What problem does it solve? It prevents plausible patches that hide symptoms without explaining them, by requiring a reproduction and a proven causal line before any production edit is made. ## Core Features & Use Cases - Evidence-first debugging loop: Reproduce the failure as one bare command, instrument to separate two competing hypotheses, isolate the minimal trigger, then predict and fix only what the mechanism requires. - Delegated investigation and fixing: Dispatch an Opus investigator to prove the cause and a Sonnet fixer to apply the predicted change, handing off through a structured file so file dumps never reach the main session. - Profiling forensics: Diagnose slowdowns, memory growth, and captured traces from CPU profiles, heap snapshots, and trace artifacts, attributing hot paths to file, symbol, and line. - Use Case: A user reports a regression with a failing command. The skill reproduces it, narrows two hypotheses with one deciding observation, proves the causal line, applies the minimal fix, and re-runs the reproduction and test suite as proof. ## Quick Start Ask the AI to debug the reported symptom or failing command, for example: debug why the export endpoint returns a 500 error when the date range spans a month boundary.