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agent-change-walkthrough

Generate narrative walkthroughs of AI-authored code changes with annotated diffs.

44|1|Updated Jan 21, 2026

Search Tags:#diff analysis #code explanation #AI code review #developer tooling #code comprehension #implementation walkthrough

Ready to run

npx skills add https://github.com/cameroncooke/skills --skill agent-change-walkthrough

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-change-walkthrough
Source: https://github.com/cameroncooke/skills/tree/main/agent-change-walkthrough
Command: npx skills add https://github.com/cameroncooke/skills --skill agent-change-walkthrough

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill clarifies complex AI-generated code changes by providing a narrative walkthrough, making it easier to understand the implementation details and their impact.

Core Features & Use Cases

Narrative Walkthrough: Generates a story of code changes from trigger to behavior.
Annotated Diffs: Includes mini-diffs with line-level context.
Dependency Ordering: Organizes steps by dependency then runtime flow.
Analysis Integration: Embeds trade-offs, alternatives, and risks inline.
Use Case: When an AI modifies a feature, use this Skill to get a clear, step-by-step explanation of exactly how the code now works, including why certain decisions were made.

Quick Start

Use the agent-change-walkthrough skill to explain the recent changes in the src/utils/helpers.ts file.

Frequently Asked Questions about agent-change-walkthrough

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

FAQPage Schema

How do I get an AI code review that explains code changes step-by-step?▼

To get an AI code review with a step-by-step code explanation, you need a narrative walkthrough tool. It analyzes git diffs to generate a story of code changes ordered by dependency and runtime flow, from trigger to final behavior. The review includes annotated mini-diffs, input/output examples, and inline analysis of trade-offs, alternatives, and risks.

What is the best way to understand AI-generated code changes from a git diff?▼

The best way to understand AI-generated code changes from a git diff is using a dependency-ordered narrative walkthrough. It weaves changed and unchanged code into a single story, organizing the explanation by dependency then runtime flow. This approach clarifies implementation details and their impact by providing context-aware annotated mini-diffs and inline analysis.

How does a narrative walkthrough help with code comprehension of complex diffs?▼

A narrative walkthrough helps with code comprehension by transforming complex diffs into a sequential story. It orders code changes by dependency and runtime flow, starting from the trigger to the final observable behavior. This method embeds annotated mini-diffs and inline analysis of trade-offs and risks directly into the explanation, making the implementation details easier to follow.

Do I need git to generate an implementation walkthrough of code changes?▼

Yes, you need git to generate an implementation walkthrough of code changes. The process requires git commands for diff analysis and file context retrieval to accurately construct the narrative. Without git, the tool cannot retrieve the necessary code changes and dependency context to build the step-by-step story.

Can I analyze trade-offs and risks when reviewing AI-authored code modifications?▼

Yes, you can analyze trade-offs and risks when reviewing AI-authored code modifications. The walkthrough process embeds inline analysis of trade-offs, alternatives, and risks directly within the narrative. This ensures you understand not only how the code works but also why certain implementation decisions were made.

When should I use a dependency-ordered explanation for my code?▼

You should use a dependency-ordered explanation when an AI modifies a feature and you need a clear, step-by-step breakdown of exactly how the code works. It is particularly useful for complex changes where understanding the runtime flow, from trigger to final behavior, is critical for grasping the implementation impact.