ez-context

To generate CLAUDE.md and AGENTS.md context files from your codebase, you can scan coding conventions and architecture to automatically produce up-to-date agent-facing documentation with built-in drift detection.

Documentation drift detection in agent workflows identifies inconsistencies between documented modules and actual code, ensuring agent-facing context files like CLAUDE.md remain accurate and synchronized with the latest architecture.

To keep Cursor rules synchronized with your coding conventions, you can automate context extraction to scan repository standards and generate rules that detect drift against the actual code structure.

Yes, automated context generation applies to software projects of varying sizes and languages, scanning conventions across diverse codebases to map architecture and verify consistency for agent documentation.

The best way to automate context extraction for AI agent documentation is to scan repository conventions, map the architecture, generate context files, and verify consistency against the actual code to prevent drift.

Agent-facing documentation goes out of sync with your code due to drift between documented modules and actual code, which requires automated drift detection and consistency verification to maintain accuracy.