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 charlieviettq/awesome-agent-skill --skill algo-hr-compensationgit clone --depth 1 https://github.com/charlieviettq/awesome-agent-skillWrote 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/charlieviettq/awesome-agent-skill/algo-hr-compensation)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/algo-hr-compensation"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-hr-compensation/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/charlieviettq/awesome-agent-skill/algo-hr-compensation"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-hr-compensation.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 | $0.00979 |
| Opus 5 | $0.00031 | $0.00490 |
| Sonnet 5 | $0.00012 | $0.00196 |
| Haiku 4.5 | $0.00006 | $0.00098 |
Grade A, and why
"algo-hr-compensation" 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
94% identical to algo-hr-compensation — 8 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compensation Benchmarking
Overview
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
IRON LAW: Benchmarking Is Only Valid With COMPARABLE Jobs
Matching by job TITLE alone is unreliable — "Senior Engineer" means
vastly different things at different companies. Match by: job content
(duties, scope), level (IC vs manager, experience band), industry,
geography, and company size. Poor job matching produces misleading
market rates.
Phase 1: Input Validation
Collect: internal compensation data (base, bonus, equity), market survey data (P25, P50, P75 by role), job matching between internal roles and survey benchmarks. Gate: Jobs properly matched, survey data current (< 18 months).
Phase 2: Core Algorithm
- Match internal jobs to market benchmarks by content, level, and scope
- Age survey data to current date: apply projected market movement rate
- Compute compa-ratio per employee: actual base / market P50
- Compute percentile positioning: where does actual pay fall in market distribution
- Analyze: by department, level, tenure, demographics for equity gaps
Phase 3: Verification
Check: compa-ratios cluster around 0.85-1.15 (normal range). Flag outliers (< 0.80 underpaid, > 1.20 overpaid). Test demographic equity. Gate: Distribution reasonable, equity analysis completed.
Phase 4: Output
Return benchmarking results with band recommendations.
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.
- 12d ago First seen · 89 lines · 62 tokens per session scan A 1c69a2978971
"algo-hr-compensation" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (25 stars, last pushed 1mo ago), licensed MIT. It adds 62 tokens to every session and 979 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to algo-hr-compensation, differing in 8 lines, and is treated as a copy.
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