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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-setup

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

83/100

Supported Platforms

Universal

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.

Substance
26/30
Structure
15/20
Description
15/15
Adoption
13/20
Freshness
15/15

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 found

Our 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.

SkillScoreStarsUpdatedFormat
compute-env-setup (this skill)by xuzhougeng831.2k8d agoSKILL.md
Agent-Reachby Panniantong10088.6k17d agoCLAUDE.md
headroomby headroomlabs-ai10074.3ktodayCLAUDE.md
Scraplingby D4Vinci10085.2k2d agoMCP Server
crawl4aiby unclecode10084.6k7d agoMCP 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.

name: 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

  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. 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.
  5. 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"
    }
  ]
}
  1. Replace all example paths with probed absolute paths. Call monitor_run when waiting is useful (again after wait_interrupted; do not resubmit). Use one get_run snapshot later or cancel_run when requested.
  2. 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 sudo unless 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.

Related Skills

View on GitHub
GitHub Stars1.2k
CategoryCustomer
Updated8d ago
Forks119

Languages

Rust

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions