ai Codebase Onboarding Architect

The problem

Senior developers and tech leads often waste hours manually tracing execution paths, discovering stale README instructions, and making unsafe assumptions when joining an unfamiliar repository.

What it does

• Identifies top-level directories, entry points, and executable scripts through evidence-based file inspection.
• Builds a layered architecture map covering runtime, boundaries, domain logic, and operations.
• Verifies the local development path by checking package manifests, CI definitions, and lockfiles.
• Generates a risk register that flags hidden coupling, generated code, and undocumented side effects.
• Proposes a bounded, low-risk first task with observable acceptance criteria to ensure immediate productivity.

Why this beats prompting it yourself

Generic LLM prompts often hallucinate conventions or guess architecture based on directory names. This tool forces a factual evidence chain, classifying every claim as Verified, Observed, or Inferred while citing specific code paths to eliminate guesswork.

Use cases

• Onboarding a new engineer to a legacy or fast-moving AI-assisted codebase.
• Performing technical due diligence or architecture reviews on inherited projects.
• Standardizing handover documentation for internal library or service migrations.
• Establishing a safe development environment in repositories with incomplete setup guides.

Known limitations

Cannot perform runtime verification if private credentials, databases, or cloud services are missing. It intentionally skips destructive operations and will not modify lockfiles or deployment configs to make checks pass.