R Tidy Modeling
Expert agents for R data science and biostatistics covering data wrangling, feature engineering, model building, statistical evaluation, clinical trials, genomics, documentation…
Plugin bundle · by Ahmad Sofi-Mahmudi
About
R Tidy Modeling is a plugin bundle for AI coding tools published by Ahmad Sofi-Mahmudi. Marketplaces we read that list it: 1 (Community GitHub marketplaces). We first saw it on 2026-10-15 and have recorded no change since. As of our marketplace pass on 2026-10-16.
Expert agents for R data science and biostatistics covering data wrangling, feature engineering, model building, statistical evaluation, clinical trials, genomics, documentation generation, and reproducible reporting using tidyverse and tidymodels
What it asks for
- Installs as a plugin bundle
None of the marketplaces we read publishes its tools or the settings it asks for.
What it declares, asks for and runs, in full
Only install plugins from developers you trust. We list what marketplaces publish and do not test plugins.
Works with
- Claude Code
From the marketplaces that list it and the MCP transport it declares, as each client documents them. We do not test plugins.
Connect
Commands and configs built only from what its marketplaces declare. We do not run them for you and do not test plugins.
As a Claude Code plugin
Built from what Community GitHub marketplaces declares.
/plugin marketplace add choxos/biostatagent
/plugin install r-tidy-modeling@biostat-agent
Where it is published
| Marketplace | Status | Version | Figures | Verified | First seen |
|---|---|---|---|---|---|
| Community marketplace choxos/biostatagent (Open the source on GitHub) | Listed | 1.0.0 | Forks (GitHub): 1 Stars (GitHub): 12 |
Not marked |
Each figure is the marketplace's own and is never added up across marketplaces. A marketplace that publishes no figure shows none: no number is not zero.
Listed in one community marketplace
| Marketplace | Repository | Stars (GitHub) |
|---|---|---|
| biostat-agent | choxos/biostatagent | 12 |
What the bundle contains
As published on Community GitHub marketplaces.
Skills: 23
advanced-adaptive-trialsbayesian-modelingcausal-mediationclinical-trialsdiagnostic-accuracyepidemiology-methodsgenomics-analysishealth-economicsipd-meta-analysismendelian-randomizationmeta-analysismodel-evaluation
Show the remaining 11
model-tuningnetwork-meta-analysispharmacokineticsr-documentation-patternsreal-world-evidencerecipes-patternsresampling-strategiesroxygen2-pkgdownsurvival-analysistidymodels-review-patternstidymodels-workflow
Commands: 7
r-analysisr-clinical-analysisr-code-reviewr-doc-generater-model-comparisonr-project-setupr-tutorial-create
Agents: 10
biostatisticiandata-wranglerfeature-engineerr-code-reviewerr-data-architectr-docs-architectr-tutorial-engineerreporting-engineertidymodels-engineerviz-specialist
What changed
- Became a product we track
- Community GitHub marketplaces: appeared
Similar plugins
Products whose own names, descriptions, tools and skills are closest in meaning to this one, on every marketplace we read. Closeness is ranked within this list only.
-
Expert agents for R data science and biostatistics covering data wrangling, feature engineering, model building, statistical evaluation, clinical trials, genomics, documentation…
-
Comprehensive R programming assistant for Claude Code — tidyverse-first data analysis, package development, Shiny, statistics, and biostatistics.
-
End-to-end data analysis workflow plugin for Claude Code. Covers the full pipeline from EDA to model training - EDA, preprocessing, statistical analysis, feature engineering, and…
-
Data science and ML workflow tools. 9 agents, 8 commands, 19 skills, 9 templates for problem framing, preprocessing, validation, EDA, experimentation, review, deployment, and…
-
Connect RStudio to AI assistants for interactive R coding and data analysis.
-
A causal inference plugin for AI agents. Describe a problem in plain language and it helps you choose a method, check assumptions, write the analysis in R or Python, and…
-
Valid and reliable data engineering and statistical analysis without hallucinations.
-
Run Stata from Claude Code through the stata-code MCP server (23 tools, 14 workflow prompts, typed errors, token-economy logs/graphs, multi-session) and an agent skill that…
-
Decision-grade data science: audits, leakage-safe experiments, diagnosis, validation, reproducible handoff.
-
DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and…
Similar plugins by marketplace
The same question asked of one marketplace at a time: is there an analog of this plugin there?
Has a listing on Claude Code: claude-plugins-community (5)
-
End-to-end data analysis workflow plugin for Claude Code. Covers the full pipeline from EDA to model training - EDA, preprocessing, statistical analysis, feature engineering, and…
-
A causal inference plugin for AI agents. Describe a problem in plain language and it helps you choose a method, check assumptions, write the analysis in R or Python, and…
-
197 ready-to-use life sciences skills that turn Claude into a domain expert for genomics, proteomics, drug discovery, biostatistics, scientific computing, and scientific writing…
-
Principles and rules for writing faithful scientific code. Scientific code serves truth, not users. This plugin teaches Claude the fundamental differences between scientific and…
-
DataRobot Agent Skills - Agentic skills for DataRobot enterprise AI and ML workflows. Covers model training, deployment, predictions, feature engineering, monitoring…
Has a listing on Claude Code: claude-plugins-official (5)
-
DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and…
-
Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams
-
Skills for tracing, evaluating, and improving AI agents with MLflow. Supports the full agent improvement loop: instrument → trace → evaluate → iterate → validate.
-
Build multimodal AI applications with Pixeltable -- tables, computed columns, embedding search, UDFs, tool-calling agents, and 25+ AI provider integrations.
-
Skills for adding DeepEval evaluations, tracing, datasets, Confident AI reports, and iterative improvement loops to AI applications.
Has a listing on Claude: knowledge-work-plugins (5)
-
DataRobot skills for AI/ML workflows — model training, deployment, predictions, feature engineering, monitoring, explainability, data preparation, App Framework CI/CD, and…
-
Connect to preclinical research tools and databases (literature search, genomics analysis, target prioritization) to accelerate early-stage life sciences R&D
-
Write SQL, explore datasets, and generate insights faster. Build visualizations and dashboards, and turn raw data into clear stories for stakeholders.
-
Build multimodal AI applications with Pixeltable -- tables, computed columns, embedding search, UDFs, tool-calling agents, and 25+ AI provider integrations.
-
Atlan data catalog plugin for Claude Code. Search, explore, govern, and manage your data assets through natural language. Powered by the Atlan MCP server with semantic search…
Has a listing on Codex: openai-curated (openai/plugins) (5)
-
Answer product and business questions with data
-
Guided NGS intake, local execution, and public-pipeline routing for BCL, FASTQ, DNA variant, RNA-seq, single-cell, epigenomics, amplicon, and metagenomics analyses, with deeper…
-
Evaluate Codex skills and plugins from chat with a beginner-friendly start command, local-first reports, token budget explanations, and guided benchmarking.
-
General life-sciences research workflows with query routing, evidence synthesis, and optional parallel subagent analysis across genetics, omics, biology, chemistry, structure…
-
Skills for NVIDIAs ecosystem spans GPU acceleration, CUDA, AI agents, inference, robotics, Physical AI, Omniverse, and simulation. This plugin helps you understand the pieces…
Has a listing on Community GitHub marketplaces (5)
-
Expert agents for R data science and biostatistics covering data wrangling, feature engineering, model building, statistical evaluation, clinical trials, genomics, documentation…
-
Comprehensive R programming assistant for Claude Code — tidyverse-first data analysis, package development, Shiny, statistics, and biostatistics.
-
Data science and ML workflow tools. 9 agents, 8 commands, 19 skills, 9 templates for problem framing, preprocessing, validation, EDA, experimentation, review, deployment, and…
-
Run Stata from Claude Code through the stata-code MCP server (23 tools, 14 workflow prompts, typed errors, token-economy logs/graphs, multi-session) and an agent skill that…
-
Decision-grade data science: audits, leakage-safe experiments, diagnosis, validation, reproducible handoff.
Has a listing on Copilot CLI: github/awesome-copilot (5)
-
Run a lightweight, role-separated AI development team with flexible tools, developer-selected models, proportionate planning, and optional QA.
-
Task Researcher and Task Planner for intermediate to expert users and large codebases - Brought to you by microsoft/edge-ai
-
Create, edit, run, save, and checkpoint Jupyter notebooks in an interactive Copilot canvas.
-
Model, visualize, and deploy applications with Radius from the GitHub Copilot app and CLI.
-
See your local Copilot token usage by day, model, and session, and get tips to use it more efficiently.
Has a listing on Copilot CLI: github/copilot-plugins (5)
-
Toolkit for building Microsoft 365 Copilot declarative agents — scaffolding, JSON manifest development, and capability configuration.
-
Build and manage Microsoft Dataverse solutions using natural language. Includes table/column creation, solution lifecycle, data operations, and MCP server configuration.
-
model-apps
Build and deploy Power Apps generative pages for model-driven apps with specialist agents for planning, entity creation, and parallel code generation. Requires PAC CLI >= 2.7.0…
-
Bring Azure DevOps work-item, repository, and pull-request workflows into GitHub Copilot.
-
Build Power Apps Canvas Apps using the Canvas Authoring MCP server.
Has a listing on Cursor: cursor/plugins (5)
-
Iterative self-referential AI loops using the Ralph Wiggum technique.
-
Inspect and manage feature gates, experiments, dynamic configs, and metrics.
-
Fan large tasks out across parallel cloud agents with planners, workers, verifiers, and structured handoffs.
-
Track AI visibility, sentiment, and citations.
-
Consult a stronger model before major decisions, when stuck, and before declaring done.
Has a listing on Docker MCP Catalog (5)
-
Read and score Opik traces, experiments, datasets and prompts from your AI assistant.
-
Bridges multiple AI models and CLIs, enabling orchestrated workflows across Claude Code, Gemini CLI, Codex CLI, and other AI development tools.
-
Tools for interacting with Hugging Face models, datasets, research papers, and more.
-
Sequential Thinking (Reference)
Dynamic and reflective problem-solving through thought sequences.
-
Analyze documents, images, videos, and audio.
Has a listing on Official MCP Registry (5)
-
Connect RStudio to AI assistants for interactive R coding and data analysis.
-
Valid and reliable data engineering and statistical analysis without hallucinations.
-
A statistical analyst in your AI chat: bring data and a question, get a citable, re-runnable report.
-
Give your AI a research team. Forecast, score, classify, or research every row of a dataset.
-
50+ pandas-powered tools for data loading, cleaning, visualization, and ML workflows
Found by meaning, not by shared code or a shared repository: similar plugins stay separate products, and this is not a review or a recommendation.
Want this mark on your plugin in our catalog?
Prove you maintain it with a file in its repository, a file on your domain or a DNS record, and you can show this mark on the card, with live banners for your site and README. How verification works
You also get alerts when a marketplace lists it, drops it, changes its verified mark or what it asks for, and its remote endpoint goes on monitoring as a web service.
Data comes from public marketplace files in GitHub repositories of their authors. Figures come from our own regular passes over the marketplaces, last read on . We publish no contact details for plugin publishers.