gpd-debug

Debugging physics calculation errors systematically involves diagnosing numerical instabilities and convergence failures through persistent state tracking. This approach spawns parallel investigation agents to identify root causes like wrong approximations and unphysical results.

Investigating unphysical results and sign errors in computational physics requires a systematic debugging approach. It maintains persistent state across context resets to trace theoretical inconsistencies and automate the collection and verification of diagnosis results.

Yes, diagnosing physics calculation errors with this systematic approach requires both the GPD runtime and the gpd-debugger agent. These dependencies enable Python orchestration and the spawning of independent investigation agents for parallel processing.

Yes, you can run parallel investigations for physics debugging across context resets. The system maintains persistent state and spawns independent subagents for each issue, ensuring investigations proceed with minimal overhead when the invocation context resets.

Quick triage of common patterns causing discrepancies in physics calculations is achieved by matching issues against a set of known causes. This allows for immediate resolution of many numerical instabilities and theoretical inconsistencies without spawning deep investigation agents.

You should avoid automated subagent investigation for physics calculation errors when the issue matches common triage patterns for immediate resolution. For complex numerical instabilities or theoretical inconsistencies, the automated parallel investigation provides better root cause diagnosis.