rate-limiting

Configures Rate-Limiting algorithms such as token bucket, sliding window and leaky bucket for APIs.

53|1|Updated Dec 18, 2025

Search Tags:#redis #rate-limiting #throttling #backpressure #sliding-window #token-bucket

Ready to run

npx skills add https://github.com/cosmix/claude-code-setup --skill rate-limiting-cosmix

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rate-limiting
Source: https://github.com/cosmix/claude-code-setup/tree/main/skills/rate-limiting
Command: npx skills add https://github.com/cosmix/claude-code-setup --skill rate-limiting-cosmix

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

API rate limiting and quota management implementation to prevent abuse, enable fair usage, and maintain system stability. This skill covers algorithms, patterns, and practical integration.

Core Features & Use Cases

Algorithms: Token Bucket, Sliding Window, Leaky Bucket.
Quota Management: Per-user and per-API rate controls with backpressure.
Distributed Patterns: Redis-backed sliding windows and per-IP controls.

Quick Start

Implement a token bucket limiter for an API endpoint and test burst handling.

Frequently Asked Questions about rate-limiting

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I implement rate limiting to prevent API abuse?▼

Rate limiting restricts the number of requests a client can make within a time window, preventing abuse and protecting service stability. Token bucket, sliding window, and leaky bucket are common algorithms; this Skill implements all three with Redis-backed state for distributed systems, per-client quotas, and burst handling.

What's the best way to apply rate limiting across microservices?▼

Distributed rate limiting uses Redis-backed state to enforce quotas consistently across multiple services and API gateways. This Skill provides patterns for per-IP controls, per-user quotas, and backpressure signaling, enabling fair usage policies in multi-tenant environments without duplicating state.

Can I use Redis for rate-limiting in a distributed system?▼

Yes. Redis provides fast, shared state management for rate-limiting algorithms across microservices. This Skill implements Redis-backed sliding windows and token bucket patterns, allowing you to maintain consistent quotas and reset times across distributed API gateways and multi-tenant deployments.

How do token bucket and sliding window algorithms differ?▼

Token bucket allows burst traffic up to a capacity limit before throttling; sliding window tracks request counts over a rolling time period for stricter enforcement. This Skill implements both, letting you choose based on whether your use case prioritizes burst flexibility or precise request distribution.

What information does rate limiting expose about remaining capacity?▼

Rate-limiting observability includes remaining quota, reset time, and backpressure signals, helping clients understand throttling status. This Skill provides these metrics for burst handling and fair client behavior, enabling API consumers to implement smart retry logic and request scheduling.

Do I need backpressure handling for API rate limits?▼

Backpressure prevents cascading failures when upstream services reject requests due to rate limits. This Skill includes backpressure patterns alongside per-client quotas and reset-time signaling, critical for stable multi-service architectures and preventing thundering-herd effects.