ai-health-check

What problem does it solve?

Many AI-driven product features ship with critical gaps like missing cost models, poor data quality, no monitoring, and unusable failure UX; this skill enforces a pre-launch audit to prevent shipping those broken features. It helps product teams catch blockers and risks early so launches are safe, affordable, and monitorable.

Core Features & Use Cases

• Six-dimension audit: Grades Model Selection, Data Quality, Cost Modeling, Production Monitoring, Failure UX, and System Optimization as Ready / Risk / Blocker.
• Automated guidance: Invokes an implementation auditor agent, surfaces hard questions, provides an overall verdict, and recommends remediation steps.
• Use case: Run before a sprint release to validate an email composer, recommendation engine, or any ML-driven feature to decide whether to ship or iterate.

Quick Start

Run the ai-health-check by entering the slash command /ai-health-check followed by a concise feature name (for example: /ai-health-check "email composer AI") to receive a graded readiness report.