Skill UI

This skill provides a comprehensive guide to optimizing Algolia search performance. It covers best practices for reducing record size, selecting optimal searchable attributes, tuning faceting, implementing client-side caching, and configuring replica strategies. Use this guide to minimize search latency and drastically improve the end-user search experience.

A comprehensive checklist guide for deploying Algolia search services to a production environment. It covers critical steps including index configuration (attributes, ranking), securing API keys (admin vs. search-only), setting up replicas for multiple sort orders, implementing monitoring, and defining graceful degradation/fallback mechanisms. Ideal for go-live and major deployments.

This comprehensive guide provides a production-ready reference architecture for implementing scalable search using Algolia. It details best practices for index design (including multi-index strategies), establishing robust data pipelines from source databases, structuring the service layer (search, indexing, settings), and integrating the frontend for optimal performance.

This guide provides production-ready, high-quality patterns for integrating with Algolia Search v5. It covers critical architectural aspects such as implementing singleton client management, performing strongly typed searches, robust error handling (including specific API error codes), efficient batch indexing, and handling multi-tenant environments. Use these patterns to refactor existing code and establish reliable coding standards.

This workflow automates the design handoff process by creating a continuous pipeline from Figma designs to functional React code. It scans Figma components, generates code batches (supporting TypeScript, Tailwind CSS, etc.), and includes change detection to automatically update the codebase when the design source changes. Ideal for building complex design systems and implementing CI/CD design-to-code workflows.

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 outlines critical security best practices for design-to-code pipelines using Anima and Figma. It covers securing API credentials by restricting token scopes (e.g., read-only access for Figma), enforcing server-side execution for SDKs, and implementing robust secret management using cloud services like GCP Secret Manager. This minimizes the risk of credential leakage in CI/CD environments.

A comprehensive skill providing patterns for implementing data privacy, PII redaction, and robust audit logging when interacting with large language models like Claude. Use this when your application handles sensitive user data, requiring compliance with regulations such as GDPR or CCPA, ensuring all API calls are secure and auditable.

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.

A comprehensive guide for configuring and managing organization-level access control within the Anthropic ecosystem. It details Workspaces setup (Production, Staging, Development), defines roles (Owner, Admin, Developer), and provides best practices for API key management. Includes practical Python code examples for implementing application-level Role-Based Access Control (RBAC) to restrict model access and usage limits.

This comprehensive skill provides multi-layered guardrails for integrating with LLMs like Claude. It enforces security and compliance by implementing input validation (PII detection, length checks), system prompt restriction (scope control, safety rules), output filtering (preventing sensitive data leakage), cost governance, and model access policies. Ideal for building secure, robust, and governed AI applications.

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.