Skill UI

This tool detects profitable arbitrage opportunities across centralized exchanges (CEX), decentralized exchanges (DEX), and cross-chain markets in real-time. It aggregates price data, calculates net profit after fees, and identifies various arbitrage paths, including direct, triangular, and cross-chain opportunities. It is designed for quantitative traders needing to monitor market efficiency and exploit price discrepancies.

This workflow provides the primary mechanism for converting complex, live websites into clean, LLM-ready markdown content. It supports single-page scraping using `scrapeUrl`, deep multi-page site crawling with `crawlUrl`, and scalable async crawling for large datasets. Ideal for building robust content ingestion pipelines and reliably extracting structured information from the web.

A comprehensive pipeline for processing, validating, and optimizing scraped web content from Firecrawl. This tool handles markdown cleaning, structured data extraction using Zod validation, content deduplication, and chunking tailored for Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. It ensures crawled data is standardized, clean, and ready for downstream consumption or knowledge base indexing.

Master the implementation of Firecrawl webhooks for robust, real-time handling of asynchronous crawling and batch scraping jobs. Instead of inefficiently polling the crawl status, configure webhook endpoints to receive instant POST events (e.g., `crawl.page`, `crawl.completed`). This guide covers event types, signature verification using HMAC-SHA256, and processing streamed data for building advanced data pipelines.

Enables Claude to analyze historical time series patterns, select the best forecasting model, and predict future values with confidence intervals for sales, traffic, or other time-dependent metrics.

This advanced workflow enables users to perform deep analyses on large-scale people intelligence datasets. Users can build custom queries with multi-dimensional filters, run comparisons across different candidate pools (e.g., Q1 vs Q4), and aggregate skill density by geographical region. It is essential for talent acquisition teams needing to identify market trends and optimize hiring strategies beyond standard search functions.

This bundle collects comprehensive diagnostic evidence from the Juicebox platform. It gathers API connectivity status, dataset health reports, analysis quota usage, and rate limit information into a single archive. Use it to systematically troubleshoot complex issues such as failed dataset uploads, stalled AI analysis runs, API authentication failures, or quota exhaustion.

This skill provides a comprehensive guide and implementation for deploying a production-ready, containerized integration service using Docker. It connects to the Juicebox API to manage datasets, execute advanced AI-powered analyses, and retrieve structured business insights. The service includes health check mechanisms and supports zero-downtime rolling updates, ensuring reliable real-time data processing and analysis.

This guide provides advanced strategies for optimizing interactions with the Juicebox AI API. It covers crucial best practices such as implementing caching mechanisms (with defined TTLs), executing batch operations for efficiency, managing rate limits, and optimizing data chunking for large datasets. Following these steps can significantly reduce end-to-end analysis time and ensure responsive, scalable data workflows.

This guide provides comprehensive patterns and best practices for designing high-impact Key Performance Indicator (KPI) dashboards. It covers everything from establishing a KPI framework (Strategic, Tactical, Operational) to defining departmental metrics (Sales, Marketing, Finance) and implementing best-practice layouts for executive summaries and SaaS metrics. Use this skill to translate raw data into actionable business insights.

This guide details how to implement comprehensive observability for AI agents. It covers monitoring key metrics such as task success rates, step failures, execution duration, and credit consumption. Use this when setting up performance dashboards, configuring real-time alerts (via webhooks/Prometheus), or tracking agent reliability over time.

Auto-activating skill guiding metadata extractor implementations within data pipelines, covering ETL, transformation, orchestration, and streaming best practices.