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Found 185 Skills
Comprehensive REST and GraphQL API design patterns with versioning, pagination, error handling, and HATEOAS principles. Use when designing APIs, defining endpoints, or architecting service contracts requiring production-grade patterns.
Design clean, consistent APIs. Use when creating new endpoints, defining contracts, or improving API ergonomics. Covers REST, versioning, and error handling.
Writes and reviews Conventional Commits commit messages (v1.0.0) to support semantic versioning and automated changelogs. Use when drafting git commit messages, PR titles, release notes, or when enforcing a conventional commit format (type(scope): subject, BREAKING CHANGE, footers, revert).
Designs and builds reusable Terraform modules. Use when creating reusable infrastructure patterns, encapsulating complex resource groups, standardizing configurations across projects, or organizing code for maintainability. Covers module structure, versioning, composition, and best practices for production modules.
Comprehensive GitHub release orchestration with AI swarm coordination for automated versioning, testing, deployment, and rollback management
Cut a release — detect versioning context, generate a changelog from conventional commits, bump versions, and create a git tag. Use when the user says "release", "cut a release", "tag a release", "bump the version", "create a changelog", "ship a version", "publish", or any variation of shipping/publishing a version. This skill is intentionally generic and works across any repo — it infers context from git history and project structure rather than assuming a specific setup.
API Gateway patterns (Kong, Traefik, AWS API Gateway) — rate limiting, auth, routing, versioning. Use when implementing API gateway, reverse proxy, or API management.
Manage database migrations and schema versioning. Use when planning migrations, version control, rollback strategies, or data transformations in PostgreSQL and MySQL.
REST/GraphQL API architect specializing in OpenAPI 3.1, HATEOAS, pagination, and versioning strategies
Expert in Machine Learning Operations bridging data science and DevOps. Use when building ML pipelines, model versioning, feature stores, or production ML serving. Triggers include "MLOps", "ML pipeline", "model deployment", "feature store", "model versioning", "ML monitoring", "Kubeflow", "MLflow".
REST/GraphQL/gRPC API design best practices. Use when designing APIs, defining contracts, handling versioning. Covers OpenAPI 3.2, GraphQL Federation, gRPC streaming.
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.