Compensation Benchmarking

Compensation Benchmarking is a Data skill for Claude Code, published by asgard-ai-platform in skills.

228 stars28 forkson asgard-ai-platform/skillsAdded 2026/09/03Repository updated 2026/06/06

ai-agentanthropicclaudeclaude-agent-skillsclaude-codecoding-agentknowledge-basemcpmethodologyopen-sourceprompt-engineeringskillstaiwan

Install in seconds

Install Compensation Benchmarking

Copy Compensation Benchmarking into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/asgard-ai-platform/skills/tree/main/algo-hr-compensation ~/.claude/skills/algo-hr-compensation

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/asgard-ai-platform/skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: algo-hr-compensation/SKILL.md in asgard-ai-platform/skills
Installs to: ~/.claude/skills/algo-hr-compensation
Collection: One of 25 skills cataloged from this repository
Category: Data — 710 skills

What Compensation Benchmarking does

Compensation Benchmarking compares internal pay to market survey data using compa-ratios and percentile positioning. Use it to assess market competitiveness, build salary bands, and check pay equity.

Compensation Benchmarking is cataloged under Data on DirSkills. Compensation Benchmarking comes from a repository tagged ai-agent, anthropic, claude, claude-agent-skills and claude-code.

Documentation

README

Excerpt

Compensation benchmarking compares internal pay levels against external market data to assess competitiveness. Uses compa-ratio (actual pay / market midpoint) and percentile positioning. Informs salary band design, pay adjustments, and equity analysis.

When to Use

Trigger conditions:

Evaluating whether current salaries are competitive with the market
Designing or updating salary bands and pay structures
Identifying pay equity gaps across demographics or roles

When NOT to use:

For individual performance-based pay decisions (use performance management)
When no market data is available (need at least survey benchmarks)

Algorithm

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Compensation Benchmarking

What else does asgard-ai-platform publish alongside Compensation Benchmarking?
Compensation Benchmarking is one of 25 skills that DirSkills catalogs from asgard-ai-platform/skills, the repository it ships in. Its siblings there include ARIMA Time Series Model, Ad Bidding Strategies and Ad Budget Allocation. Each one is a separate skill with its own page in this directory, installs the same way Compensation Benchmarking does, and is maintained by asgard-ai-platform in that same repository. The rest of the collection is listed on the asgard-ai-platform/skills page.
How does Compensation Benchmarking compare to other Data skills?
Compensation Benchmarking ranks #641 by stars among the 710 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Compensation Benchmarking against them. Open each page to compare what they document and how they install.