Running R Analysis In Existing Projects

Work inside an existing R project to extend analyses, modify scripts, run statistical models, update visualizations, and regenerate reports.

Security Scan

The skill's requests and instructions are consistent with its stated purpose of extending and re-running analyses inside an existing R project; it reads and edits project files and may install R packages, which is expected behavior for this task.

Purpose & Capability

The name/description (extend/debug R projects) matches the behavior described in SKILL.md and README: reading project files, adding models/plots, installing missing R packages, and re-rendering reports. No unrelated credentials, binaries, or config paths are requested.

Instruction Scope

Instructions explicitly operate on project files and standard R artifacts (.Rproj, .Rmd, .qmd, scripts, data/, results/, reports/). The skill will read and modify code and run R rendering commands (rmarkdown::render, quarto render), which is appropriate for the declared purpose. Note: executing user R code and installing packages are powerful actions but expected for this skill.

Install Mechanism

This is an instruction-only skill with no install spec and no code files to run. The README/SKILL.md indicate using install.packages() or BiocManager::install() when packages are missing — that is standard for R workflows and is not an installer downloading arbitrary executables.

Credentials

No environment variables, credentials, or config paths are requested. The only external access implied is network access to CRAN/Bioconductor to install R packages, which is proportional to the stated functionality.

Persistence & Privilege

The skill is not forced-always and does not request persistent system-wide privileges. It does write outputs into project folders (results/, reports/) as expected; this is scoped to the project and within the skill's purpose.

Assessment

This skill is coherent for extending and rerunning analyses in an R project, but it will read and modify your project files, execute R code, and may install R packages from CRAN/Bioconductor. Before installing/using it: back up the project or use version control, run the skill in an isolated environment if possible, review diffs of any code changes the skill proposes, and verify installed packages come from trusted repositories (use renv/lockfiles when available). Refuse or inspect any changes you don't expect (especially code that accesses external networks or secrets).

This skill operates inside an already structured R project. It helps extend, debug, or enhance existing analyses without recreating the project from scratch.

Use this skill when the user wants to:

• Continue analysis in an existing R project
• Modify or extend R scripts
• Add new statistical models or tests
• Update plots or figures
• Regenerate reports after data or code changes
• Debug R errors in a project

---

What This Skill Does

When activated, this skill will:

1. Understand the project structure

   • Detect folders like data/, scripts/, results/, reports/
   • Identify .Rproj, .Rmd, .qmd, or .R files

2. Inspect existing analysis

   • Read current scripts and reports
   • Identify which packages and methods are being used
   • Avoid rewriting working components unnecessarily

3. Extend or modify analysis

   • Add new models or statistical tests
   • Introduce new plots using ggplot2
   • Add new data processing steps
   • Improve code structure or reproducibility

4. Re-run and update outputs

   • Recompute results
   • Overwrite or version new outputs in results/
   • Re-render R Markdown or Quarto reports

5. Debug issues

   • Fix missing packages
   • Resolve file path problems
   • Handle common R errors and warnings

---

Example User Requests That Should Trigger This Skill

• "Add a survival analysis to this R project"
• "Update the plots in my report"
• "This R Markdown file throws an error, fix it"
• "Extend this analysis with a mixed-effects model"
• "Re-run everything after I updated the data"

---

Example Workflow

User: Add a logistic regression model and update the report.

Skill actions:

• Locate main analysis script
• Add logistic regression using glm()
• Save model summary to results/
• Update report with new section and plot
• Re-render HTML/PDF report

---

Tools & Packages Commonly Used

Purpose	R Packages
Data wrangling	tidyverse, dplyr
Modeling	stats, lme4, glmnet
Visualization	ggplot2
Reporting	rmarkdown, quarto
Project management	here, renv

---

Notes

• Respect the existing project structure and style
• Do not delete user code unless explicitly requested
• Prefer incremental updates over full rewrites
• Always regenerate reports after modifying analysis