Skill UI

This skill provides robust, automated guardrails for web scraping pipelines using Firecrawl. It enforces critical policies to ensure compliance and stability, including domain blocklists (preventing scraping prohibited sites), managing API credit budgets, filtering low-quality content (e.g., error pages, thin content), and implementing granular per-domain rate limiting. Use this when building highly reliable, legal, and cost-efficient scraping solutions.

This guide provides advanced techniques for managing API rate limits (429 errors) and complexity budgets when interacting with the Linear API. It covers reading rate limit headers, implementing exponential backoff with jitter for robust retries, controlling request bursts via token buckets, and optimizing GraphQL queries to minimize complexity scores. Use this when developing reliable integrations that must adhere to strict API quotas.

This skill specializes in implementing Service Level Objectives (SLOs) and Service Level Indicators (SLIs) to enforce reliability standards. It guides users in designing comprehensive SLO frameworks, calculating error budgets, and building robust monitoring systems. Use this when you need to establish data-driven reliability targets, balance feature velocity with technical health, and align service performance directly with core business objectives.

This framework provides a comprehensive methodology for defining, measuring, and implementing Service Level Indicators (SLIs), Service Level Objectives (SLOs), and managing error budgets. It helps engineering teams quantify service reliability (e.g., availability, latency) and establish clear, measurable targets (SLOs). By tracking error budgets, teams can strategically balance rapid feature development (innovation velocity) against maintaining high system stability and reliability.

Provides SRE guidance for defining SLIs/SLOs, managing error budgets, automating toil, and configuring monitoring/alerting so teams can scale reliable services and respond to incidents effectively.

Defines and monitors SLAs, SLIs, and SLOs so teams can set performance targets, track metrics like error budgets and availability, and react before issues impact customers.

This comprehensive guide outlines best practices for building reliable and robust AI agent harnesses. It covers optimizing action spaces, defining structured tools, managing observation formats, and implementing essential error recovery contracts. Use these principles to dramatically improve agent completion rates, manage context budgets effectively, and transition AI prototypes into stable, production-grade autonomous systems.

A utility designed for robust blockchain interactions using the Alchemy API. It implements CU-aware throttling based on Compute Unit costs, manages concurrent requests, and includes advanced retry logic for handling 429 Too Many Requests errors. Ideal for optimizing high-volume, reliable Web3 applications that must adhere to strict API rate limits and usage budgets.

A senior-level tool for performing deep-dive technical SEO audits and site architecture mapping. It analyzes crawl health, indexing blocks, sitemap integrity, and structural issues (like canonicalization, link flow, and crawl budget waste). Use this to diagnose complex Google Search Console errors and propose a full, engineered roadmap for website stabilization and optimization.

A comprehensive production readiness checklist for integrating SerpApi into live applications. This guide covers critical steps including secure API key management, setting up usage monitoring and credit budgets, implementing robust error handling, and optimizing performance for high availability. Use this when deploying or upgrading search features powered by SerpApi.

This skill enforces disciplined SLO definition based on SRE principles, ensuring that reliability promises are measurable and actionable. It guides users in selecting the right SLI (Service Level Indicator), accurately calculating error budgets, setting multi-window burn-rate alerts, and reviewing existing SLO documents for common architectural flaws (e.g., targets too aggressive, wrong SLI selection). Ideal for defining, reviewing, and maintaining service reliability commitments.

A comprehensive guide for optimizing SEO indexing and managing crawl budget specifically for Next.js applications. Covers fixing Google Search Console coverage errors, implementing proper canonical tags, auditing sitemaps, managing static rendering, and optimizing internal linking for maximum search visibility.