skills.rest
zhizhunbao
zhizhunbaoCOMMUNITY
Explore all skills by this author →
GitHub

learning-code_consistency

Compare function definitions, logic, and outputs across Python scripts, notebooks, and Markdown.

Updated Jan 28, 2026
Search Tags:#documentation#reporting#code consistency#jupyter notebook#python script#lab submission
One-click install
npx skills add https://github.com/zhizhunbao/ai-dev-config --skill learning-code-consistency-zhizhunbao
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: learning-code_consistency
Source: https://github.com/zhizhunbao/ai-dev-config/tree/main/core/skills/learning-code_consistency
Command: npx skills add https://github.com/zhizhunbao/ai-dev-config --skill learning-code-consistency-zhizhunbao

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill ensures that your Python scripts (.py), Jupyter notebooks (.ipynb), and documentation (.md) are consistent, preventing errors and ensuring accurate reporting before submission.

Core Features & Use Cases

  • Code Logic Verification: Compares function definitions, logic, and outputs between .py and .ipynb files.
  • Documentation Accuracy: Validates that data, statistics, and image references in .md files match the actual code output.
  • Image Reference Check: Confirms all images are correctly referenced and accessible.
  • Use Case: Before submitting a lab assignment, use this Skill to automatically check if the code in your .py file produces the same results as shown in your .ipynb and documented in your .md file, including all necessary screenshots.

Quick Start

Run the learning-code_consistency skill to check the alignment between my python script, notebook, and markdown documentation.

Frequently Asked Questions about learning-code_consistency

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I check code consistency between a Python script and a Jupyter notebook?▼

To check code consistency, this Skill compares function definitions, execution logic, and output values between your .py files and .ipynb notebooks, identifying discrepancies before you submit your lab assignment.

How do I verify that my Markdown documentation matches my Python code output?▼

You can verify documentation accuracy by using this Skill to validate that data, statistics, and image references in your .md files correctly match the actual output generated by your Python code.

What is the best way to ensure image references are correct in a lab submission?▼

The best way to ensure image references are correct is to run a check that confirms all images referenced in your documentation are correctly linked and accessible, preventing broken links in your submission.

Do I need any specific dependencies to verify Python script and Jupyter notebook alignment?▼

No specific dependencies are required to verify alignment, as the Skill operates independently to compare your Python scripts, Jupyter notebooks, and Markdown documentation without external components.

When should I use an automated documentation accuracy check for academic submissions?▼

You should use an automated documentation accuracy check right before submitting a lab assignment, ensuring your code logic, output values, and screenshots are reproducible and perfectly aligned.

Related Skills

Explore More Education & Research Skills→
Ming-Kai-LCMing-Kai-LC

notebook-tester

Execute Jupyter notebooks and verify outputs for errors and quality metrics.

Community
Intermediate
rangerrick337rangerrick337
10

pdf-conversion

Convert PDFs to Markdown with preserved formatting and embedded images.

Community
Intermediate
zhizhunbaozhizhunbao

learning-notebook_conversion

Convert Jupyter notebooks to Python scripts while preserving code and markdown.

Community
Basic
cegit27cegit27
3

canary

Monitor live production apps for console errors and performance regressions.

Official
Advanced