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 wentorai/research-plugins --skill epidemiology-guidegit clone --depth 1 https://github.com/wentorai/research-pluginsWrote 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/wentorai/research-plugins/epidemiology-guide)<a href="https://agentmods.dev/skills/wentorai/research-plugins/epidemiology-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/epidemiology-guide/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/wentorai/research-plugins/epidemiology-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/epidemiology-guide.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 | $0.00022 | $0.01905 |
| Opus 5 | $0.00011 | $0.00953 |
| Sonnet 5 | $0.00004 | $0.00381 |
| Haiku 4.5 | $0.00002 | $0.00191 |
Grade A, and why
epidemiology-guide 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 6d 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 — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Epidemiology Guide
A skill for designing and analyzing epidemiological studies. Covers study design selection, measures of disease frequency and association, bias assessment, and public health data analysis methods.
Study Design Selection
Design Hierarchy
Evidence Strength
|
Systematic Review / Meta-Analysis (Highest)
|
Randomized Controlled Trial
|
Cohort Study (Prospective)
|
Case-Control Study
|
Cross-Sectional Study
|
Case Report / Case Series (Lowest)
When to Use Each Design
| Design | Research Question | Time | Cost | Bias Risk |
|---|---|---|---|---|
| RCT | Does intervention X prevent outcome Y? | Years | Very high | Lowest |
| Prospective Cohort | Does exposure X increase risk of Y? | Years | High | Moderate |
| Retrospective Cohort | Historical exposure-outcome relationship? | Months | Moderate | Moderate-High |
| Case-Control | What exposures are associated with rare disease? | Months | Low | High |
| Cross-Sectional | What is the prevalence of X? | Weeks | Low | High |
| Ecological | Do population-level factors correlate with disease? | Weeks | Very low | Very high |
Measures of Disease Frequency
import numpy as np
def compute_measures(cases: int, population: int,
person_time: float = None,
period_years: float = 1.0) -> dict:
"""
Compute basic epidemiological measures.
Args:
cases: Number of new cases (for incidence) or existing cases (for prevalence)
population: Population at risk
person_time: Person-years of follow-up (for incidence rate)
period_years: Time period in years (for cumulative incidence)
"""
measures = {}
# Point prevalence
measures['prevalence'] = {
'value': cases / population,
'per_1000': (cases / population) * 1000,
'formula': 'cases / population at a point in time'
}
# Cumulative incidence (risk)
measures['cumulative_incidence'] = {
'value': cases / population,
'per_1000': (cases / population) * 1000,
'period_years': period_years,
'formula': 'new cases / population at risk during time period'
}
# Incidence rate (if person-time available)
if person_time:
measures['incidence_rate'] = {
'value': cases / person_time,
'per_1000_py': (cases / person_time) * 1000,
'formula': 'new cases / person-time at risk'
}
return measures
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.
- 6d ago First seen · 201 lines · 22 tokens per session scan A 25c1c7fe57c7
epidemiology-guide is a skill published in the GitHub repository wentorai/research-plugins (291 stars, last pushed 2mo ago), licensed MIT. It adds 22 tokens to every session and 1,905 once invoked, about $0.0001 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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