agent-change-walkthrough

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.

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.

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.

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.

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.

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.