Use when designing or changing REST, gRPC, GraphQL, webhook, event, DTO, SDK, OpenAPI, protobuf, or public interface contracts. Focus on compatibility, validation, error semantics, pagination, versioning, and generated artifacts.
Use when designing or changing backend APIs, services, repositories, workers, jobs, external clients, auth flows, configs, or service boundaries. Focus on contracts, ownership, data access, reliability, tests, and operations.
Use when designing or changing database schemas, migrations, indexes, ORM models, query patterns, transactions, consistency, data retention, partitioning, backfills, or storage choices for backend or ML systems.
Use when implementing or reviewing deep learning training, fine-tuning, evaluation, inference, GPU performance, batching, quantization, checkpointing, distributed training, model packaging, or deployment behavior.
Use when work involves retries, idempotency, queues, events, workflows, consistency, timeouts, cancellation, circuit breakers, backpressure, rate limits, duplicate delivery, partial failure, or cross-service reliability.
Use when designing, reviewing, or changing machine learning systems, recommendation/ranking/search models, prediction services, feature pipelines, training pipelines, model serving, evaluation, monitoring, drift handling, or ML rollout strategy.
Use when designing or reviewing ML/data pipelines, feature pipelines, data validation, schema drift, train/serve skew, labels, backfills, lineage, monitoring, model registry flows, or data-quality incidents.
Use when investigating bugs, production issues, flaky tests, regressions, logs, metrics, traces, dashboards, alerts, runbooks, or when adding instrumentation to make backend, performance, or ML behavior diagnosable.