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 choxos/BiostatAgent --skill clinical-trial-design-patternsgit clone --depth 1 https://github.com/choxos/BiostatAgentWrote 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/choxos/biostatagent/clinical-trial-design-patterns)<a href="https://agentmods.dev/skills/choxos/biostatagent/clinical-trial-design-patterns"><img src="https://agentmods.dev/badge/skills/choxos/biostatagent/clinical-trial-design-patterns/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/choxos/biostatagent/clinical-trial-design-patterns"><img src="https://agentmods.dev/badge/skills/choxos/biostatagent/clinical-trial-design-patterns.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 | $0.00035 | $0.02632 |
| Opus 5.5 | $0.00014 | $0.01053 |
| Sonnet 5.5 | $0.00007 | $0.00526 |
| Haiku 4.5 | $0.00003 | $0.00263 |
Grade A, and why
clinical-trial-design-patterns 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 2mo 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 — 333 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clinical Trial Design Patterns
When to Use This Skill
- Selecting appropriate trial design for clinical objectives
- Implementing multi-arm or multi-endpoint trials
- Designing stratified trials
- Planning adaptive designs
- Understanding design trade-offs
Two-Arm Parallel Design
Standard Design
The most common design: randomize patients to treatment or control.
# simtrial implementation
sim_pw_surv(
n = 400,
block = c(rep("control", 1), rep("experimental", 1)), # 1:1
enroll_rate = data.frame(rate = 20, duration = 12),
fail_rate = fail_rate
)
# Mediana implementation
DataModel() +
OutcomeDist(outcome.dist = "NormalDist") +
SampleSize(200) + # Per arm
Sample(id = "Control", outcome.par = parameters(mean = 0, sd = 1)) +
Sample(id = "Treatment", outcome.par = parameters(mean = 0.5, sd = 1))
Unequal Randomization
When to Use:
- Increase exposure to experimental treatment
- Ethical considerations
- Resource optimization
# 2:1 randomization (experimental:control)
sim_pw_surv(
n = 300,
block = c("control", rep("experimental", 2))
)
# Mediana with unequal allocation
DataModel() +
Sample(id = "Control", sample.size = 100, ...) +
Sample(id = "Treatment", sample.size = 200, ...)
Trade-off: Unequal allocation reduces power for same total N.
Multi-Arm Designs
Dose-Finding (Multiple Doses vs Placebo)
# Three doses + placebo
DataModel() +
OutcomeDist(outcome.dist = "NormalDist") +
SampleSize(75) + # Per arm
Sample(id = "Placebo", outcome.par = parameters(mean = 0, sd = 1)) +
Sample(id = "Low Dose", outcome.par = parameters(mean = 0.3, sd = 1)) +
Sample(id = "Mid Dose", outcome.par = parameters(mean = 0.5, sd = 1)) +
Sample(id = "High Dose", outcome.par = parameters(mean = 0.7, sd = 1))
# Analysis with Dunnett-type comparison
AnalysisModel() +
Test(id = "Low vs Placebo", samples = samples("Placebo", "Low Dose"), method = "TTest") +
Test(id = "Mid vs Placebo", samples = samples("Placebo", "Mid Dose"), method = "TTest") +
Test(id = "High vs Placebo", samples = samples("Placebo", "High Dose"), method = "TTest") +
MultAdjProc(proc = "HolmAdj")
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.
- 2mo ago First seen · 333 lines · 35 tokens per session scan A bfddcc1843cb
clinical-trial-design-patterns is a skill published in the GitHub repository choxos/BiostatAgent (12 stars, last pushed 5mo ago), licensed MIT. It adds 35 tokens to every session and 2,632 once invoked, about $0.0001 per session on Opus 5.5. 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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