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 simtrial-fundamentalsgit 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/simtrial-fundamentals)<a href="https://agentmods.dev/skills/choxos/biostatagent/simtrial-fundamentals"><img src="https://agentmods.dev/badge/skills/choxos/biostatagent/simtrial-fundamentals/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/simtrial-fundamentals"><img src="https://agentmods.dev/badge/skills/choxos/biostatagent/simtrial-fundamentals.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 | $0.00038 | $0.03274 |
| Opus 5.5 | $0.00015 | $0.01310 |
| Sonnet 5.5 | $0.00008 | $0.00655 |
| Haiku 4.5 | $0.00004 | $0.00327 |
Grade A, and why
simtrial-fundamentals 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 — 465 lines — stays where its author put it; the contents beside it link to each section on GitHub.
simtrial Fundamentals
When to Use This Skill
- Simulating time-to-event (survival) clinical trial data
- Generating piecewise exponential failure/dropout times
- Modeling delayed treatment effects or non-proportional hazards
- Performing weighted logrank tests (Fleming-Harrington, Magirr-Burman)
- Running MaxCombo tests for non-proportional hazards
- Simulating group sequential designs
- Calculating RMST or milestone endpoints
Package Overview
simtrial by Merck provides fast, extensible clinical trial simulation for time-to-event endpoints. Key features:
- Piecewise exponential distributions for flexible hazard modeling
- Built-in support for non-proportional hazards scenarios
- Integration with gsDesign2 for group sequential designs
- Parallel computation via doFuture/foreach
- Pipe-friendly API using data.table for performance
Core Data Generation Functions
sim_pw_surv() - Main Simulation Function
Generates stratified time-to-event outcome randomized trial data.
sim_pw_surv(
n = 100, # Total sample size
stratum = data.frame( # Stratum definitions
stratum = "All",
p = 1 # Prevalence/probability
),
block = c(rep("control", 2), rep("experimental", 2)), # Randomization block
enroll_rate = data.frame( # Enrollment rates by period
rate = 9,
duration = 1
),
fail_rate = data.frame( # Failure rates by stratum/treatment/period
stratum = rep("All", 4),
period = rep(1:2, 2),
treatment = c(rep("control", 2), rep("experimental", 2)),
duration = rep(c(3, 1), 2),
rate = log(2) / c(9, 9, 9, 18) # Hazard rates
),
dropout_rate = data.frame( # Dropout rates
stratum = rep("All", 2),
period = rep(1, 2),
treatment = c("control", "experimental"),
duration = rep(100, 2),
rate = rep(0.001, 2)
)
)
Returns: Data frame with columns:
stratum: Patient stratumenroll_time: Calendar time of enrollmenttreatment: Treatment assignment ("control" or "experimental")fail_time: Time from enrollment to eventdropout_time: Time from enrollment to dropoutcte: Calendar time of event (enroll_time + min(fail_time, dropout_time))fail: Event indicator (1 = event, 0 = censored)
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 · 465 lines · 38 tokens per session scan A 40925e34986e
simtrial-fundamentals is a skill published in the GitHub repository choxos/BiostatAgent (12 stars, last pushed 5mo ago), licensed MIT. It adds 38 tokens to every session and 3,274 once invoked, about $0.0002 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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