What problem does it solve? AI agents acting as Technical Product Owners often produce vague, generic outputs that developers cannot execute and owners cannot verify. This Skill supplies concrete, high-quality output examples covering the full product lifecycle so every deliverable is structured, specific, actionable, and testable. ## Core Features & Use Cases - Output Benchmarks: Reference examples for product analysis, PRDs, architecture plans, module decomposition, roadmaps, sprint plans, developer tasks, acceptance criteria, UX reviews, code reviews, security reviews, bug tickets, and release readiness. - Decision Vocabulary: Standardized decision statuses (Approved, Approved with fixes, Needs revision, Blocked, Rejected) plus a scoring rubric for judging AI Developer Agent output. - Reusable Templates: Copy-ready blocks for architecture plans, developer tasks, reviews, and release gates. - Use Case: When asked to review an AI developer's implementation of a Telegram intake bot, produce a structured code review with severity-ranked issues, required fixes, and an acceptance gate instead of a vague "looks good" response. ## Quick Start Use the examples-of-good-outputs guidelines to write a structured code review of the attached developer implementation with a clear decision status.
