project-onboard

To automate CLAUDE.md generation for multi-agent coding, run a forensic onboarding analysis on your project root. This maps repository structure and entrypoints to produce standardized agent-specific metadata ensuring consistent AI context.

Forensic project onboarding for AI coding agents is the automated process of analyzing repository structure and documentation to generate an evidence-based project passport. This ensures all agents share a consistent understanding of the codebase and identify wiki-to-code discrepancies.

Set up a legacy repository for multi-agent coding environments by running automated forensic onboarding within the project root. This performs a deep scan of module flows and entrypoints to generate reliable agent-specific configuration files and resolve wiki-to-code mismatches.

Yes, you can use automated onboarding to detect wiki-to-code discrepancies by executing a forensic analysis within your project root. The deep scan maps code flows against existing documentation and generates a mismatch report for consistent agent context.

Yes, project onboarding works with both minimal and complex repository structures through dual-scenario seeding. It automatically configures project depth based on complexity, ranging from a minimal spine setup to a full forensic analysis generating deep-scan artifacts.

A key limitation of automated forensic onboarding is that it requires execution directly within the project root directory to function correctly. The evidence-based documentation and agent metadata generation depend entirely on mapping the actual repository structure and code flows present.