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.
Install / Use
npx skills add xuzhougeng/wisp-science --skill compute-env-setupInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Customer SupportSupported Platforms
Our assessment of compute-env-setup
compute-env-setup scores 83/100 on our quality scale, 241st of 335 Customer Support skills we index.
Its SKILL.md is 4.2 KB long, split into 6 sections with 1 code example: a solid amount of guidance for an agent.
With 1,167 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 8 days ago, so compute-env-setup is actively maintained.
- It is released under AGPL-3.0, a copyleft license: you can use it, but modified versions you distribute must carry the same license.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
Safety scan
No issues foundOur scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.
Automated pattern scan on 2026-10-02. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.
compute-env-setup compared with similar skills
All 4 of these similar skills score higher than compute-env-setup; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| compute-env-setup (this skill)by xuzhougeng | 83 | 1.2k | 8d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 88.6k | 17d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 74.3k | today | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 85.2k | 2d ago | MCP Server |
| crawl4aiby unclecode | 100 | 84.6k | 7d ago | MCP Server |
Frequently asked questions
- How do I install compute-env-setup?
- Run
npx skills add xuzhougeng/wisp-science --skill compute-env-setup. The install tabs above show the steps for each supported agent. - Which AI agents does compute-env-setup work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is compute-env-setup safe to use?
- Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is AGPL-3.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is compute-env-setup still maintained?
- The repository was last updated 8 days ago, so compute-env-setup is actively maintained.
Skill content
View source on GitHubname: compute-env-setup description: 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. license: Apache-2.0
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
- Require a selected
ssh:<alias>context with a recent Probe result. Respect recorded GPU, privilege, interpreter, conda/mamba, module, and scheduler capabilities. - 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.
- Use at most a few bounded read-only
shellcommands to confirm free space, existing environments, and cache paths. - Write an idempotent project script such as
runs/setup-<environment>.sh. It must use user-writable paths, fail fast, activate the environment explicitly, run all smoke checks, and write a small JSON manifest only after validation succeeds. - Submit the setup script through one persisted Run:
{
"context_id": "ssh:gpu-box",
"title": "Set up singlecell environment",
"command": "bash setup-singlecell.sh /home/me/envs/singlecell /home/me/wisp-env-manifests/singlecell.json",
"timeout_secs": 14400,
"input_paths": ["runs/setup-singlecell.sh"],
"output_specs": [
{
"glob": "ssh://gpu-box/home/me/wisp-env-manifests/singlecell.json",
"kind": "environment-manifest",
"residency": "remote"
}
]
}
- Replace all example paths with probed absolute paths. Call
monitor_runwhen waiting is useful (again afterwait_interrupted; do not resubmit). Use oneget_runsnapshot later orcancel_runwhen requested. - Record the validated activation command, versions, cache paths, GPU witness,
date, and known limitations in a normal project file such as
environments/<context>/<name>.md. This file is documentation, not a hidden resolver.
Setup-script requirements
- Make repeated execution safe: reuse a matching environment or stop with an actionable version mismatch.
- Keep pip install phases ordered; a later dependency resolver must not silently replace pinned torch, CUDA, JAX, NumPy, or compiled extensions.
- Never use
sudounless the Probe explicitly records suitable privilege and the user authorizes it. Prefer conda packages, modules, or user paths. - Put multi-gigabyte weights in durable remote storage. Populate them with the model's real loader, verify non-empty content and completion markers, then run a representative inference witness.
- Write the manifest atomically only after imports, GPU visibility, and the representative workload pass.
Local and WSL boundary
Local and WSL Runs are currently capped at 300 seconds and do not support
input_paths. Use local-env-setup for normal interactive setup. Use
run_in_context only for a bounded command that finishes within that limit and
writes outputs to host-visible project paths.
Unsupported backends
Wisp has no scheduler, Modal, RunPod, cloud Batch, container-service, or managed endpoint execution context today. Do not invent a provider id or hide those lifecycles inside an SSH submission command. Explain the boundary or use a dedicated direct SSH host until a backend implementing submit, poll, cancel, recovery, secrets, and artifact harvest exists.
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
