Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add w95/awesome-claude-corporate-skills --skill compensation-benchmarkinggit clone --depth 1 https://github.com/w95/awesome-claude-corporate-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/w95/awesome-claude-corporate-skills/compensation-benchmarking)<a href="https://agentmods.dev/skills/w95/awesome-claude-corporate-skills/compensation-benchmarking"><img src="https://agentmods.dev/badge/skills/w95/awesome-claude-corporate-skills/compensation-benchmarking/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/w95/awesome-claude-corporate-skills/compensation-benchmarking"><img src="https://agentmods.dev/badge/skills/w95/awesome-claude-corporate-skills/compensation-benchmarking.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00062 | $0.04347 |
| Opus 5 | $0.00031 | $0.02174 |
| Sonnet 5 | $0.00012 | $0.00869 |
| Haiku 4.5 | $0.00006 | $0.00435 |
Grade A, and why
compensation-benchmarking scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 558 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compensation Benchmarking
Overview
This skill helps HR professionals and leaders make data-driven compensation decisions that are competitive, equitable, and aligned to business strategy. It provides frameworks for market research, salary band development, pay equity analysis, compensation proposals, and ongoing market monitoring that attract and retain talent while managing labor costs.
When to Use This Skill
- Researching market rates for specific roles
- Building or updating salary bands
- Creating compensation strategy
- Analyzing pay equity issues
- Conducting salary reviews or adjustments
- Preparing compensation for offer negotiations
- Benchmarking against competitors
- Analyzing compensation by demographics
- Building executive compensation packages
- Creating retention-focused compensation strategies
Key Components
1. Market Research & Data Sources
Primary Data Sources:
Salary Surveys:
- Bureau of Labor Statistics (BLS): Free, government data
- Salary.com, Glassdoor, PayScale: Self-reported data (consider bias)
- Robert Half, Mercer, Towers Watson: Professional surveys (cost-based)
- Industry-specific surveys: Often most relevant but specialized
Methodology:
- Identify comparable roles in target market
- Compare: Company size, industry, geography, experience level
- Collect data from 3-5 sources minimum
- Weight most recent and relevant data more heavily
- Adjust for cost of living by geography
- Account for company size and maturity
Data Points to Collect:
- Median salary (most reliable)
- 25th and 75th percentiles (understand range)
- Benefits and total compensation
- Bonus and variable comp (if applicable)
- Stock options or equity (if applicable)
- Job title and description (ensure comparability)
- Company size, industry, geography
- Years of experience required
Critical: Ensure Comparable Roles Don't compare apples to oranges:
- Title might vary: "Senior Product Manager" vs. "Product Manager III"
- Scope and responsibility: 1-person team vs. 10-person team
- Experience level: 5 years vs. 15 years experience
- Geography: San Francisco vs. Austin (significant cost of living differences)
- Industry: Tech startup vs. enterprise healthcare
- Match on as many dimensions as possible
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 558 lines · 62 tokens per session scan A 8a360b444745
compensation-benchmarking is a skill published in the GitHub repository w95/awesome-claude-corporate-skills (198 stars, last pushed 6mo ago), licensed MIT. It adds 62 tokens to every session and 4,347 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
financial-expert
A financial research skill for China and Hong Kong securities, funds, company finances, economic indicators, research reports, announcements, news, and business-risk data. It returns data and neutral analysis rather than placing trades or recommending investments.
stock-analysis-lead
Orchestrate a US-stock investment analysis — classify sector archetype, fetch SEC filings, dispatch a tiered fan-out of six vertical equity-research agents (business model, earnings quality, balance sheet, management, industry, peer comparison) over a validated JSON findings contract, then synthesize a buy/hold/sell…
stock-business-review
Review a US-listed company's business model and revenue structure for an equity-research workup. Covers product/service mix, customer concentration, geographic exposure, industry position, revenue-growth decomposition (organic vs acquired vs price vs volume), and information-tier discipline (which numbers are facts vs…
stock-earnings-quality-review
Review a US-listed company's earnings quality, cash-flow integrity, and operating leverage for an equity-research workup. Covers operating cash flow vs net income drift, free cash flow trajectory, capex character (maintenance vs expansion), equity issuance / shareholder-return yield, revenue-quality signals…
stock-balance-sheet-review
Review a US-listed company's balance sheet health for an equity-research workup. Covers net-debt/EBITDA leverage, current ratio, cash runway, goodwill concentration and impairment history, DSO trend, inventory days, off-balance-sheet items (operating leases, contingent liabilities), and pension underfunding. Trigger…
stock-industry-review
Review a US-listed company's industry position and competitive moat for an equity-research workup. Covers Porter Five Forces scan, market-share trend (absolute and relative to industry growth), TAM size and trajectory, unit economics where disclosed (LTV/CAC, unit gross margin), moat classification (network / brand /…