AI coding agents may be getting bad instructions from ‘smelly’ config files
Summary
This article examines how configuration files used by AI coding agents can contain “smells” that reduce reliability and increase token costs. Researchers catalog several common problems, including lint leakage, context bloat, skill leakage, conflicting instructions, init fossilization, and blind references. The piece explains how these flaws affect tools like Claude Code, Codex, Cursor, and Gemini in software development workflows. It also outlines practical ways developers can clean up prompts and keep agent configs concise, current, and task-specific.
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