gaia-document-project

To generate project documentation for AI context, you can run a skill that scans your repository, detects the tech stack from manifests, and outputs a structured project-documentation.md file. This provides a single source of truth for AI reasoning and onboarding.

The best way to document a codebase for onboarding is to automate repository scanning to inventory source files and directory structures, then generate a comprehensive markdown artifact. This maps dependencies and the tech stack into a single source of truth.

Yes, this project documentation tool works with diverse codebases by detecting the tech stack from canonical manifests like package.json, pom.xml, or go.mod. It extracts dependencies and environment information to support various frameworks.

You create a single source of truth for repository structure by scanning source files and mapping the directory layout into a project-documentation.md artifact. This deterministic file scan excludes irrelevant paths and records all dependencies.

The generated project documentation artifact is saved under the docs/planning-artifacts/ directory as project-documentation.md. This specific path ensures the documentation is consistently placed for governance and AI provisioning.

Yes, you can exclude irrelevant files when scanning your codebase for documentation. The skill implements deterministic file scanning that automatically excludes irrelevant paths to ensure the generated artifact only contains meaningful project structure data.