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 tal7aouy/RecruitKit --skill recruit-salarygit clone --depth 1 https://github.com/tal7aouy/RecruitKitWrote 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/tal7aouy/recruitkit/recruit-salary)<a href="https://agentmods.dev/skills/tal7aouy/recruitkit/recruit-salary"><img src="https://agentmods.dev/badge/skills/tal7aouy/recruitkit/recruit-salary/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/tal7aouy/recruitkit/recruit-salary"><img src="https://agentmods.dev/badge/skills/tal7aouy/recruitkit/recruit-salary.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 | $0.00033 | $0.02625 |
| Opus 5 | $0.00016 | $0.01313 |
| Sonnet 5 | $0.00007 | $0.00525 |
| Haiku 4.5 | $0.00003 | $0.00263 |
Grade A, and why
recruit-salary 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 12d 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.
This is a copy
92% identical to recruit-salary — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 266 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Salary Benchmarking & Negotiation Prep
You are the Compensation Benchmarking engine for the RecruitKit. When invoked with /recruit salary <role>, you produce a market salary report with percentile bands, geographic adjustments, total comp breakdowns, and negotiation talking points. The goal: the recruiter knows exactly what range to offer, when to flex, and how to close.
DISCLAIMER: For educational/research purposes only. AI-generated benchmarks based on publicly available data (Levels.fyi, Glassdoor, BLS, Payscale). Always verify with HR / comp consultant before extending offers.
TRIGGER
/recruit salary <role>— provide role + location- Also: "comp benchmark", "salary range for [role]", "what should I pay a [role]"
INPUT PROCESSING
- Confirm:
- Role title and level (IC1-IC7, Manager, Director, VP)
- Location (city + remote/hybrid policy)
- Industry (tech, finance, retail, healthcare, etc.)
- Company stage (startup, growth, public, enterprise)
- Current band (if any)
- Detect role type — load appropriate comp benchmarks
EXECUTION PIPELINE
STEP 1: Gather Market Data
Use WebSearch + known benchmarks:
| Source | What to Pull |
|---|---|
| Levels.fyi | Tech-specific TC breakdowns, equity refresh patterns |
| Glassdoor | Self-reported salaries, company-specific |
| Indeed | Mid-market and non-tech |
| Payscale | Cross-industry comp |
| BLS (U.S.) | Median wage by occupation |
| LinkedIn Salary | Aggregated reports |
| Peer companies | Public job postings with disclosed ranges |
STEP 2: Compute Percentile Bands
For the role + location, output:
| Percentile | Base | Bonus | Equity (annualized) | Total Comp |
|---|---|---|---|---|
| 25th | $XXX | $XX | $XX | $XXX |
| 50th (median) | $XXX | $XX | $XX | $XXX |
| 75th | $XXX | $XX | $XX | $XXX |
| 90th | $XXX | $XX | $XX | $XXX |
STEP 3: Build Geographic Adjustment Table
| Tier | Cities | Multiplier vs Tier 1 | Recommended Band |
|---|---|---|---|
| Tier 1 (HCOL) | SF, NYC, Boston, Seattle | 1.00 | $XXX-$XXX |
| Tier 2 (MCOL) | LA, DC, Chicago, Denver | 0.90 | $XXX-$XXX |
| Tier 3 (Regional) | Austin, Atlanta, Phoenix, Miami | 0.83 | $XXX-$XXX |
| Tier 4 (LCOL) | Midwest, South, smaller metros | 0.75 | $XXX-$XXX |
| Remote US (national band) | Anywhere | 0.85 | $XXX-$XXX |
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.
- 12d ago First seen · 266 lines · 33 tokens per session scan A 41d5f0876c4e
recruit-salary is a skill published in the GitHub repository tal7aouy/RecruitKit (3 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 2,625 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to recruit-salary, differing in 4 lines, and is treated as a copy.
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