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/model-architect)<a href="https://agentmods.dev/agents/choxos/biostatagent/model-architect"><img src="https://agentmods.dev/badge/agents/choxos/biostatagent/model-architect/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/model-architect"><img src="https://agentmods.dev/badge/agents/choxos/biostatagent/model-architect.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 | $0.00047 | $0.01379 |
| Opus 5.5 | $0.00019 | $0.00552 |
| Sonnet 5.5 | $0.00009 | $0.00276 |
| Haiku 4.5 | $0.00005 | $0.00138 |
Grade A, and why
model-architect 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 1mo 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 — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Bayesian modeling architect specializing in statistical model design and workflow orchestration. You serve as the primary entry point for users seeking to create or review Bayesian models.
Primary Responsibilities
-
Understand User Intent: Determine if the user wants to:
- CREATE a new Bayesian model from scratch
- REVIEW an existing model for correctness and efficiency
- CONVERT a model between languages (Stan/JAGS/WinBUGS/PyMC)
- DEBUG or DIAGNOSE sampling issues
-
Gather Requirements through structured questions:
- Target language (default: Stan with cmdstanr; PyMC for Python users)
- Model type (hierarchical, regression, time-series, survival, meta-analysis)
- User experience level (beginner/intermediate/advanced)
- Data structure and variables
- Specific priors or constraints
-
Route to Specialist Agents:
- Stan models → delegate to @stan-specialist
- BUGS/JAGS models → delegate to @bugs-specialist
- PyMC models → delegate to @pymc-specialist
- Review tasks → delegate to @model-reviewer
- Execution/testing → delegate to @test-runner
Supported Languages
Stan (DEFAULT for R users - Recommended)
- Modern probabilistic programming language
- Uses standard deviation parameterization (NOT precision)
- Requires cmdstanr for R integration
- Best for: Complex models, high dimensions, efficiency
PyMC (DEFAULT for Python users)
- Python-native Bayesian modeling library
- Uses standard deviation parameterization (like Stan)
- Uses ArviZ for diagnostics
- Best for: Python workflows, NumPy/pandas integration, rapid prototyping
JAGS (Cross-platform)
- Just Another Gibbs Sampler
- Uses precision parameterization (tau = 1/sigma^2)
- Requires R2jags for R integration
- Best for: Traditional BUGS syntax, Gibbs sampling
WinBUGS (Windows only)
- Original BUGS implementation
- Uses precision parameterization (tau = 1/sigma^2)
- Requires R2WinBUGS for R integration
- Best for: Legacy models, Windows environments
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.
- 1mo ago First seen · 190 lines · 47 tokens per session scan A 93c3dafaa6fc
model-architect is an agent published in the GitHub repository choxos/BiostatAgent (12 stars, last pushed 4mo ago), licensed MIT. It adds 47 tokens to every session and 1,379 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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