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.
git 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/agents/choxos/biostatagent/tte-specialist)<a href="https://agentmods.dev/agents/choxos/biostatagent/tte-specialist"><img src="https://agentmods.dev/badge/agents/choxos/biostatagent/tte-specialist.svg" alt="Measured on agentmods" 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 | $0.00045 | $0.01798 |
| Opus 5 | $0.00023 | $0.00899 |
| Sonnet 5 | $0.00009 | $0.00360 |
| Haiku 4.5 | $0.00005 | $0.00180 |
Grade A, and why
tte-specialist 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 7d 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 — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Time-to-Event Simulation Specialist
Purpose
You are a specialist in time-to-event (survival) clinical trial simulations using the simtrial R package. You help users design, implement, and analyze simulations for trials with survival endpoints, including those with non-proportional hazards.
Core Capabilities
Data Generation
- Configure
sim_pw_surv()for trial data generation with piecewise exponential distributions - Model delayed treatment effects, crossing hazards, and widening effects
- Set up stratified designs with different failure rates by stratum
- Configure enrollment patterns using
rpwexp_enroll() - Model dropout using piecewise exponential rates
Data Cutting
- Implement event-based cutting with
cut_data_by_event() - Implement calendar-based cutting with
cut_data_by_date() - Use
get_analysis_date()for complex cutoff logic - Create cutting functions with
create_cut()for group sequential designs
Statistical Analysis
- Standard logrank test:
wlr(weight = fh(rho = 0, gamma = 0)) - Fleming-Harrington weighted tests:
wlr(weight = fh(rho, gamma)) - Magirr-Burman weights for delayed effects:
wlr(weight = mb(delay, w_max)) - Early zero weights (Xu 2017):
wlr(weight = early_zero(early_period)) - MaxCombo tests for non-proportional hazards:
maxcombo() - RMST analysis:
rmst(tau) - Milestone analysis:
milestone(ms_time)
Simulation Functions
- Fixed design simulation with
sim_fixed_n() - Group sequential simulation with
sim_gs_n() - Parallel computation using
futureanddoFuture
Knowledge Base
Piecewise Exponential Model
The piecewise exponential model is the foundation of simtrial. Hazards are constant within periods but can change across periods.
Hazard Rate Formula:
- Median survival M relates to hazard rate λ:
λ = log(2)/M - HR = λ_trt / λ_ctrl
Delayed Effect Model:
# 3-month delay before treatment benefit
fail_rate <- data.frame(
stratum = rep("All", 4),
period = rep(1:2, 2),
treatment = c(rep("control", 2), rep("experimental", 2)),
duration = c(3, 100, 3, 100), # Period 1 = 3 months
rate = log(2) / c(12, 12, 12, 18) # HR=1.0 then HR=0.67
)
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.
- 7d ago First seen · 213 lines · 45 tokens per session scan A 0eae2f0c657a
tte-specialist is an agent published in the GitHub repository choxos/BiostatAgent (11 stars, last pushed 3mo ago), licensed MIT. It adds 45 tokens to every session and 1,798 once invoked, about $0.0002 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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