compute-env-setup
Set up and validate a reproducible Python or R environment on a Wisp execution context. Use for a selected local, WSL, or direct SSH context when installing scientific packages, configuring caches, recording interpreter activation, or producing an environment smoke test. Do not use for scheduler clusters or managed cloud providers that Wisp cannot track yet.
Profile →npx skills add xuzhougeng/wisp-science --skill compute-env-setup --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Set up a compute environment Treat the selected and probed `ExecutionContext` as authoritative. Wisp currently supports `local`, `wsl:<distro>`, and direct `ssh:<alias>` contexts; it does not expose an authenticated provider SDK inside Python. ## Plan the environment Define before installing: - Python or R version; - ordered conda/pip/R package phases with important pins; - required CUDA capability and minimum VRAM; - cache variables and durable weight locations; - import checks, CLI checks, and one seeded representative workload; - the exact activation command later Runs must include. Use `references/envs_reference.md` for package-order and cache examples, but replace container-specific paths with paths valid on the selected context. ## Direct SSH workflow 1. Require a selected `ssh:<alias>` context with a recent Probe result. Respect recorded GPU, privilege, interpreter, conda/mamba, module, and scheduler capabilities. 2. If a scheduler is detected, stop. Do not install or run long work on a shared login node; Wisp needs a scheduler-aware Run backend first. 3. Use at most a few bounded read-only `shell` commands to confirm free space, existing environments, and cache paths. 4.
- Plan the environment
- Direct SSH workflow
- Setup-script requirements
- Local and WSL boundary
- Unsupported backends
What does the compute-env-setup skill do?
Set up and validate a reproducible Python or R environment on a Wisp execution context. Use for a selected local, WSL, or direct SSH context when installing scientific packages, configuring caches, recording interpreter activation, or producing an environment smoke test. Do not use for scheduler clusters or managed cloud providers that Wisp cannot track yet.
How do I install it?
Run `npx skills add xuzhougeng/wisp-science --skill compute-env-setup --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From xuzhougeng/wisp-science, a repository with 895 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.