Rate Limiter

Overview

This skill enables AI agents to design, implement, and configure production-grade rate limiting for APIs. It covers algorithm selection, middleware generation, Redis-backed distributed counting, abuse pattern detection, and proper HTTP response headers.

Instructions

1. Assess the API Surface

Before writing any code, analyze the target application:

• List all public endpoints and their HTTP methods
• Classify endpoints by sensitivity: authentication (highest), write operations (high), read operations (medium), static/health (low)
• Identify existing middleware stack and framework (Express, Fastify, Django, Gin, etc.)
• Check if Redis or another shared store is available for distributed rate limiting

2. Choose the Right Algorithm

Select based on the use case:

Default recommendation: Sliding Window Counter — it handles 95% of use cases with good accuracy and reasonable memory usage.

3. Implement Layered Limits

Always implement at least two layers:

Layer 1 — Global IP limit: Catches volumetric abuse before authentication. Typical: 100-300 req/min per IP.

Layer 2 — Endpoint-specific limits: Different limits per endpoint category. Auth endpoints get the strictest limits (3-10 req/min).

Layer 3 — Authenticated user quotas (if applicable): Daily or hourly caps per API key or user ID. Return quota status in response headers.

4. Response Headers

Always include these headers on EVERY response (not just 429s):

X-RateLimit-Limit: 60
X-RateLimit-Remaining: 45
X-RateLimit-Reset: 1708012800
Retry-After: 30  (only on 429 responses)

5. Abuse Detection Patterns

Beyond simple counting, detect:

• Credential stuffing: Many unique usernames from one IP on auth endpoints
• Scraping: Sequential access patterns on listing endpoints
• Header anomalies: Missing User-Agent, rotating headers on rapid requests
• Distributed attacks: Same user-agent/fingerprint across many IPs

6. Graceful Degradation

Always implement a fallback when Redis is unavailable:

• Fall back to in-memory rate limiting per process
• Log the Redis failure prominently
• Do NOT fail open (allowing unlimited requests) unless explicitly configured

7. Avoid Common Pitfalls

• Trust proxy configuration: Always verify how

  X-Forwarded-For

  is parsed. Behind a load balancer, the client IP may not be

  req.ip

• Shared NAT: Don't set per-IP limits too low — corporate networks may have thousands of users behind one IP. Minimum 100 req/min for non-auth endpoints
• Key design: Use composite keys (

  ip:endpoint

  ,

  userId:endpoint

  ) not just IP alone
• Time sync: Use Redis server time (

  TIME

  command) not local clock for distributed setups

Examples

Example 1: Express middleware with Redis sliding window

Prompt: "Add rate limiting to my Express API. Auth endpoints should allow 5 requests per minute per IP. All other endpoints 60 per minute per authenticated user."

Output: The agent creates

src/middleware/rateLimiter.ts

import { Redis } from 'ioredis';
import { Request, Response, NextFunction } from 'express';

const redis = new Redis(process.env.REDIS_URL);

interface RateLimitConfig {
  windowMs: number;
  max: number;
  keyGenerator: (req: Request) => string;
}

const configs: Record<string, RateLimitConfig> = {
  auth: {
    windowMs: 60_000,
    max: 5,
    keyGenerator: (req) => `rl:auth:${req.ip}`,
  },
  default: {
    windowMs: 60_000,
    max: 60,
    keyGenerator: (req) => `rl:api:${(req as any).userId ?? req.ip}`,
  },
};

export function rateLimit(category: keyof typeof configs) {
  const config = configs[category];
  return async (req: Request, res: Response, next: NextFunction) => {
    const key = config.keyGenerator(req);
    const now = Date.now();
    const windowStart = now - config.windowMs;

    const multi = redis.multi();
    multi.zremrangebyscore(key, 0, windowStart);
    multi.zadd(key, now.toString(), `${now}:${Math.random()}`);
    multi.zcard(key);
    multi.expire(key, Math.ceil(config.windowMs / 1000));
    const results = await multi.exec();

    const count = results![2][1] as number;
    res.set('X-RateLimit-Limit', config.max.toString());
    res.set('X-RateLimit-Remaining', Math.max(0, config.max - count).toString());
    res.set('X-RateLimit-Reset', Math.ceil((now + config.windowMs) / 1000).toString());

    if (count > config.max) {
      res.set('Retry-After', Math.ceil(config.windowMs / 1000).toString());
      return res.status(429).json({ error: 'Too many requests' });
    }
    next();
  };
}

Example 2: Abuse detection for credential stuffing

Prompt: "Detect credential stuffing on my login endpoint. Block IPs that try more than 20 unique usernames in 5 minutes."

Output: The agent adds a detection middleware that tracks unique username attempts per IP using a Redis HyperLogLog, blocking IPs that exceed the threshold and logging the event with full request metadata for incident response.

Guidelines

• Always test rate limiting with concurrent requests — race conditions in counter logic are common
• Include integration tests that verify 429 responses and header values
• Document the rate limits in your API documentation or OpenAPI spec
• Consider offering rate limit increase for paid tiers via configuration, not code changes
• Log all rate limit events in structured format for security monitoring