rate-limiter-designer

Design and implement rate limiting strategies

509 stars

Best use case

rate-limiter-designer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Design and implement rate limiting strategies

Teams using rate-limiter-designer should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/rate-limiter-designer/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/sdk-platform-development/skills/rate-limiter-designer/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/rate-limiter-designer/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How rate-limiter-designer Compares

Feature / Agentrate-limiter-designerStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Design and implement rate limiting strategies

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Rate Limiter Designer Skill

## Overview

This skill designs and implements rate limiting strategies including token bucket, sliding window, and quota systems to protect APIs while providing fair access.

## Capabilities

- Implement token bucket and leaky bucket algorithms
- Configure per-key and per-user limits
- Design quota and usage systems
- Generate rate limit HTTP headers
- Implement distributed rate limiting
- Configure burst allowances
- Design rate limit tiers
- Handle rate limit exceeded responses

## Target Processes

- Platform API Gateway Design
- Authentication and Authorization Patterns
- Developer Portal Implementation

## Integration Points

- Redis for distributed state
- Rate limiting middleware
- API gateway plugins
- CDN rate limiting
- Database-backed quotas

## Input Requirements

- Rate limit requirements
- Tier definitions
- Burst allowances
- Distribution strategy
- Header conventions

## Output Artifacts

- Rate limiting implementation
- Quota management system
- Rate limit headers
- Tier configuration
- Admin management API
- Usage tracking

## Usage Example

```yaml
skill:
  name: rate-limiter-designer
  context:
    algorithm: sliding-window
    storage: redis
    tiers:
      - name: free
        requests: 100
        window: 1h
        burst: 10
      - name: pro
        requests: 10000
        window: 1h
        burst: 100
    headers:
      limit: X-RateLimit-Limit
      remaining: X-RateLimit-Remaining
      reset: X-RateLimit-Reset
    responses:
      exceeded:
        status: 429
        retryAfter: true
```

## Best Practices

1. Use sliding window for accuracy
2. Include burst allowances
3. Return standard rate limit headers
4. Provide clear Retry-After values
5. Implement distributed limiting
6. Design fair quota systems

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