Can this skill automatically update my notebook cells?

Yes, it provides an automation pattern and a Python script that can programmatically modify the .ipynb JSON to replace hardcoded values with the correct configuration variables.

Why use an automated audit tool instead of manual search?

Manual reviews often miss hardcoded function parameters or outdated comments hidden in large notebooks. This skill uses programmatic pattern matching across the entire notebook JSON structure to ensure nothing is missed.

Does it check comments and documentation as well?

Yes, it identifies 'wrong comments'—instances where a comment describes a value (e.g., '2 years') that no longer matches the actual configuration setting.

What specific parameters does this skill check for?

It is optimized for trading and data science parameters such as lookback_days, timeframe settings, slippage costs, and version references.

Notebook Configuration Auditor

Name: Notebook Configuration Auditor
Author: smith6jt-cop

bysmith6jt-cop

0•

データサイエンスとML

Identifies and fixes hardcoded values in Jupyter notebooks that conflict with defined configuration constants.

The Notebook Configuration Audit skill ensures consistency in data science and trading notebooks by scanning for hardcoded parameters that contradict central configuration cells. It prevents common errors where settings like lookback periods, slippage costs, or timeframes are updated in one location but remain hardcoded in later cells. By automating the cross-referencing of definitions versus usages, it helps maintain a single source of truth across complex experimental workflows, ensuring that notebook behavior always matches its documented settings.

主な機能

01Automated repair scripts to replace hardcoded values with configuration references

02Detection of hardcoded values within function parameters across all notebook cells

030 GitHub stars

04Cross-referencing of configuration definitions against actual code usage

05Automated pattern matching for configuration constants like lookback and timeframe

06Validation of comments and print statements to ensure they match live code logic

ユースケース

01Auditing legacy trading notebooks for outdated slippage or fee assumptions

02Ensuring consistency between notebook documentation comments and execution logic

03Synchronizing training parameters across large Jupyter notebooks after a config change

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add smith6jt-cop/skills_registry notebook-config-audit

For use in Claude.ai and ChatGPT

Download Skill