Results for “versioning”

Expert API designer for REST, GraphQL, gRPC architectures. Activate on: API design, REST API, GraphQL schema, gRPC service, OpenAPI, Swagger, API versioning, endpoint design, rate limiting, OAuth flow. NOT for: database schema (use data-pipeline-engineer), frontend consumption (use web-design-expert), deployment (use devops-automator).

Reproduces an existing design as a faithful, self-contained HTML file, then generates and evaluates design variations to recommend the strongest option.

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.

Designs REST and GraphQL APIs following production best practices for resource naming, error handling, versioning, authentication, and documentation. Use this skill when designing new API endpoints, reviewing API contracts, implementing GraphQL schemas, establishing API conventions for a project, or writing API documentation. Apply when creating any route in Next.js API routes or route handlers, any Supabase Edge Function, or any backend endpoint — even if it starts small, these patterns prevent painful rewrites later.

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.