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golden-jupyter-kw

@yusufkaraaslan · 收录于 1 周前 · 上游提交 2 天前

Use when testing the golden_jupyter_kw golden build

场景构建验证版本测试

适合你，如果需要验证 golden_jupyter_kw 构建的正确性

/ 下载安装
golden-jupyter-kw.skill双击,或拖进 Claude 桌面版 / Cowork,即完成安装↓ .skill↓ .zip
用别的 agent?下载 .zip 解压,把文件夹放进它的技能目录
Claude Code~/.claude/skills/(项目级 .claude/skills/)
Codex CLI~/.codex/skills/
Cursor自动读取上面两处目录
其他工具见其文档的「skills」目录;两个下载是同一份文件,只是名字不同
/ 通过 npx 安装 校验哈希
npx oh-my-skill add yusufkaraaslan/skill_seekers/golden-jupyter-kw
/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- yusufkaraaslan/skill_seekers/golden-jupyter-kw
/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify yusufkaraaslan/skill_seekers/golden-jupyter-kw
安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。
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~569最小装载
~1.1K含声明引用
~1.1K文本包总量
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怎么用

商店整理自技能原文 · 版本 09410e2 · 表述以原文为准
它做什么

安装后，Claude 可以基于 golden_jupyter_kw 笔记本中的代码示例、分析流程和方法论来回答相关问题，并引用其中的依赖库（如 numpy、pandas、sklearn）和最佳实践。

什么时候触发

当你询问 golden_jupyter_kw 的概念、分析工作流、代码示例、方法、可视化结果或库的使用模式时触发。

装好后可以这样说
Claude 会引用笔记本中的 Modeling 部分。
Claude 会参考 Loading 部分的代码示例。
技能原文 SKILL.md作者撰写 · MIT · 09410e2

Golden_Jupyter_Kw Notebook Skill

Use when testing the golden_jupyter_kw golden build

💡 When to Use This Skill

Use this skill when you need to:

  • Understand golden_jupyter_kw concepts and analysis workflow
  • Reference code examples and their outputs
  • Reproduce data analysis or computation steps
  • Review methodology, visualizations, and results
  • Find library usage patterns and best practices
📖 Section Overview

Total Sections: 5

Content Breakdown:

  • Setup: 1 sections
  • Modeling: 1 sections
  • Loading: 1 sections
  • Empty Cat: 0 sections
  • Other: 2 sections
🔑 Key Concepts

Main topics covered in this notebook

Major Topics:

  • Getting Started

Subtopics:

  • Modeling Results
📦 Dependencies

3 package(s) imported

  • numpy
  • pandas
  • sklearn
⚡ Quick Reference

Common documentation patterns found:

Getting Started (1 sections):

  • Getting Started (section 1)

Modeling (1 sections):

  • Modeling Results (section 5)
📝 Code Examples

High-quality code cells from notebook

Bash Examples (1)

Example 1 (Quality: 5.0/10):

pip install pandas
Python Examples (3)

Example 1 (Quality: 9.5/10):

def long_example():
    x0 = 0
    x1 = 1
    x2 = 2
    x3 = 3
    x4 = 4
    x5 = 5
    x6 = 6
    x7 = 7
    x8 = 8
    x9 = 9
    x10 = 10
    x11 = 11
    x12 = 12
    x13 = 13
    x14 = 14
    x15 = 15
    x16 = 16
    x17 = 17
    x18 = 18
    x19 = 19
    x20 = 20
    x21 = 21
    x22 = 22
    x23 = 23
    x24 = 24
    x25 = 25
    x26 = 26
    x27 = 27
    x28 = 28
    x29 = 29
    x30 = 30
    x31 = 31
    x32 = 32
    x33 = 33
    x34 = 34
    x35 = 35
    x36 = 36
    x37 = 37
    x3
...

In [2] (Quality: 7.5/10):

import pandas as pd
df = pd.read_csv('data.csv')
df.head()

Example 3 (Quality: 2.0/10):

%timeit broken()
📊 Notebook Statistics
  • Total Sections: 5
  • Code Cells: 2
  • Markdown Cells: 2
  • Raw Cells: 1
  • Notebooks: 1
  • Programming Languages: 2

Language Breakdown:

  • python: 3 code cells
  • bash: 1 code cells
🗺️ Navigation

Reference Files:

  • references/section_s1-s1.md - Setup
  • references/section_s5-s5.md - Modeling
  • references/section_s2-s2.md - Loading
  • references/section_04.md - Empty Cat
  • references/section_s3-s4.md - Other

See references/index.md for complete notebook structure.

Generated by Skill Seeker | Jupyter Notebook Scraper

按 MIT 许可原样转载,未经改动 · 在 GitHub 查看 →

评论

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