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You can reconcile project documentation and agent memory by running an end-of-session cleanup skill that cross-references docs, memory indexes, and CLAUDE.md/AGENTS.md files against the codebase. It eliminates stale, duplicated, or misaligned knowledge artifacts to ensure all layers reflect the latest code changes.

Knowledge rot occurs when project documentation, agent memory, and AI instruction files fall out of sync with the latest code, leaving stale context. You prevent knowledge rot by enforcing deterministic validation rules, size ceilings, and promoting stable memory content to permanent documentation at session end.

Yes, this knowledge cleanup workflow supports Claude Code, OpenAI Codex, OpenCode, and OpenClaw. It applies platform-specific path references to reconcile memory indexes, project documentation, and configuration files across all these cross-platform development environments.

You enforce size limits and detect broken links using a deterministic anti-bloat linter. It applies machine-checkable rules to catch memory/docs size inversions, broken index links, and relative time references, ensuring project instruction files remain within hard size ceilings.

The best way to promote temporary agent memory is through a knowledge graduation mechanism. It identifies stable, reusable context from cross-session agent memory and moves it to permanent project documentation, preventing memory bloat while preserving critical development context for future milestones.

You should run a knowledge base cleanup at the end of development sessions or after reaching a development milestone. This ensures that all project knowledge artifacts, including docs, memory indexes, and instruction files, are reconciled with the current codebase before context is lost or becomes outdated.