Skill UI

This skill provides a comprehensive guide to instrumenting Algolia search services for enterprise-grade observability. It integrates core monitoring patterns, including Prometheus metrics (latency, error rates, request counts), OpenTelemetry distributed tracing, and structured logging. Use this to achieve deep insights into search performance, user experience, and system health in production environments.

This skill aggregates real-time financial information, including hot news articles and market trends from multiple sources (e.g., Weibo, Zhihu). It is also capable of retrieving structured data from prediction markets like Polymarket. Use this when you need a unified, comprehensive view of the financial market landscape, spanning breaking news, aggregated sentiment, and forward-looking market predictions.

This skill provides a comprehensive workflow for generating professional and structured financial reports. It guides the user through planning, writing, and editing various sections, and can also generate necessary chart configurations using visual tools. Ideal for condensing complex financial analysis into clear, actionable reports.

This advanced workflow allows developers to clone live websites directly into structured React or HTML code using the Anima platform. It goes beyond simple cloning by featuring post-generation customization rules (e.g., updating utility classes or imports) and a dedicated design token mapper. This ensures the generated code is immediately usable and adheres strictly to the project's existing design system tokens, while also organizing the final component structure.

This guide details production-ready patterns for integrating the Anima SDK into automated design-to-code pipelines. It covers best practices such as implementing a singleton client, managing generation cache, normalizing output for project conventions (e.g., TSX structure, utility classes), and incorporating retry logic for API resilience. Use it when building robust, reusable Anima client wrappers for enterprise applications.

This guide demonstrates how to implement tool use (function calling) with Claude's Messages API. It covers defining structured tools using JSON Schema, handling tool calls, executing the corresponding code, and feeding the results back to create sophisticated, multi-step AI agents and complex workflows.

This skill guides the secure deployment of Claude API integrations into production cloud environments. It provides comprehensive instructions for implementing services across multiple infrastructures, including Docker, GCP Cloud Run, and Kubernetes. The guide covers best practices such as secret management, health checks, and reliable rollback procedures to ensure high service reliability.

This module provides comprehensive observability for Anthropic's Claude API integrations. It enables the tracking of critical operational metrics including latency, token usage (input/output), estimated API costs, and error rates. Use it to set up structured logging and Prometheus metrics for robust monitoring and alerting, ensuring reliable and cost-effective AI application deployments.

A comprehensive checklist for deploying Claude API integrations to a production environment. It covers essential best practices for reliability, observability, and cost control, including authentication procedures, advanced error handling, rate limit management, health check implementation, and infrastructure deployment strategies (e.g., canary releases) to ensure a smooth and stable launch.

This module provides structured implementations of critical reliability patterns (Circuit Breaker, Graceful Degradation, Idempotency) for integrating with the Claude API. It helps developers build resilient, production-grade applications that can handle transient failures, rate limits, and service outages without failing completely, significantly enhancing system stability and user experience.

This resource provides production-ready architectural patterns for integrating Anthropic's Claude models using TypeScript and Python. It covers robust client wrappers, managing multi-turn conversations, enforcing structured JSON output via prefilling, and implementing multi-tenant architectures. Essential for building reliable, enterprise-grade AI applications.

This workflow provides a complete, end-to-end solution for building and deploying robust web scrapers using Crawlee on the Apify platform. It guides users through every stage: defining input schemas, setting up the Actor with a sophisticated router pattern, extracting structured data (e.g., product details), saving results to datasets, and managing the full lifecycle from local testing to cloud deployment via apify-cli.