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 latestaiagents/agent-skills --skill compensation-analysisgit clone --depth 1 https://github.com/latestaiagents/agent-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/latestaiagents/agent-skills/compensation-analysis)<a href="https://agentmods.dev/skills/latestaiagents/agent-skills/compensation-analysis"><img src="https://agentmods.dev/badge/skills/latestaiagents/agent-skills/compensation-analysis/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/latestaiagents/agent-skills/compensation-analysis"><img src="https://agentmods.dev/badge/skills/latestaiagents/agent-skills/compensation-analysis.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 | $0.00055 | $0.01483 |
| Opus 5 | $0.00028 | $0.00741 |
| Sonnet 5 | $0.00011 | $0.00297 |
| Haiku 4.5 | $0.00006 | $0.00148 |
Grade A, and why
compensation-analysis 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 5d 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 — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compensation Analysis
Build fair, competitive compensation structures.
When to Use
- Benchmarking salaries against market
- Creating or updating salary bands
- Analyzing pay equity
- Planning compensation reviews
- Building total rewards packages
Compensation Structure
Salary Band Framework
## Band Structure
| Level | Title Examples | Band Width | Typical Range |
|-------|---------------|------------|---------------|
| L1 | Associate, Junior | 20% | Entry level |
| L2 | Mid-level, Specialist | 25% | 2-4 years exp |
| L3 | Senior, Lead | 30% | 5-8 years exp |
| L4 | Staff, Principal | 35% | 8-12 years exp |
| L5 | Director, Senior Staff | 40% | 12+ years exp |
## Band Positioning
| Position | % of Midpoint | When to Use |
|----------|--------------|-------------|
| Below Min | <80% | Rarely, new to role |
| Min | 80% | New to level |
| Target | 90-100% | Fully competent |
| Midpoint | 100% | Market rate |
| Above Mid | 100-120% | High performer |
| Max | 120% | Exceptional, at cap |
Building Salary Bands
## Step 1: Market Data
- Gather salary data from 3+ sources
- Sources: Radford, Mercer, Levels.fyi, Glassdoor, Payscale
- Match to job families and levels
## Step 2: Determine Positioning
| Strategy | Market Position | When to Use |
|----------|----------------|-------------|
| Lead | 75th percentile | Talent-competitive roles |
| Match | 50th percentile | Standard roles |
| Lag | 25th percentile | Budget constraints |
## Step 3: Set Band Width
- Narrower bands (20%): Entry-level, structured roles
- Wider bands (40%): Senior, variable roles
## Step 4: Calculate Ranges
Midpoint = Market rate at target percentile
Min = Midpoint × (1 - Band Width/2)
Max = Midpoint × (1 + Band Width/2)
Example (30% band, $100K midpoint):
- Min: $100K × 0.85 = $85,000
- Max: $100K × 1.15 = $115,000
Pay Equity Analysis
Analysis Framework
## Step 1: Data Collection
Required fields:
- Base salary
- Job level/band
- Department
- Location
- Tenure
- Gender
- Race/ethnicity (where legally collected)
- Performance rating
## Step 2: Group Comparison
Compare pay within:
- Same job level
- Same department
- Same location
- Similar tenure
## Step 3: Statistical Analysis
- Calculate pay gap percentages
- Run regression analysis controlling for:
- Job level
- Experience
- Performance
- Location
- Education (if relevant)
## Step 4: Identify Outliers
Flag individuals who are:
- >5% below expected pay
- >10% above expected pay
- Unexplained by legitimate factors
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.
- 5d ago First seen · 237 lines · 55 tokens per session scan A f38284e1542c
compensation-analysis is a skill published in the GitHub repository latestaiagents/agent-skills (5 stars, last pushed 4mo ago), licensed MIT. It adds 55 tokens to every session and 1,483 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 2026-09-03.
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