Claude Code: My Workflow for Legacy Bug Fixing

Handling a codebase where the original developer has vanished is a nightmare, but using Claude Code as an LLM agent for codebase exploration actually makes this manageable. I've been using it to triage a mess of inherited project files this week, and the efficiency gain in understanding legacy logic is significant.

While GPT-4o is great for isolated snippets, Claude Code's ability to index the local directory and execute terminal commands makes it superior for "archaeology" work. Instead of copy-pasting files into a chat window, I can just tell it to find where a specific bug is originating.

Here is my current AI workflow for mapping out unknown repositories:

1. Contextual Mapping: I start by asking the agent to summarize the project structure and identify the entry points.
2. Trace Analysis: When a bug hits, I feed the stack trace directly to the agent.
3. Iterative Patching: I let it propose a fix, run the build command to check for regressions, and refine the prompt based on the compiler errors.

Compared to standard IDE plugins, the terminal-native approach feels more like a pair-programmer.

• Context Window: Claude handles the large-scale file relationships better than most.
• Execution: The ability to run grep or ls internally beats manual searching.
• Accuracy: Lower hallucination rate on actual file paths compared to Gemini.

For anyone dealing with "ghost" developers and undocumented spaghetti code, this is a practical tutorial in survival. Stop manually searching through folders; let the agent map the dependencies for you.