01 / ContextThe question
behind the work.
Generic AI chat tools do not understand the active RStudio session, and tools that can modify code create a second problem: an analyst must be able to inspect and control every proposed action.
My role
I designed, implemented, documented, tested, submitted, and now maintain the R package. That includes RStudio addins, context collection, provider integrations, model interpretation, staged actions, project-root safety, Quarto support, pkgdown documentation, and CRAN release work.
03 / In detailBuilt inside the analyst's workspace
Ravel lives in the place where statistical work already happens. It can collect selected code, active editor content, loaded objects, console output, plots, project files, package metadata, and git state through explicit RStudio-aware helpers.
That context supports code explanation, debugging, model interpretation, modeling comparisons, and Quarto drafting without making an analyst reconstruct the project in a separate chat window.
Actions stay reviewable
I built preview-first code and file actions so an analyst can inspect a proposed change before it is applied. Ravel records approval history and blocks writes outside the detected project root unless the user explicitly allows them.
The package also includes helpers for lm and glm interpretation, Quarto methods and results drafting, provider configuration, and remote MCP declarations. The useful parts of AI assistance stay close to reproducible analysis work.
A published CRAN release
Ravel is published on CRAN, has reached 2,000+ users, and installs through install.packages('ravel'). I prepared the documentation, test suite, examples, release assets, pkgdown site, GitHub Actions matrix, and submission materials as part of the package itself.
Version 0.1.2 added OpenAI Responses API support, remote MCP declarations, refreshed provider defaults, and stronger file-action controls. The release passed checks across Windows, macOS, Ubuntu release, and Ubuntu devel.
04 / Engineering judgmentThe decisions
that shaped it.
- Used explicit context collection rather than quietly sending an entire project to a provider.
- Staged every mutating action for review and avoided .GlobalEnv execution unless a user requests it.
- Kept network-backed providers out of examples and tests to meet CRAN expectations and maintain deterministic checks.
- Added MCP capability without a new required dependency, preserving a light installation path.
Evaluation & results
Ravel is published on CRAN, has reached 2,000+ users, and installs through install.packages('ravel'). Version 0.1.2 passed the GitHub Actions matrix on Windows, macOS, Ubuntu release, and Ubuntu devel. The submitted package checks reported zero errors, zero warnings, and one note.