rseng-reproducible-environments
SolidCovers making research software environments reproducible: pinning a language version and its dependencies in a per-project virtual environment, choosing a package/environment manager, and packaging code and its full stack into a container. Use when the user asks how to set up venv/conda/poetry/uv/renv, lock or pin dependencies, share a runnable environment, escape "dependency hell" or "works on my machine", write a Dockerfile, build an Apptainer/Singularity image for HPC, or decide between a virtual environment and a container. For regenerating a project's results end to end see rseng-reproducibility; for environments of multi-step pipelines see rseng-workflows.
Install
Quality Score: 83/100
Skill Content
Details
- Author
- fdiblen
- Repository
- fdiblen/rseng-agent-skills
- Created
- 4 days ago
- Last Updated
- 4 days ago
- Language
- Python
- License
- MIT
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
rseng-reproducibility
Covers end-to-end computational reproducibility: making a project's results regenerable with one command, determinism and seed discipline, research compendium structure, replication packages for papers, Binder-launchable repositories, artifact evaluation and reproducibility badges. Use PROACTIVELY when the user wants results others can reproduce, prepares a replication package or artifact submission, mentions reproducibility, research compendia, Binder or badges, asks why results differ between runs or machines, or is about to publish results whose regeneration path is untested. (Pinning environments: rseng-reproducible-environments; pipeline automation: rseng-workflows; run-level lineage: rseng-provenance.)
environments
Virtual-environment-first setup for robotics projects: decide uv/venv vs Docker, make local and remote-server runs reproduce identically, handle GPU passthrough and headless/display forwarding. Use when: setting up any new robotics project environment; 'uv', 'venv', 'virtualenv', 'docker for this project', 'reproducible environment', 'works locally but not on the server', 'GPU in container'. Load early in any robium build, right after architect. Decision rule of thumb: pure-Python ML stacks → uv; anything needing ROS 2 or system deps → Docker. Not for: multi-module application Dockerfiles and compose wiring (integration skill).