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/commands/choxos/biostatagent/create-model)<a href="https://agentmods.dev/commands/choxos/biostatagent/create-model"><img src="https://agentmods.dev/badge/commands/choxos/biostatagent/create-model/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/commands/choxos/biostatagent/create-model"><img src="https://agentmods.dev/badge/commands/choxos/biostatagent/create-model.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 | $0.00022 | $0.00796 |
| Opus 5.5 | $0.00009 | $0.00318 |
| Sonnet 5.5 | $0.00004 | $0.00159 |
| Haiku 4.5 | $0.00002 | $0.00080 |
Grade A, and why
create-model 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bayesian Model Creation Workflow
You are helping the user create a Bayesian model. Follow this structured workflow:
Step 1: Gather Requirements
Ask the user to specify:
-
Model Type (select one):
- Hierarchical/Multilevel model
- Regression model (linear, logistic, Poisson, etc.)
- Time series model (AR, state-space, etc.)
- Survival analysis model
- Meta-analysis model
-
Target Language:
- Stan with cmdstanr (DEFAULT for R - recommended)
- PyMC with ArviZ (DEFAULT for Python)
- JAGS with R2jags
- WinBUGS with R2WinBUGS (Windows only)
-
Experience Level:
- Beginner (extensive comments, educational explanations)
- Intermediate (standard documentation)
- Advanced (minimal comments, efficiency-focused)
-
Data Description:
- Outcome variable type (continuous, binary, count, time-to-event)
- Predictor variables
- Grouping structure (if hierarchical)
- Sample sizes
-
Prior Preferences (optional):
- Specific prior distributions
- Informative vs weakly informative
- Domain-specific constraints
Step 2: Route to Specialist
Based on the target language:
-
Stan: Use @stan-specialist with skills:
stan-fundamentalsfor syntax- Appropriate model type skill (hierarchical-models, regression-models, etc.)
-
PyMC: Use @pymc-specialist with skills:
pymc-fundamentalsfor syntax- Appropriate model type skill
-
JAGS/WinBUGS: Use @bugs-specialist with skills:
bugs-fundamentalsfor syntax- Appropriate model type skill
Step 3: Generate Model
The specialist will provide:
-
Complete model code with appropriate comments based on experience level
-
Integration code (R or Python):
- Data preparation
- Model compilation/fitting
- Basic diagnostics (posterior/ArviZ)
-
Generated quantities for:
- Posterior predictive checks
- Derived quantities of interest
Step 4: Validate Output
Before presenting to user, verify:
- Model syntax is correct for target language
- All parameters have priors
- Parameterization is correct (SD for Stan/PyMC, precision for BUGS)
- Integration code is complete and runnable (R or Python)
- Comments match experience level
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 · 107 lines · 22 tokens per session scan A 3f9a1b62f5eb
create-model is a command published in the GitHub repository choxos/BiostatAgent (12 stars, last pushed 4mo ago), licensed MIT. It adds 22 tokens to every session and 796 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.
Other commands, from other repositories
openehr-explain
One-stop router that explains or looks up any openEHR thing — auto-detects an archetype, a template, an RM/AM/BASE type, an RM structural concept, an ADL idiom, an AQL query or keyword, or a terminology code (replaces /archetype-explain, /template-explain, /type-spec, /rm-structure, /adl-idiom, /terminology).
create-scene
Create a single animated scene for mathematical visualization.
srs-sm
Draft and QC the Supplementary Materials: scope approval → SM writing → figure QC for SM figures.
ars-lit-review
ARS academic-paper lit-review mode — annotated bibliography in paper format.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.