Skill UI

Guides REST API teams on naming resources, versioning, pagination, OpenAPI specs, error handling, headers, and rate limiting to keep services consistent, scalable, and client-friendly.

Guides REST and GraphQL API architects through domain modeling, OpenAPI 3.1 contract drafting, pagination, versioning, error handling, and mock verification.

A comprehensive guide to modern API design principles and architectural decision-making. Learn how to select the right pattern (REST, GraphQL, tRPC), implement robust versioning strategies, handle pagination, define standardized response formats, and incorporate essential security measures like authentication and rate limiting. Essential for building scalable and reliable backend services.

This comprehensive SDK provides the .NET framework bindings to manage the Azure API Center service. It enables centralized API inventory management, governance, versioning, and lifecycle control (creation, definition upload, environment deployment). Ideal for building robust, scalable API management pipelines within the Azure ecosystem.

Guides REST and GraphQL API creation with endpoint planning, versioning, auth, rate limits, error handling, pagination, and OpenAPI docs to keep designs consistent when building or reviewing services.

Guides building transactional and marketing email systems that balance automation, deliverability, preference centers, and template versioning so every send hits inboxes and scales with growing infrastructure needs.

Transforms OpenAPI specifications into comprehensive, interactive documentation sites. This skill generates runnable code examples (cURL, Python, JS, Go), detailed authentication guides, error references, and supports versioning. It is crucial for building professional developer portals that guide users through API usage seamlessly.

This comprehensive guide covers the entire MLOps lifecycle, specializing in building robust, scalable, and production-grade Machine Learning systems. Expertise includes orchestrating complex ML pipelines using Kubeflow, Airflow, Prefect, and cloud-native services (AWS SageMaker, Azure ML, GCP Vertex AI). It covers experiment tracking (MLflow, W&B), model versioning, model registries, infrastructure as code (Terraform), and container deployment using Kubernetes. Ideal for engineers tasked with moving ML models from research to reliable, managed production environments.

Provides automated guidance for managing model versions in ML deployment, including serving, monitoring, and production optimization whenever someone requests the model versioning manager.

This comprehensive deep dive guides you through the entire database migration lifecycle using the Supabase CLI. Learn best practices for executing zero-downtime schema changes, safely backfilling data in production, managing schema versioning across environments, and implementing robust rollback strategies. Covers advanced PostgreSQL concepts like concurrent indexing and multi-phase column renaming.

Enables Claude to manage AI/ML model versions through the model-versioning-tracker plugin, automating logging, lineage tracking, performance summaries, and version retrieval whenever users request registry or deployment support.

This skill provides a comprehensive guide to implementing robust API versioning strategies, supporting methods like URL path, custom headers, and query parameters. It covers building backward compatibility layers, adding standard deprecation notices (e.g., Sunset Header), and utilizing OpenAPI specifications to detect breaking changes, ensuring stable API evolution over time.