Skill UI

A comprehensive guide detailing the breaking changes and step-by-step process for migrating applications from Algolia Search SDK v4 to v5. This resource covers updates to method signatures, import styles, and client usage patterns, ensuring a smooth upgrade process for developers.

A comprehensive guide to implementing advanced user behavior tracking and real-time data synchronization with Algolia. This guide demonstrates how to use the Insights API to track clicks, conversions, and views, and how to build event-driven pipelines using database change listeners (e.g., Prisma middleware) to keep your Algolia index perfectly synced with your core application data. Essential for deep search analytics and relevance tuning.

This skill provides advanced sentiment analysis tailored for financial news and market texts. It supports two modes: high-speed, localized analysis using FinBERT, and advanced, reasoning-based analysis via LLM. Users can determine the sentiment polarity (positive, negative, or neutral), score, and specific reason for financial content, making it ideal for market intelligence and data enrichment.

This specialized skill provides a robust framework for tracking and updating financial investment signals. It systematically assesses the impact of new market information—such as breaking news or price movements—on existing investment theses. The system determines if a signal is Strengthened, Weakened, Falsified, or Unchanged, providing a crucial layer of continuous signal validation for sophisticated financial decision-making.

This integration automates the critical process of converting design assets from Figma into production-ready code using Anima. It establishes a full CI/CD pipeline via GitHub Actions, enabling scheduled or manual component generation. Use this when automating design system handoffs, maintaining code synchronization with design changes, or implementing robust, reliable development workflows.

A suite of utilities designed to significantly boost Anima code generation performance. It implements file-based caching with Time-To-Live (TTL) management, enables incremental updates by tracking only changed Figma components, and optimizes output size by cleaning up comments and excess whitespace. Ideal for optimizing large batch generations or speeding up production build pipelines.

A comprehensive checklist for validating the production readiness of design-to-code pipelines, specifically for Anima's service converting Figma designs into React, Vue, and HTML. Use this guide when deploying automated code generation services, setting up CI/CD for design handoffs, or validating output quality to ensure component integrity and prevent production failures.

A comprehensive reference guide for diagnosing and fixing common Anthropic Claude API errors, categorized by HTTP status codes (400, 429, 500, etc.). Includes specific error JSON patterns, root causes (e.g., role alternation, rate limits), suggested fixes, and code examples in Bash and Python for effective troubleshooting.

This tool collects comprehensive diagnostic evidence for troubleshooting Anthropic Claude API issues. It gathers critical data such as request IDs, rate limit headers, SDK versions, and connection details. It is essential for generating accurate support tickets or diagnosing persistent API failures.

A comprehensive runbook for diagnosing and resolving outages, degradation, and rate-limiting issues when using the Claude API. It provides structured triage steps, decision trees, and mitigation strategies for P1 through P4 severity incidents, ensuring minimal downtime and stable API interaction.

A comprehensive guide and set of scripts for performing load testing and capacity planning for Anthropic's Claude API. This resource helps developers benchmark performance, anticipate traffic spikes, manage rate limits (RPM/ITPM/OTPM), and implement robust horizontal scaling strategies for mission-critical AI services using Python.

Provides validated architectural patterns for integrating Claude API into production systems. Learn to implement synchronous API gateways, scalable asynchronous queue-based processing, and multi-model routing strategies. This guide helps developers design robust, high-volume, and fault-tolerant AI applications.