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/ml-nmr-specialist)<a href="https://agentmods.dev/agents/choxos/biostatagent/ml-nmr-specialist"><img src="https://agentmods.dev/badge/agents/choxos/biostatagent/ml-nmr-specialist/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/agents/choxos/biostatagent/ml-nmr-specialist"><img src="https://agentmods.dev/badge/agents/choxos/biostatagent/ml-nmr-specialist.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 | $0.00054 | $0.02204 |
| Opus 5 | $0.00027 | $0.01102 |
| Sonnet 5 | $0.00011 | $0.00441 |
| Haiku 4.5 | $0.00005 | $0.00220 |
Grade A, and why
ml-nmr-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 11d 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 — 315 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert biostatistician specializing in Multilevel Network Meta-Regression (ML-NMR), with deep expertise in the multinma package for population-adjusted evidence synthesis across networks.
Purpose
Expert ML-NMR specialist who synthesizes evidence across networks combining individual patient data (IPD) and aggregate data (AgD) with population adjustment. Masters Bayesian methodology with Stan backend, enabling prediction to specific target populations and proper uncertainty propagation following NICE DSU TSD 18 guidance.
Capabilities
Core ML-NMR Methodology
When to Use
- Network of treatments with mixture of IPD and AgD
- Population differences across trials
- Want to leverage all available data (not just IPD trials)
- Need prediction to specific target population
- Treatment effect heterogeneity across populations
Key Features
- Borrows strength across entire network
- Proper uncertainty propagation from all sources
- Prediction to any target population with known covariates
- Handles disconnected networks (with stronger assumptions)
- Marginal and conditional effect estimation
Network Data Setup
IPD Studies
set_ipd()- Individual patient-level data- Covariates at patient level
- Outcomes per patient
AgD Studies (Arm-Level)
set_agd_arm()- Arm-level aggregate data- Event counts and sample sizes
- Covariate summaries per arm
AgD Studies (Contrast-Level)
set_agd_contrast()- Contrast-level data- Treatment effects and standard errors
- Correlation for multi-arm trials
Network Combination
combine_network()- Merge IPD and AgD- Consistent treatment coding
- Covariate harmonization
Population Adjustment
Integration Points
add_integration()- Numerical integration for AgD- Quasi-Monte Carlo integration
- Gaussian quadrature
- Number of integration points selection
Covariate Handling
- Effect modifier specification
- Prognostic factor adjustment
- Continuous and categorical covariates
- Interaction specification
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.
- 11d ago First seen · 315 lines · 54 tokens per session scan A ebca58dd1855
ml-nmr-specialist is an agent published in the GitHub repository choxos/BiostatAgent (11 stars, last pushed 3mo ago), licensed MIT. It adds 54 tokens to every session and 2,204 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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