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 Aperivue/medsci-skills --skill cross-nationalgit clone --depth 1 https://github.com/Aperivue/medsci-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/aperivue/medsci-skills/cross-national)<a href="https://agentmods.dev/skills/aperivue/medsci-skills/cross-national"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/cross-national/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/aperivue/medsci-skills/cross-national"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/cross-national.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 6 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00061 | $0.04673 |
| Opus 5 | $0.00030 | $0.02337 |
| Sonnet 5 | $0.00012 | $0.00935 |
| Haiku 4.5 | $0.00006 | $0.00467 |
Grade A, and why
cross-national 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.
How it starts
The opening of the file, as written. The whole thing — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cross-National Comparison Study Skill
You are assisting a medical researcher in conducting a cross-national comparison study using parallel nationally representative surveys (e.g., KNHANES for Korea, NHANES for the US, CHNS for China).
When to Use
- Researcher has a clinical question to compare across two countries
- KNHANES + NHANES data available (or other parallel survey pairs)
- Goal: produce a complete analysis with country-stratified results + comparison table
Inputs
- Research question: exposure → outcome association to compare across countries
- Korean data path: KNHANES CSV file
- US data path: NHANES CSV directory (multiple tables to merge)
- Harmonization table (optional): CSV mapping variables across surveys
- Default: replicate-study skill's
harmonization_knhanes_nhanes.csv
- Default: replicate-study skill's
Reference Files
- Harmonization table:
medsci-skills/skills/replicate-study/references/harmonization_knhanes_nhanes.csv - Upstream:
medsci-skills/skills/write-paper/references/paper_types/cross_national.md— writing templatemedsci-skills/skills/analyze-stats/references/analysis_guides/survey_weighted.md
Workflow
Phase 1: Study Definition
- Confirm research question: Exposure → Outcome
- Define variable coding for both countries:
- Exposure: PHQ-9, BMI category, smoking, etc.
- Outcome: diabetes, hypertension, mortality, etc.
- Covariates: age, sex, education, income, smoking, alcohol, obesity, CVD
- Check harmonization table for variable availability
- Output: study protocol summary for user approval
Phase 2: Data Preparation
KNHANES (single CSV):
- Load CSV, filter age ≥20 (or per protocol)
- Derive variables using KNHANES coding:
- Smoking: BS3_1 (1,2=current, 3=former, 8=never)
- Alcohol: BD1_11 (2-6=frequent, 1=occasional, 8=never)
- Obesity: HE_obe (≥4=obesity for BMI≥25 Asian cutoff)
- PHQ-9: BP_PHQ_1~9, sum score, ≥10=depression
- Diabetes: HE_glu≥126 | HE_HbA1c≥6.5 | DE1_dg=1
- CVD: DI4_dg=1 | DI5_dg=1 | DI6_dg=1
- Set survey design: svydesign(id=~psu, strata=~kstrata, weights=~wt_itvex, nest=TRUE)
What ships with it
1 file 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 · 265 lines · 61 tokens per session scan A 0bd9581d2033
cross-national is a skill published in the GitHub repository Aperivue/medsci-skills (292 stars, last pushed 4d ago), licensed MIT. It adds 61 tokens to every session and 4,673 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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