skill-role-detect-stack

To detect a project's tech stack from config files, analyze package.json, pyproject.toml, pom.xml, go.mod, and Dockerfiles to identify languages, frameworks, databases, and tooling across common ecosystems.

Frameworks and ecosystems identifiable from source files include Node, Python, Java, Go, and Rust. The detection covers frontend and backend frameworks, databases, ORMs, and build or test tooling configurations.

Yes, you can use Docker and CI configurations to determine a project's tooling. These files are explicitly parsed alongside ecosystem-specific configs to extract framework, database, and build tool data.

Resolving frontend and backend stacks with ORM usage for deployment involves extracting framework and database dependencies from configuration files, producing a structured tech stack report to guide integration.

Yes, this tech stack detection works for automated repository analysis. It produces a structured report of the detected stack to spawn correct agent variants for compatibility checks on new repositories.

The best way to identify ORM and database usage from package files is to scan dependency manifests like pyproject.toml and package.json, which resolves the exact tooling and database layers in use.