What problem does it solve? Services can be overwhelmed by abusive or accidental traffic spikes, whether from DoS attacks or misbehaving client scripts. This Skill provides concrete patterns for enforcing request limits so a single client cannot degrade the service for everyone. ## Core Features & Use Cases - Multiple Limiting Strategies: Covers fixed window, sliding window, and token bucket algorithms with guidance on when each applies. - Server-Side Enforcement: Shows in-process limiting with Resilience4j configuration and Bucket4j Java code for local buckets. - Distributed Limiting: Explains Redis-backed Bucket4j counters so multi-node deployments share a global limit instead of multiplying it per pod. - Use Case: A public REST API running on three Kubernetes pods needs a per-user limit of 100 requests per minute. Use Redis + Bucket4j so the limit holds globally, and return 429 with a Retry-After header when exceeded. ## Quick Start Apply the rate-limiting skill to add a Bucket4j-based per-user limit with a 429 response to my REST controller.
