Rstudio Research Agent

Interact with R and RStudio environments for scientific research tasks including creating projects, running analyses, managing dependencies, and generating p...

Security Scan

The skill's requirements and instructions are consistent with an R/RStudio research automation tool, but it will execute user R code and install packages from the network — review and run only on trusted projects or in a sandbox.

Purpose & Capability

Name, description, and declared capabilities (project scaffolding, renv, running R/RMarkdown/quarto, plotting, debugging) match the instructions across the main SKILL.md and sub-skill files. Requested actions (activating renv, sourcing scripts, initializing Git, generating plots, and producing package install commands) are expected for this purpose.

Instruction Scope

The SKILL.md instructs the agent to set working directories, activate renv, source and execute arbitrary R scripts and render reports. That is appropriate for running analyses, but it means the skill will execute whatever R code exists in the project (which can perform arbitrary filesystem and network actions). The skill also generates and may suggest running install commands (CRAN, Bioconductor, GitHub) which require network access. Users should be aware the agent will run code with the permissions of the host process.

Install Mechanism

There is no install spec or downloaded code — this is instruction-only. No external archives or package installs are performed by the skill itself; package installation happens at R runtime (install.packages, BiocManager::install, remotes::install_github) which is expected for the described features.

Credentials

The skill declares no environment variables, credentials, or config paths beyond working within a project directory and using renv. It does not request unrelated secrets or system credentials, which is proportionate to its stated functionality.

Persistence & Privilege

always:false and no special privileges are requested. The skill can create files (project scaffolding, .Rproj, renv.lock, .gitignore) and optionally initialize Git — these actions are consistent with its purpose and limited to the project scope rather than system-wide privileged changes.

Assessment

This skill is coherent for R research automation, but it will execute R scripts and may install packages from CRAN/Bioconductor/GitHub — actions that can access files, run shell commands via R, or download code. Only let the skill run on projects and code you trust, or run it inside an isolated environment (container/VM) with limited network and filesystem access. Review any generated install commands before executing (especially GitHub installs), back up sensitive data, and prefer using renv lockfiles to ensure reproducible package versions. Absence of automated scan findings here is expected (instruction-only skill) and does not remove the runtime risk of executing untrusted R code.

A Claude Code skill for comprehensive R-based research workflow automation. This skill enables interaction with R and RStudio environments for scientific computing, statistical analysis, bioinformatics, and data visualization.

Overview

This skill helps researchers and data scientists:

• Create structured, reproducible R research projects
• Execute R scripts and RMarkdown analyses
• Debug environment and dependency issues
• Generate publication-quality plots and reports
• Manage R packages with renv for reproducibility

Use this skill when the user wants to:

• Create a new R project with standard structure
• Run R analyses on existing projects
• Troubleshoot R package dependencies
• Generate statistical reports or visualizations
• Set up reproducible R workflows

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What This Skill Does

When activated, this skill provides four main capabilities:

1. Create R Research Projects

• Scaffold new R projects with standard folder structure
• Initialize Git repositories (optional)
• Set up renv for package management
• Generate template scripts and reports
• Create .Rproj files for RStudio

2. Run Analyses in Existing Projects

• Execute R scripts and RMarkdown files
• Handle parameterized analyses
• Return results, tables, and plots
• Generate HTML/PDF reports

3. Debug Environment and Dependencies

• Check for missing R packages
• Resolve library conflicts
• Suggest fixes for environment issues
• Verify R version compatibility

4. Generate Publication-Quality Plots

• Create figures with ggplot2 and other visualization libraries
• Export to PDF/PNG/SVG/TIFF formats
• Follow journal-specific formatting guidelines
• Support multi-panel composite figures
• Use color-blind friendly palettes

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Example User Requests That Should Trigger This Skill

• "Create a new R project for my genomics data analysis"
• "Run analysis.R in my existing project and show results"
• "Check if all required packages are installed"
• "Generate a scatter plot with regression line from my dataset"
• "Set up a reproducible R workflow for RNA-seq analysis"
• "Debug my R environment - packages won't load"
• "Create a statistical report for this clinical trial data"

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Project Structure

Projects created by this skill follow this standardized structure:

my-research-project/
├── data/
│   ├── raw/               # Original, immutable data files
│   └── processed/         # Cleaned, transformed data
├── scripts/               # Analysis and processing scripts
├── results/
│   ├── figures/           # Plots and visualizations
│   ├── tables/            # Summary tables
│   └── models/            # Saved model objects (.rds files)
├── reports/               # R Markdown/Quarto documents
├── renv.lock              # Package version lock file
├── .Rproj                 # RStudio project file
└── README.md              # Project documentation

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Tools & Packages Commonly Used

Purpose	R Packages
Data wrangling	tidyverse, data.table
Visualization	ggplot2, patchwork, scales
Statistics	stats, lme4, survival, broom
Bioinformatics	Bioconductor (DESeq2, edgeR, limma)
Reporting	rmarkdown, quarto
Reproducibility	renv

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Example Workflows

Creating a New Project

User: Create a new R project for gene expression analysis with Git initialized.

Skill actions:

1. Create directory structure (data/, scripts/, results/, reports/)
2. Initialize Git repository
3. Set up renv environment
4. Install DESeq2, tidyverse, ggplot2
5. Generate analysis template scripts
6. Create R Markdown report template

Running an Analysis

User: Run the differential expression analysis and return results.

Skill actions:

1. Activate project environment (renv)
2. Execute analysis script
3. Capture console output and plots
4. Return summary tables and model statistics
5. Generate report if requested

Debugging Dependencies

User: My R script fails with "package not found" errors.

Skill actions:

1. Check R version and package library paths
2. Scan script for required packages
3. Compare with installed packages
4. Generate installation commands
5. Check for version conflicts

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Notes

• Requires R >= 4.0.0
• Supports both RStudio and command-line R
• Uses renv for reproducible package management
• All outputs saved to files (not just console)
• Follows R best practices and modern conventions

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Sub-Skills

This skill includes specialized sub-skills:

• create-project: Scaffold new R research projects
• run-analysis: Execute R scripts and generate reports
• debug-env: Troubleshoot R environments and dependencies
• generate-plots: Create publication-quality figures with journal formatting

Each sub-skill can be invoked independently or as part of a complete workflow.