Does this skill support different FireCrawl account tiers?

Yes, it includes logic tailored to Free, Pro, and Enterprise tiers, allowing the agent to adjust request-per-minute and daily burst limits based on your specific plan.

Why are idempotency keys important for FireCrawl?

Idempotency keys ensure that if a request is retried due to a timeout or network error, FireCrawl recognizes it as the same operation, preventing duplicate processing and unnecessary credit usage.

How does this skill handle 429 'Too Many Requests' errors?

It implements an automated exponential backoff algorithm that progressively increases wait times between retries, including a random jitter to prevent synchronized retry spikes.

Can I use this for high-concurrency scraping?

Yes, the skill provides patterns for queue-based rate limiting using P-Queue to manage concurrency and stay within specified interval caps automatically.

FireCrawl Rate Limit Manager

Name: FireCrawl Rate Limit Manager
Author: micsapp

bymicsapp

0•

웹 스크래핑 및 데이터 수집

Implements robust rate limiting, exponential backoff, and idempotency patterns for resilient FireCrawl API interactions.

The FireCrawl Rate Limit Manager skill provides Claude with the specialized knowledge to build stable, high-performance web scraping integrations. It implements advanced resilience patterns including exponential backoff with jitter, deterministic idempotency keys for safe retries, and header-based monitoring for X-RateLimit limits. By providing standardized logic for different FireCrawl tiers (Free, Pro, Enterprise) and queue-based concurrency management, this skill ensures that your data collection workflows maximize throughput while gracefully handling API throttling and network fluctuations.

주요 기능

01Header monitoring for X-RateLimit-Limit, Remaining, and Reset values

02Tier-aware configuration for Free, Pro, and Enterprise limit thresholds

03Deterministic idempotency key generation for safe request retries

040 GitHub stars

05Exponential backoff with randomized jitter to prevent thundering herd issues

06Queue-based concurrency control via p-queue implementation

사용 사례

01Ensuring data integrity during network failures using idempotent requests

02Optimizing throughput for high-volume data extraction pipelines

03Scaling web scraping operations without hitting hard API blocks

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add micsapp/micstec-skills firecrawl-rate-limits

For use in Claude.ai and ChatGPT

주요 기능

01Header monitoring for X-RateLimit-Limit, Remaining, and Reset values

02Tier-aware configuration for Free, Pro, and Enterprise limit thresholds

03Deterministic idempotency key generation for safe request retries

040 GitHub stars

05Exponential backoff with randomized jitter to prevent thundering herd issues

06Queue-based concurrency control via p-queue implementation

사용 사례

01Ensuring data integrity during network failures using idempotent requests

02Optimizing throughput for high-volume data extraction pipelines

03Scaling web scraping operations without hitting hard API blocks

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add micsapp/micstec-skills firecrawl-rate-limits

For use in Claude.ai and ChatGPT