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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/rate-limiter-designer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How rate-limiter-designer Compares
| Feature / Agent | rate-limiter-designer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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
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