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Environment Setup

CommunityPopular
Norman-bury
environment-setup

Use when Python environment setup is needed for data visualization or conda installation is required

Overview

PublisherNorman-bury
Repositoryresearch-writing-skill
Skill nameenvironment-setup
Stars
3.2K
Forks
214
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Norman-bury on GitHub. Read the source before you install it.

Installation

Install the Environment Setup AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Norman-bury/research-writing-skill.git /tmp/research-writing-skill
mkdir -p .claude/skills
cp -r /tmp/research-writing-skill/skills/environment-setup .claude/skills/environment-setup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Environment Setup in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Environment Setup on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Environment Setup is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

环境配置

本技能用于在终端完成 Python 画图环境的全流程配置。

适用场景

  • 用户要求安装 Miniconda
  • 用户要求创建虚拟环境
  • 用户要求"用 Python 画图"但环境未就绪
  • 绘图脚本运行报环境相关错误

Checklist

  • 系统识别(macOS/Linux/Windows)
  • Miniconda 安装/修复
  • conda 初始化
  • 创建 research 环境
  • 安装绘图依赖
  • 环境自检
  • 更新 plan/progress.md

一、系统识别

macOS / Linux

bash
uname -s
uname -m
echo "$SHELL"

Windows PowerShell

powershell
$PSVersionTable.PSVersion
$env:OS

二、Miniconda 安装

macOS 全自动流程

bash
set -euo pipefail

# 1) 选择安装包
ARCH="$(uname -m)"
if [ "$ARCH" = "arm64" ]; then
  URL="https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-arm64.sh"
else
  URL="https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh"
fi

# 2) 下载并静默安装
INSTALLER="$HOME/Downloads/miniconda.sh"
curl -fsSL "$URL" -o "$INSTALLER"
bash "$INSTALLER" -b -p "$HOME/miniconda3"

# 3) 当前 shell 立即可用
export PATH="$HOME/miniconda3/bin:$PATH"

# 4) 初始化 shell
"$HOME/miniconda3/bin/conda" init "$(basename "$SHELL")"

# 5) 验证
conda --version

Windows 全自动流程(PowerShell)

powershell
$ErrorActionPreference = "Stop"

# 1) 下载
$installer = Join-Path $env:TEMP "Miniconda3-latest-Windows-x86_64.exe"
Invoke-WebRequest -Uri "https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe" -OutFile $installer

# 2) 静默安装
$target = "$env:USERPROFILE\miniconda3"
Start-Process -FilePath $installer -ArgumentList "/InstallationType=JustMe","/RegisterPython=0","/S","/D=$target" -Wait

# 3) 初始化 powershell
& "$target\Scripts\conda.exe" init powershell

# 4) 验证
$env:Path = "$target;$target\Scripts;$target\condabin;" + $env:Path
conda --version

三、创建科研虚拟环境

默认环境名research

创建与激活

bash
conda create -n research python=3.11 -y
conda activate research
python -m pip install --upgrade pip

四、安装绘图依赖

bash
pip install numpy pandas scipy matplotlib seaborn scikit-learn statsmodels jupyter ipykernel openpyxl
python -m ipykernel install --user --name research --display-name "Python (research)"

可选:

bash
pip install plotly pingouin

五、环境自检

bash
python - <<'PY'
import sys
import numpy, pandas, matplotlib, seaborn, sklearn, statsmodels
print('Python:', sys.version.split()[0])
print('numpy:', numpy.__version__)
print('pandas:', pandas.__version__)
print('matplotlib:', matplotlib.__version__)
print('seaborn:', seaborn.__version__)
print('sklearn:', sklearn.__version__)
print('statsmodels:', statsmodels.__version__)
print('ENV CHECK: OK')
PY

六、画图任务前检查

执行画图任务前,至少确认:

  1. 已激活 research 环境
  2. matplotlibseaborn 导入正常
  3. 输出目录存在(如 figures/

七、常见问题与修复

conda: command not found

macOS / Linux:

bash
export PATH="$HOME/miniconda3/bin:$PATH"
conda init "$(basename "$SHELL")"

Windows PowerShell:

powershell
$env:Path = "$env:USERPROFILE\miniconda3;$env:USERPROFILE\miniconda3\Scripts;" + $env:Path

下载慢或超时

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

包冲突

bash
conda create -n research_clean python=3.11 -y
conda activate research_clean
pip install -r requirements.txt

八、执行约束

Frequently asked questions

What does the Environment Setup AI skill do?

Use when Python environment setup is needed for data visualization or conda installation is required

Why use Environment Setup on TypingMind?

Because you install it once and use it with any model. Environment Setup is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Environment Setup in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Norman-bury/research-writing-skill/tree/main/skills/environment-setup. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Environment Setup?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Environment Setup?

As many as you like. As long as a model supports skills, you can use Environment Setup with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Environment Setup AI skill free?

Yes. It is published on GitHub by Norman-bury under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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