Compensation & Salary Benchmarking

Compensation & Salary Benchmarking extends your AI assistant with the ability to build competitive compensation plans using market data, salary bands, equity, bonuses, geographic pay adjustments, and retention risk scoring. Rather than leaving your conversation to handle this manually, you can ask your Claude agent directly — and it will take care of the task end-to-end, using Compensation & Salary Benchmarking as its underlying capability.

Compensation & Salary Benchmarking works across Claude, Cursor, OpenClaw through the Model Context Protocol (MCP) — an open standard that lets AI clients share tools and skills without lock-in. Because MCP is platform-agnostic by design, you install Compensation & Salary Benchmarking once and it becomes available across all your AI clients. Whether you're working in Claude for focused sessions or Cursor for integrated workflows, the skill behaves consistently.

Getting started with Compensation & Salary Benchmarking takes about two minutes. Place the skill at `~/.claude/skills/afrexai-compensation-planner/` (personal, all projects) or `.claude/skills/afrexai-compensation-planner/` (project-specific), then restart your AI client. From that point, typing `/afrexai-compensation-planner` in any conversation activates it, or the AI will use it on its own when it detects a relevant request.

Compensation & Salary Benchmarking has 642 installs and is part of the growing Data & Analytics skill ecosystem on DiscoverAISkills. The source code is open on GitHub — you can inspect it, contribute fixes, or fork it to adapt the skill for your specific setup. Like all skills on DiscoverAISkills, it is free to install and use. The broader AI skills ecosystem continues to expand as developers contribute new capabilities across categories like developer tools, data analysis, writing, automation, and more.