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golden-jupyter

yusufkaraaslan
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Metatestingdesign

About

This skill provides a Jupyter notebook environment for testing and understanding the golden_jupyter golden build. It includes code examples, outputs, and analysis workflows using Python 3.11.4 with key data science libraries like numpy, pandas, and sklearn. Developers should use it to reproduce analysis steps, review methodologies, and reference implementation patterns.

Quick Install

Claude Code

Recommended
Primary
npx skills add yusufkaraaslan/Skill_Seekers -a claude-code
Plugin CommandAlternative
/plugin add https://github.com/yusufkaraaslan/Skill_Seekers
Git CloneAlternative
git clone https://github.com/yusufkaraaslan/Skill_Seekers.git ~/.claude/skills/golden-jupyter

Copy and paste this command in Claude Code to install this skill

Documentation

Golden_Jupyter Notebook Skill

Use when testing the golden_jupyter golden build

πŸ“‹ Notebook Information

Kernel: Python 3

Language: python 3.11.4

πŸ’‘ When to Use This Skill

Use this skill when you need to:

  • Understand golden_jupyter 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

πŸ“– Section Overview

Total Sections: 5

Content Breakdown:

  • analysis: 5 sections

πŸ”‘ Key Concepts

Main topics covered in this notebook

Major Topics:

  • Getting Started

Subtopics:

  • Modeling Results

πŸ“¦ 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)

πŸ“ 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()

πŸ“Š 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

πŸ—ΊοΈ Navigation

Reference Files:

  • references/analysis.md - analysis

See references/index.md for complete notebook structure.


Generated by Skill Seeker | Jupyter Notebook Scraper

GitHub Repository

yusufkaraaslan/Skill_Seekers
Path: tests/golden/phase2/jupyter
0
ai-toolsast-parserautomationclaude-aiclaude-skillscode-analysis

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