Rate Limiting Implementation

This walks you through implementing rate limiting from token buckets to sliding windows, with examples in TypeScript and Python. You get Redis-based distributed rate limiting for multi-server setups, Express middleware you can drop in, and patterns for tiered limits and adaptive throttling. The best practices section is solid on the operational details: returning 429s with Retry-After headers, handling burst traffic, and not storing state in memory when you're running multiple instances. If you're building a public API or need to prevent abuse without breaking legitimate users during traffic spikes, the reference implementations cover the algorithms that actually matter in production.

skills.sh installs: 507
GitHub stars: 379
last updated: Aug 21, 2026

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Install

$ npx -y skills add aj-geddes/useful-ai-prompts --skill rate-limiting-implementation --agent claude-code

Installs into .claude/skills of the current project.

Files

Overview

Implement rate limiting and throttling mechanisms to protect your services from abuse, ensure fair resource allocation, and maintain system stability under load.

When to Use

Protecting public APIs from abuse
Preventing DOS/DDOS attacks
Ensuring fair resource usage across users
Implementing API quotas and billing tiers
Managing system load and backpressure
Enforcing SLA limits
Controlling third-party API usage
Database connection management

Quick Start

Minimal working example:

interface TokenBucketConfig {
  capacity: number;
  refillRate: number; // tokens per second
  refillInterval: number; // milliseconds
}

class TokenBucket {
  private tokens: number;
  private lastRefill: number;
  private readonly capacity: number;
  private readonly refillRate: number;
  private readonly refillInterval: number;
  private refillTimer?: NodeJS.Timeout;

SKILL.md on GitHub