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local-env-setup

Prepare optional local Python/R runtimes, user-configured Python MCP servers, Node/scimaster-cli, and pixi for the user’s task. Reuse detected paths, configure Wisp interpreters, and apply mirrors when needed. Use for 配置环境, missing runtime dependencies, or requested local installations.

Install / Use

npx skills add xuzhougeng/wisp-science --skill local-env-setup

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

93/100

Supported Platforms

Universal

Our assessment of local-env-setup

local-env-setup scores 93/100 on our quality scale, 564th of 4,657 Development & Engineering skills we index (top 13%).

Its SKILL.md is 13 KB long, well organised into 29 sections with 21 code examples: a thorough specification that gives an agent plenty to work with.

With 1,167 GitHub stars, it is one of the more widely adopted skills in the catalogue.

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

Maintenance, license and trust

  • The repository was last updated 8 days ago, so local-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.

local-env-setup compared with similar skills

All 4 of these similar skills score higher than local-env-setup; compare them before choosing.

SkillScoreStarsUpdatedFormat
local-env-setup (this skill)by xuzhougeng931.2k8d agoSKILL.md
Agent-Reachby Panniantong10088.6k17d agoCLAUDE.md
headroomby headroomlabs-ai10074.3ktodayCLAUDE.md
rufloby ruvnet10073.7ktodayCLAUDE.md
CowAgentby zhayujie10047.2ktodayCLAUDE.md

Frequently asked questions

How do I install local-env-setup?
Run npx skills add xuzhougeng/wisp-science --skill local-env-setup. The install tabs above show the steps for each supported agent.
Which AI agents does local-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 local-env-setup safe to use?
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 local-env-setup still maintained?
The repository was last updated 8 days ago, so local-env-setup is actively maintained.

name: local-env-setup description: Prepare optional local Python/R runtimes, user-configured Python MCP servers, Node/scimaster-cli, and pixi for the user’s task. Reuse detected paths, configure Wisp interpreters, and apply mirrors when needed. Use for 配置环境, missing runtime dependencies, or requested local installations. For remote SSH compute use compute-env-setup. license: Apache-2.0 tags: environment, uv, python, r, node, npm, pixi, scimaster, mirror, china, install, macos, windows, linux

Local runtime setup

Wisp starts and runs native biological tools without Python, R, uv, or Node. First-run model setup and Settings → Models show a background path check. Cmd/Ctrl+P → Quick setup (快速配置) reopens setup and refreshes the check. It never runs installers or creates a virtualenv. Detected executable paths are saved in the Local execution context without replacing existing choices. A found path does not establish version compatibility or installed packages.

| Task | Optional tools | |---|---| | Python analysis / persistent python tool | Existing Python environment; uv can help create one | | R analysis / persistent r tool | Rscript with jsonlite | | User-configured Python MCP | That server's own interpreter and dependencies | | bear-* literature skills | Node >= 20, npm, sci (scimaster-cli) | | Bioinformatics workflows | pixi for isolated conda/pip environments |

Start with the user's task and existing environment. Install only what it needs. Do not treat every missing optional tool as a broken installation. After changing PATH, use Check paths again in model settings; restart Wisp only if the GUI process needs to inherit a changed PATH. Interpreter settings can be saved directly without restarting the app.

Step 0 — Detect platform, region, and current state

Read the Environment section in the system prompt (Operating system, Working directory).

0a — Region / network (mirror or not)

Before any install or pip/npm/pixi add, decide whether the user is on mainland China and needs mirrors.

Signals (use several; do not rely on one):

| Signal | Mainland likely | |---|---| | User writes in Chinese and mentions 国内 / 镜像 / 翻墙 / 清华 / 阿里 | yes | | TZ / system timezone Asia/Shanghai, Asia/Chongqing, Asia/Urumqi | hint | | Locale zh_CN, zh-Hans-CN | hint | | curl -s --connect-timeout 3 https://pypi.org/simple/ fails or >5s; tuna mirror responds in <2s | yes | | User explicitly says they are not in China / have full international access | no |

If ambiguous, ask once: "Are you on mainland China? I'll use domestic mirrors for pip/npm/conda if yes."

When mainland mirrors apply, set these before installs (user shell profile or session env):

# PyPI / uv (Python environments + pixi pip deps)
export UV_INDEX_URL=https://pypi.tuna.tsinghua.edu.cn/simple
export PIP_INDEX_URL=https://pypi.tuna.tsinghua.edu.cn/simple

# npm (scimaster-cli)
npm config set registry https://registry.npmmirror.com

Windows (PowerShell, persist for user):

[Environment]::SetEnvironmentVariable("UV_INDEX_URL", "https://pypi.tuna.tsinghua.edu.cn/simple", "User")
[Environment]::SetEnvironmentVariable("PIP_INDEX_URL", "https://pypi.tuna.tsinghua.edu.cn/simple", "User")
npm config set registry https://registry.npmmirror.com

Pixi conda channels (global or per-project pixi.toml):

[project]
channels = ["https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/"]

[pypi-config]
index-url = "https://pypi.tuna.tsinghua.edu.cn/simple"

Or global:

pixi config set --global pypi-config.index-url https://pypi.tuna.tsinghua.edu.cn/simple

Alternatives if tuna is slow: Aliyun PyPI https://mirrors.aliyun.com/pypi/simple/, USTC conda mirrors.

If international access works, do not set mirrors — use defaults.

0b — Tool presence

Run with shell (PowerShell on Windows, sh -c elsewhere):

Windows:

Get-Command python,python3,Rscript,uv,node,npm,sci,pixi -ErrorAction SilentlyContinue | Select-Object Name,Source
uv --version 2>$null; node --version 2>$null; npm --version 2>$null; sci --version 2>$null; pixi --version 2>$null

macOS / Linux:

for c in python3 python Rscript uv node npm sci pixi; do command -v $c && $c --version 2>/dev/null; done

The optional environment panel in onboarding, model settings, and Capabilities shows detected paths. Inspect the saved Local interpreter settings before choosing another environment.

Python and R, when requested

Reuse an existing project environment when suitable. In the desktop app, set_runtime_interpreter saves a path on the execution context:

{"context_id":"local","language":"python","executable":"/absolute/path/to/python"}

For R, use "language":"r" and an absolute Rscript path. Users can also open Runtime interpreters and browse for either executable. Running REPLs keep their previous interpreter until restarted; the agent tool restarts the current conversation's matching REPL and clears its variables.

For Python, invoke the chosen executable to verify its version and install only needed packages. python/requirements-kernel.txt lists common analysis packages shipped as a requirements file; native biological tools require none of them. The kernel worker itself uses the Python standard library. For R, verify Rscript -e 'library(jsonlite)' using the chosen absolute path; install jsonlite in that environment if needed, then test the Wisp r tool.

The CLI uses an existing environment at <workspace>/.wisp/python/.venv if present, otherwise Python on PATH. The desktop retains these legacy fallback locations for users who already prepared them; it never creates them on launch:

| OS | Desktop venv path | |---|---| | Windows | %APPDATA%\science.wisp-science\wisp-science\python\.venv | | macOS | ~/Library/Application Support/science.wisp-science/wisp-science/python/.venv | | Linux | ~/.local/share/science.wisp-science/wisp-science/python/.venv |

Install uv

International:

# Windows
powershell -ExecutionPolicy Bypass -c "irm https://astral.sh/uv/install.ps1 | iex"
# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

Mainland China: prefer winget / Homebrew / distro package if the astral installer is slow or blocked; set UV_INDEX_URL (above) before uv pip install.

winget install --id astral-sh.uv -e          # Windows
brew install uv                               # macOS

Default binary: ~/.local/bin/uv (Unix) or %USERPROFILE%\.local\bin\uv.exe (Windows). Ensure that dir is on PATH.

Python via uv

uv python install 3.11
uv python list

Target: Python 3.11+. With mainland mirrors, export UV_INDEX_URL first.

Create a project environment when needed

Set ENV_DIR to a task-specific environment location and REQ to the bundled or repository python/requirements-kernel.txt when those analysis packages are wanted. Reuse an existing suitable environment instead of recreating it.

uv venv "$ENV_DIR"
uv pip install -r "$REQ" --python "$ENV_DIR/bin/python"
"$ENV_DIR/bin/python" -c "import sys; print(sys.executable)"

Windows PowerShell: use $envDir, $req, and Scripts\python.exe:

uv venv $envDir
uv pip install -r $req --python "$envDir\Scripts\python.exe"
& "$envDir\Scripts\python.exe" -c "import sys; print(sys.executable)"

Save that absolute interpreter through set_runtime_interpreter when available, then verify a small python tool call. If the tool is unavailable in the CLI, use the CLI fallback location above or launch with the prepared environment on PATH.

User-configured Python MCP servers

Read that server's installation requirements. Prepare its own environment and configure its MCP command with the absolute executable and appropriate arguments. Do not reinstall the removed mcp-servers/bio-tools tree or add Python MCP packages merely to use Wisp's native biological tools. Test the configured connection after setup.

Layer 2 — Literature: Node + scimaster-cli

Required for bundled bear-support, bear-counter, bear-map, bear-scoop, bear-trace, bear-review, bear-onboard, bear-propose.

Install Node >= 20

International: https://nodejs.org/ LTS, or winget install OpenJS.NodeJS.LTS, or brew install node.

Mainland China:

# Windows — winget often works; or npmmirror-hosted installer
winget install OpenJS.NodeJS.LTS
# macOS — brew or fnm with npmmirror
brew install node
# fnm alternative:
# export FNM_NODE_DIST_MIRROR=https://npmmirror.com/mirrors/node
# fnm install 20 && fnm use 20

After install, open a new terminal; verify node --version (v20+).

scimaster-cli

Set npm registry first if mainland (see 0a), then:

npm install -g scimaster-cli
sci init        # paste SciMaster API Key
sci --version
sci usage

API Key: SciMaster settings → API Key. Do not proceed with bear-* skills if sci --version fails.

In the wisp-science desktop app, you can also save the SciMaster key in Settings -> Credentials -> SCIMaster. Wisp will sync that key into ~/.scimaster/config.json for scimaster-cli.

Layer 3 — Bioinformatics: pixi

pixi manages isolated per-project environments (conda + pip) — use for scanpy/single-cell, variant calling stacks, etc. The Wisp python tool uses the selected context interpreter. The same environment can run standalone commands through pixi run python … or pixi run …, or supply the interpreter for persistent python/r analysis. Choose process lifetime according to state reuse, script requirements, and task lifecycle; installing an environment does not select an execution method. Scripts consuming existing runtime objects can use script_path and required_objects.

Workflow engines

For multi-step analysis pipelines (many rules, parallel execution, resume after failure), pair pixi with a dedicated workflow engine — pixi manages the environments, the engine schedules the steps. Run engines from shell in the project directory.

| Engine | What it is | When to choose | |---|---|---| | snakemake | Python-defined rules, mature conda/mamba integration | Established rules; Python-centric teams | | nextflow | Groovy DSL, container-first | nf-core ecosystem; HPC/cloud portability | | oxo-flow | Rust-native, TOML-defined DAG engine; CLI + web UI | Lightweight single-binary install; rule-level conda/mamba/pixi/docker/singularity backends; checkpoint/resume |

Install pixi

International:

curl -fsSL https://pixi.sh/install.sh | bash
powershell -ExecutionPolicy ByPass -c "irm -useb https://pixi.sh/install.ps1 | iex"

Mainland China: if install script is slow, try brew install pixi (macOS) or download release from GitHub mirror; then configure mirrors (0a).

Typical project workflow

In the user's analysis directory:

pixi init
pixi add scanpy anndata          # example; adjust to task
pixi run python analysis.py

Multiple envs: use [environments] / features in pixi.toml, or separate project dirs — see pixi docs.

With mainland mirrors, set [pypi-config] and channels in pixi.toml (0a) before large pixi add.

Verify pixi

pixi --version
pixi info    # shows config paths and channels

Workarounds

| Issue | Fix | |---|---| | uv/node installed but app still says missing | Restart wisp-science; confirm tools on PATH for the GUI user (macOS: relaunch from Dock after shell profile update). | | Cannot modify PATH | Set UV_PATH / PIXI_PATH to full binary paths before launching wisp-science. | | Mainland: timeouts on pypi.org / regi

Truncated for display — read the full file on GitHub.

Related Skills

View on GitHub
GitHub Stars1.2k
CategoryDevelopment
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