Five or six rounds of adding print() statements, running the code, reading the output, adding more print() statements. That is how most AI coding agents debug today, and it is exactly how a first-year developer debugged in 2005.
A new open-source CLI called debug-skill takes a different approach. It wraps the Debug Adapter Protocol (DAP) - the same standard that powers the debugger in VS Code - and exposes basic debugging actions as commands an AI agent can call directly. Set a breakpoint, step through code, inspect variables, evaluate expressions. Each command blocks until execution pauses, then returns the full program state to the agent.
The creator reports that giving an agent access to a proper debugger cut debugging cycles from 5-6 print-and-pray loops down to a single targeted stop. That tracks with common sense: if you can pause execution at the exact line where things go wrong and inspect every variable in scope, you do not need to guess where to sprinkle logging.
The practical benefit is not just speed. Every print-debug cycle eats tokens from the agent's context window (the amount of text it can hold in working memory). Fewer cycles means the agent retains more of your codebase context, which means better fixes.
Debug-skill works as a thin CLI layer, so it should slot into any agent setup that can shell out to external tools. The repository is on GitHub and supports the standard DAP, which means it is language-agnostic in principle - anywhere VS Code's debugger works, this should too.
It is a small tool solving a specific, annoying problem. If you are running Claude Code or Cursor on a codebase with tricky bugs, giving the agent a real debugger instead of println is a no-brainer upgrade.