Does this skill include client-side implementation advice?

Yes, it covers best practices for graceful degradation and exponential backoff strategies when clients receive a 429 Too Many Requests response.

How does this skill handle distributed rate limiting?

It provides patterns for centralized coordination using Redis Lua scripts, ensuring consistency across multiple application nodes.

Which rate limiting algorithm should I choose for a standard REST API?

The Token Bucket algorithm is generally the best choice for APIs as it allows for controlled bursts while maintaining a steady long-term rate.

What is the difference between rate limiting and quota management?

Rate limiting focuses on preventing short-term spikes (e.g., requests per second), while quotas manage long-term usage budgets (e.g., requests per month).

Rate Limiting Design Patterns

Name: Rate Limiting Design Patterns
Author: melodic-software

bymelodic-software

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API Development

Implements robust API protection through standardized rate limiting, throttling, and quota management patterns.

About

This skill provides Claude with expert-level guidance on protecting web services and APIs from abuse, DDoS attacks, and resource exhaustion. It covers essential algorithms such as Token Bucket and Sliding Window Counter, offers implementation strategies for distributed environments using Redis, and ensures compliance with standard HTTP rate limit headers. It is an indispensable resource for backend developers designing scalable, resilient systems that require fair resource allocation, cost control, and predictable performance.

Key Features

12 GitHub stars
Comparison guide for selecting the optimal algorithm based on burst handling and memory
Implementation of Token Bucket, Leaky Bucket, and Sliding Window algorithms
Multi-tier limiting patterns covering global, per-tenant, and per-endpoint levels
Strategies for distributed rate limiting in multi-node architectures using Redis
Standardized HTTP response header configurations (X-RateLimit, Retry-After)

Use Cases

Protecting public-facing APIs from malicious brute-force or DDoS attacks
Preventing downstream service failures through traffic smoothing and leaky bucket patterns
Managing API quotas for different subscription tiers in SaaS applications

About

Key Features

12 GitHub stars
Comparison guide for selecting the optimal algorithm based on burst handling and memory
Implementation of Token Bucket, Leaky Bucket, and Sliding Window algorithms
Multi-tier limiting patterns covering global, per-tenant, and per-endpoint levels
Strategies for distributed rate limiting in multi-node architectures using Redis
Standardized HTTP response header configurations (X-RateLimit, Retry-After)

Use Cases

Protecting public-facing APIs from malicious brute-force or DDoS attacks
Preventing downstream service failures through traffic smoothing and leaky bucket patterns
Managing API quotas for different subscription tiers in SaaS applications