MaxellVideocassette discusses the difficulties of using GitHub Copilot for cleaning up codebases, sharing observations and requesting advice from others who’ve tried automating code cleanup.

Cleaning Up a Project with GitHub Copilot

Author: MaxellVideocassette

Problem Statement

When attempting to use GitHub Copilot for tidying up a project—such as removing overlapping styles, orphaned code, or deprecated files—the results can be inconsistent. Common issues encountered include:

  • The AI assistant claims to have completed the cleanup while making minimal practical changes.
  • It sometimes deletes necessary components because of context limitations, leading to incomplete or incorrect cleanup operations.

Methods Attempted

  • Using direct prompts to guide the Copilot or agent to perform cleanup has not consistently produced reliable results.
  • Generating a project summary before cleanup marginally improves the process but often falls short of expectations.

Observations and Frustrations

  • There is a tension between “vibe code” (leaving things as-is, possibly messy) and “clean code” (actively and thoroughly maintained codebases).
  • Manual review remains the fallback, as current automated solutions lack sufficient context awareness to safely and fully clean the project.

Community Inquiry

The author asks if others—especially those who value order and code clarity—have discovered effective workflows or prompting strategies that enable Copilot or similar agents to clean up projects more reliably than manual intervention.

They explicitly seek:

  • More effective prompt strategies
  • Tool enhancements or auxiliary steps
  • Unexpected uses of Copilot’s features

Summary

Despite the growing capabilities of AI assistants like GitHub Copilot, project cleanup remains an area with significant challenges related to context, reliability, and fine control. Manual review is still often required, and further community input is needed to discover possible workarounds or improvements.

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