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 tuanductran/hr-skills --skill hr-salary-benchmarkinggit clone --depth 1 https://github.com/tuanductran/hr-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/tuanductran/hr-skills/hr-salary-benchmarking)<a href="https://agentmods.dev/skills/tuanductran/hr-skills/hr-salary-benchmarking"><img src="https://agentmods.dev/badge/skills/tuanductran/hr-skills/hr-salary-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/tuanductran/hr-skills/hr-salary-benchmarking"><img src="https://agentmods.dev/badge/skills/tuanductran/hr-skills/hr-salary-benchmarking.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 63 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00066 | $0.00808 |
| Opus 5 | $0.00033 | $0.00404 |
| Sonnet 5 | $0.00013 | $0.00162 |
| Haiku 4.5 | $0.00007 | $0.00081 |
Grade A, and why
hr-salary-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 9d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Salary benchmarking
Benchmark pay against external market data to set competitive, defensible salary ranges — selecting the right data sources, matching roles accurately, and translating market data into internal pay bands.
Supported tasks
- Selecting appropriate salary survey and market data sources by role and market
- Matching internal roles accurately to external benchmark job codes
- Building salary ranges from benchmark data adjusted for geography and level
- Assessing whether current pay is competitive against market percentiles
- Benchmarking total compensation, not just base salary, against market
- Handling roles with thin or unreliable market data
- Refreshing salary benchmarks on a regular cadence
- Presenting benchmarking findings to leadership for pay decisions
- Benchmarking pay across multiple countries or regions consistently
- Reconciling internal pay equity considerations with market benchmark data
- Building a benchmarking methodology document for compensation governance
- Comparing benchmarking vendors and data sources for cost and reliability
Key prompts
Selecting data and matching roles
- "What salary survey or market data sources are most appropriate for benchmarking [role] in [industry/region]?"
- "How should we accurately match our internal [role] to the closest external benchmark job code, given differences in scope?"
- "Compare the reliability and cost trade-offs of [benchmarking vendor A] vs. [benchmarking vendor B] for [role type]."
- "How often should we refresh benchmark data for [fast-moving role/industry] versus a more stable function?"
Building ranges
- "Build a salary range for [role] in [location] using [percentile target] against current market benchmark data."
- "Adjust this benchmark-derived salary range for [level/geography] differences from the base survey data."
- "How should we handle benchmarking for a role with thin or unreliable external market data?"
- "How do we set ranges for a hybrid role that spans two distinct market benchmark job codes?"
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 65 lines · 66 tokens per session scan A f170f9803428
hr-salary-benchmarking is a skill published in the GitHub repository tuanductran/hr-skills (57 stars, last pushed 2d ago), licensed MIT. It adds 66 tokens to every session and 808 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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