Claude-Mem is a Claude Code plugin that records and compresses a developer’s entire coding session using AI, then re‑injects relevant context into future interactions. The article examines the project’s error‑handling anti‑pattern detection script, detect-error-handling-antipatterns.ts, which scans source files for risky patterns such as logging only an error message or silently swallowing exceptions. The author, Ramu Narasinga, walks through the workflow: run the detector with `bun run`, analyze the resulting CRITICAL, HIGH, and MEDIUM findings, and then apply fixes—adding proper logging, approved overrides, or removing the try‑catch block.

The script relies on regular expressions to flag anti‑patterns, and the author muses on whether an abstract syntax tree approach could improve accuracy. By automating this audit, Claude-Mem helps teams avoid common debugging nightmares and maintain cleaner, more reliable codebases. The discussion is relevant to developers working with AI‑assisted tooling, open‑source projects like Supabase, shadcn/ui, and LobeChat, and anyone interested in production‑grade code hygiene.