Code review, debugging, architecture, testing and docs. 6 reviewed prompts for engineering.
Checks a release against a readiness list, names the biggest risks, and plans staged rollout with stop thresholds and ordered rollback steps. Use before a production launch.
Recommends a practical build, test and deployment pipeline for your app, sized to your team and risk level, with a phased rollout. Use when setting up deployment for the first time or fixing slow, manual releases.
Designs how a machine learning model is trained, validated, deployed, monitored, and retired in production, including drift detection, rollback, and governance. Use when moving a model from notebook to production or fixing a fragile pipeline.
Plans how to run an AI model on your own hardware: memory estimate, model size and compression level, runtime setup, expected speed, risks and a benchmark checklist. Use it when privacy or offline use rules out cloud services.
Designs a pipeline strategy for building, testing and deploying multiple independent services from a single repository, with rollout and rollback rules. Use when starting or untangling a delivery pipeline.
Produces a ranked list of likely causes and concrete fixes when a single-page web app shows a blank screen in production but works locally. Use it right after a failed or odd deploy.
