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 90le/worker-rights-cn --skill compensation-calculatorgit clone --depth 1 https://github.com/90le/worker-rights-cnWrote 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/90le/worker-rights-cn/compensation-calculator)<a href="https://agentmods.dev/skills/90le/worker-rights-cn/compensation-calculator"><img src="https://agentmods.dev/badge/skills/90le/worker-rights-cn/compensation-calculator/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/90le/worker-rights-cn/compensation-calculator"><img src="https://agentmods.dev/badge/skills/90le/worker-rights-cn/compensation-calculator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00064 | $0.01138 |
| Opus 5 | $0.00032 | $0.00569 |
| Sonnet 5 | $0.00013 | $0.00228 |
| Haiku 4.5 | $0.00006 | $0.00114 |
Grade A, and why
compensation-calculator 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 11d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compensation Calculator
Overview
Estimate monetary claims with a deterministic script where possible. Do not rely on free-form arithmetic for core amounts.
Required Facts
Before calculating, collect:
- Work start date.
- End date or expected termination date.
- Average monthly wage for the last 12 months or actual shorter period.
- City or region.
- Local average monthly wage if wage-cap analysis is needed.
- Termination path: mutual, non-fault without notice, economic layoff, unlawful, unknown.
- Previous month's wage if claiming substitute notice wage under a non-fault dismissal path.
- Unpaid wages, unused annual leave days, unsigned-contract months, overtime amount, if claimed.
If wage or dates are missing, output missing inputs instead of estimating.
Script Use
Use scripts/calculate_compensation.py for deterministic baseline calculations:
python3 scripts/calculate_compensation.py --input case.json
To derive the wage base from up to 12 monthly payroll records without saving them, use a UTF-8 CSV with exactly month,gross_wage columns:
python3 scripts/calculate_compensation.py --input case.json --payroll-csv payroll.csv
The result includes the source SHA-256, normalized monthly rows, missing-month list, total, average, and calculation formula. The CSV average replaces any average_monthly_wage supplied in the JSON; verify missing months and the legally applicable wage-base period before relying on it.
For a standard-hours overtime estimate, set work_schedule_type to standard and provide an explicit overtime_monthly_wage_base in the JSON. Then import a UTF-8 CSV with exactly work_date,started_at,ended_at,break_minutes,day_type,compensatory_leave_minutes:
python3 scripts/calculate_compensation.py --input case.json --attendance-csv attendance.csv
day_type is workday, rest_day, or statutory_holiday. Timestamps use local YYYY-MM-DDTHH:MM. The script rejects overlaps, aggregates split shifts by work date, preserves the source SHA-256, and emits each daily formula and amount. It supports only standard hours; comprehensive or flexible schedules require separate approval documents and local analysis. Attendance rows and day labels remain worker-provided facts that must be checked against original records and employer-arrangement evidence.
What ships with it
8 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.
- 11d ago First seen · 105 lines · 64 tokens per session scan A ae5523caf928
compensation-calculator is a skill published in the GitHub repository 90le/worker-rights-cn (94 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 64 tokens to every session and 1,138 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.
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