golden-jupyter-kw
About
This skill provides code examples and outputs for testing the golden_jupyter_kw golden build, helping developers understand analysis workflows and reproduce computational steps. It covers key concepts like modeling results with dependencies including numpy, pandas, and sklearn. Use it to reference methodology, visualizations, and library usage patterns during golden build testing.
Quick Install
Claude Code
Recommendednpx skills add yusufkaraaslan/Skill_Seekers -a claude-code/plugin add https://github.com/yusufkaraaslan/Skill_Seekersgit clone https://github.com/yusufkaraaslan/Skill_Seekers.git ~/.claude/skills/golden-jupyter-kwCopy and paste this command in Claude Code to install this skill
Documentation
Golden_Jupyter_Kw Notebook Skill
Use when testing the golden_jupyter_kw golden build
π‘ 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
π Section Overview
Total Sections: 5
Content Breakdown:
- Setup: 1 sections
- Modeling: 1 sections
- Loading: 1 sections
- Empty Cat: 0 sections
- Other: 2 sections
π Key Concepts
Main topics covered in this notebook
Major Topics:
- Getting Started
Subtopics:
- Modeling Results
π¦ Dependencies
3 package(s) imported
numpypandassklearn
β‘ Quick Reference
Common documentation patterns found:
Getting Started (1 sections):
- Getting Started (section 1)
Modeling (1 sections):
- Modeling Results (section 5)
π 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()
π 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
πΊοΈ Navigation
Reference Files:
references/section_s1-s1.md- Setupreferences/section_s5-s5.md- Modelingreferences/section_s2-s2.md- Loadingreferences/section_04.md- Empty Catreferences/section_s3-s4.md- Other
See references/index.md for complete notebook structure.
Generated by Skill Seeker | Jupyter Notebook Scraper
GitHub Repository
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