Results for “prompt-versioning”

3 skills

Design and implement ML operations — model registry, serving patterns, deployment strategies (shadow/canary/blue-green), drift detection, feature stores, retraining triggers, and prediction monitoring. Use when asked to "deploy a model", "model registry", "MLflow", "feature store", "drift detection", "retrain trigger", "shadow mode", "model versioning", "serving infrastructure", or "ML pipeline". Do NOT use for: prompt engineering or RAG pipelines — see prompt-engineering and rag-architect skills. Do NOT use for: general API deployment without an ML component.

More results

Build multiple genuinely different versions of a UI piece you describe, rendered behind a visual picker so you can flip through them live and promote the one that feels right. Use this proactively, without waiting to be asked, whenever evaluating a new feature, a layout, a section, a visual, a design, or any UI decision — divergent options beat a single guess. Fast, no interview, single-component scope. For a full structured exploration (interview, 5 variants of a whole page, real feedback collection, implementation plan) when the direction itself is still open, use `design-lab` instead — that one requires explicit invocation. Source: github.com/emilkowalski/skills.

Render a proposed redesign of a page as a self-contained static HTML file, then iterate via chat-driven commands until approved.