ds-notebook-readability

Add intent-focused markdown and docstring explanations to analytical notebooks.

6|Updated Apr 9, 2026
One-click install
npx skills add https://github.com/Khodzitcky-Vl/data-science-ai-superpowers --skill ds-notebook-readability
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: ds-notebook-readability
Source: https://github.com/Khodzitcky-Vl/data-science-ai-superpowers/tree/main/ds-notebook-readability
Command: npx skills add https://github.com/Khodzitcky-Vl/data-science-ai-superpowers --skill ds-notebook-readability

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Analytical notebooks often become hard to review or reuse because the reader cannot tell why each step exists, which assumptions were made, or what output drives later conclusions.

Core Features & Use Cases

  • Comment analytical intent: Add concise markdown notes, docstrings, or block comments that explain non-obvious choices, assumptions, and decision logic without restating syntax.
  • Improve notebook handoff readiness: Perform a “handoff pass” over long cells, metric/label/model/diagnostic sections, transformations, joins, aggregations, and export/narrative steps to ensure a new analyst can interpret results.
  • Target explanations only where needed: Explain inputs, grains/denominators, edge cases, authoritative outputs, and interpretation boundaries so correctness and intent are visible to reviewers.

Quick Start

Use ds-notebook-readability when you are editing or reviewing a notebook and want to add the minimum necessary explanations so another analyst can understand the methodology, assumptions, and decision logic.

Frequently Asked Questions about ds-notebook-readability

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

FAQPage Schema
How do I improve notebook readability for peer review and analyst handoff?

Improve notebook readability by adding intent-focused markdown notes and docstrings that explain non-obvious analytical choices, assumptions, and decision logic without restating syntax. Target explanations at long cells, transformations, metrics, and authoritative outputs so reviewers can interpret results.

What should I document in an analytical notebook to make metrics and transformations clear?

Document analytical intent in notebooks by explaining inputs, grains, denominators, edge cases, and interpretation boundaries. Ensure authoritative outputs and dependencies are clear so another analyst can understand the methodology and decision rules.

How do I write effective comments for data analytics notebooks without just explaining code syntax?

Write effective notebook comments by adding concise markdown notes and block comments that explain non-obvious analytical choices and assumptions. Avoid syntax-only comments and instead target explanations where correctness and intent are needed for handoff.

When do I need to add documentation to a notebook for a peer review pass?

Add documentation to a notebook for peer review when long cells, metric sections, transformations, joins, and aggregations lack explanations of analytical intent. Perform a handoff pass to ensure a new analyst can interpret diagnostic steps and conclusions.

What is the best way to ensure analytical notebooks are ready for team handoff and reuse?

Ensure notebooks are ready for handoff by performing a handoff pass over transformations and export steps to add the minimum necessary explanations. Target explanations at decision logic and authoritative outputs so another analyst can reuse the analysis.

What are the limitations of adding intent-focused explanations to analytical notebooks?

Limitations of notebook explanations include targeting only non-obvious analytical choices rather than every cell, avoiding syntax-only comments, and ensuring the minimum necessary explanations are added so correctness and intent are visible without creating reading friction.