Feature Implementation

To generate production-ready code from a spec, you need automated feature implementation that creates fully typed modules, pytest suites for edge cases, and Google-style docstrings while validating imports. This ensures correct integration before delivery.

Automating pytest suite generation requires implementing features from detailed task descriptions to automatically create tests covering happy paths, edge cases, and error handling. This guarantees comprehensive code coverage for new modules.

To enforce type safety and coding standards during codegen, apply build-mode tasks that generate fully typed modules and run quality-gate checks. This validates imports and runs linters before handing off the code.

Yes, feature implementation works for adding API endpoints and middleware by generating fully typed modules from detailed task descriptions. It ensures new symbols are imported, referenced, and integrated correctly throughout the project.

To ensure new code symbols are properly wired into an existing project, the implementation process adds Google-style docstrings and validates that new symbols are imported and referenced throughout the codebase before final delivery.

Quality gate checks needed before delivering new source files include running linters, formatters, and import validation. This preparation ensures the generated code meets strict production standards before handoff to the Inspector.