What problem does it solve? Technical Product Owners and reviewers lack a consistent, technology-neutral baseline for judging whether AI-generated code is readable, tested, secure, and consistent with the existing repository, leading to fragile or unreviewable implementations. ## Core Features & Use Cases - Baseline Coding Standards: Defines rules for naming, functions, comments, error handling, logging, configuration, dependencies, and formatting across backend, frontend, database, and API code. - Structured Output Requirements: Mandates a standard implementation report covering summary, files changed, requirement mapping, tests, risks, and follow-up work. - Acceptance and Rejection Gates: Provides explicit criteria for rejecting work that is untested, insecure, hardcoded with secrets, or inconsistent with repository architecture. - Use Case: A TPO agent assigns a feature to an AI developer agent, then uses these standards to verify the submission includes validation, authorization checks, tests, and a migration plan before accepting it. ## Quick Start Ask the agent to review the latest implementation against the coding standards and list any acceptance rule violations.
