, Cursor or custom agents directly to Lark (Feishu) for messaging, document automation, approval workflows and enterprise data access.375k
Running Claude Code Via Litellm CopilotRun Claude Code at a fraction of the cost by routing requests through LiteLLM to the GitHub Copilot Chat API.270k72
Codex PetGenerate a complete Codex Pet spritesheet and metadata from one reference image without needing an OpenAI key or Codex Pro.246k8FAQ
What is comp-analysis?
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 whe
When should I use comp-analysis?
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 whe
Is comp-analysis safe to install?
Review the Security Audits panel on this page before production use.