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Api Rate Limiting

Checked
aj-geddes·vlatest·backend·MIT

Provides API guidance and assistance for agent workflows.

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add aj-geddes/useful-ai-prompts --skill api-rate-limiting -a claude-code
  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

aj-geddes

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned aj-geddes/useful-ai-prompts at 3f5182cfd7.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Scanned
9 files, 440 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill provides guidance and assistance for implementing API rate limiting strategies within agent workflows. It enables protection of APIs from abuse, management of traffic spikes, and enforcement of usage limits using various algorithms like Token Bucket and Sliding Window. The skill includes reference implementations and best practices for different scenarios, including distributed systems utilizing Redis.

When to use it

  • Protecting APIs from brute force attacks
  • Managing traffic spikes
  • Implementing tiered service plans
  • Preventing DoS attacks

Key capabilities

  • Implementation of Token Bucket rate limiting algorithm.
  • Implementation of Sliding Window rate limiting algorithm.
  • Guidance on Redis-based rate limiting for distributed environments.
  • Strategies for tiered rate limiting based on user plans.
  • Recommendations for including rate limit headers in API responses.

Example prompts

  • "Implement a Token Bucket rate limiter with a capacity of 10 tokens and a refill rate of 2 tokens per second."
  • "How can I use Redis to implement distributed rate limiting?"
  • "What are the best practices for setting appropriate window sizes for a Sliding Window algorithm?"

Tips & gotchas

  • For production environments, avoid using in-memory storage. Utilize a distributed solution like Redis.
  • Always include rate limit headers in API responses and consider providing a Retry-After header when limits are exceeded.
  • Be mindful of distributed scenarios and avoid simple counters for managing rate limits across multiple systems.