Language-specific research software practice

The process skills in this pack are language-agnostic; this skill answers language-SPECIFIC questions with per-language guidance. Each language guide follows the same template - introduction, information sources, setup, development environments, style standards, packaging, testing, quality assurance, optimization, logging, documentation, dependencies, starting points - so comparable answers exist for every covered language.

Current-tool quick reference per language

The strongest current stack per ecosystem (the pack-wide rule: best current option, runner-up named, reason given - and these move, so verify against the ecosystem when in doubt):

• Python: uv for environments/packaging (runner-up: pip+venv - uv is faster and lockfile-native), ruff as the single linter+formatter (replaces flake8/isort/black), pytest for tests, mypy for types, pyproject.toml as the one config home. Python 2 relics (2to3, six) and legacy tool chains belong to migration work, not new projects.
• R: usethis/devtools for package workflow, testthat for tests, lintr + styler for quality, roxygen2 for docs, renv for reproducible libraries; rOpenSci's guide is the community bar.
• C/C++: CMake as the build lingua franca, Catch2 or GoogleTest for tests, clang-tidy + clang-format for quality, sanitizers (ASan/UBSan) in CI, a package manager (vcpkg/Conan) over vendored sources.
• Fortran: fpm (Fortran Package Manager) for new projects, gfortran in CI, pFUnit for tests; modern-Fortran style over F77 habits, and interop via iso_c_binding when Python needs to call it.
• Julia: built-in Pkg with Project.toml/Manifest.toml, Test stdlib, JuliaFormatter; register in General only once the API settles.
• JavaScript/TypeScript: TypeScript by default for anything shared, vitest for tests, Biome as linter+formatter (runner-up: eslint+prettier - Biome is one fast tool), npm lockfiles committed.
• Bash: shellcheck non-negotiable, bats-core for tests when a script earns them - and past ~100 lines, prefer a real language (rseng-software-design's honesty about scripts).

These map the pack's practices INTO each ecosystem; the process itself (testing discipline, packaging, CI shape) stays in the process skills.

How to use the guides

• Identify the language, open the matching page from references.md, and answer from its sections rather than from generic memory - the guides encode current, community-reviewed tool choices (for example the Python guide's Ruff-first style tooling, pytest, mypy/Pydantic typing and Sphinx/MkDocs documentation stack).
• For multi-language projects, apply each language's guide to its part and keep shared concerns (CI, licensing, citation) with the process skills.
• Where a language guide and a general skill overlap (testing, packaging, documentation), the general skill gives the WHY and the language guide gives the concrete WHAT for that ecosystem.
• The covered languages: Python, R, JavaScript/TypeScript, C/C++, Fortran, Rust, Bash - plus a technology overview page for adjacent topics.

Choosing a language

For "which language should this project use", combine the guides' starting-point sections with rseng-management-planning (technology choice): weigh ecosystem fit for the research domain, team experience, and long-term maintainability over micro-benchmarks.

Working with this skill

The generated references.md beside this file lists the source material and pointers:

• references.md - verified Learn more pointers

Learn more (verified):

• https://peps.python.org/pep-0008/ - PEP 8 style guide for Python code
• https://style.tidyverse.org - Tidyverse style guide for R
• https://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines - C++ Core Guidelines
• https://doc.rust-lang.org/book/ - The Rust Programming Language book
• https://fortran-lang.org/learn/ - Fortran-lang community learning resources

Related skills

Check whether any of these applies before moving on:

• rseng-code-quality - linter and formatter per ecosystem
• rseng-dependency-management - verifying current ecosystem tooling
• rseng-management-planning - language choice for new projects
• rseng-notebooks - Python/Julia notebook practice
• rseng-project-scaffolding - language templates at kickoff
• rseng-testing - per-language test framework choice