Skill UI

This workflow establishes a comprehensive local environment for developing and testing clinical AI integrations. It simulates an Electronic Health Record (EHR) using a local HAPI FHIR server and utilizes synthetic patient data. Developers can iterate on complex encounter workflows and API connections safely, without ever using real Protected Health Information (PHI).

Provides structured methods and code examples to optimize Abridge's clinical AI integration for high-volume, enterprise deployments. Focuses on reducing critical latencies across the entire workflow, including audio streaming throughput, note generation pipelines, and secure FHIR push operations, ensuring scalability for large health systems.

This module provides robust webhook handling for Abridge, managing critical clinical documentation lifecycle events. It ingests events such as note completion, encounter status changes, signing, and quality alerts. By implementing signature verification and idempotency checks, this system ensures reliable, secure integration of clinical data into external platforms like EHRs.

Implement robust data handling practices when integrating with Adobe APIs. This comprehensive guide covers critical compliance areas, including Personally Identifiable Information (PII) redaction from extracted PDFs, enforcing Firefly content policies, secure credential redaction in logs, and managing data subject access/deletion requests for GDPR/CCPA compliance using the Adobe Privacy Service API. Essential for building privacy-compliant enterprise applications.

A deep-dive guide for re-architecting complex enterprise systems within the Adobe ecosystem. This skill covers three major migration patterns: consolidating multiple legacy Adobe APIs into the unified Firefly Services SDK, implementing 'strangler-fig' patterns to replace competitor APIs (e.g., PDFTron, Cloudinary) with native Adobe services, and migrating authentication methods (JWT to OAuth). It provides practical code examples and architectural advice for full platform replatforming.

This guide details how to implement comprehensive observability for various Adobe API integrations (e.g., Firefly, PDF Services). It covers the four pillars of observability: structured metrics using Prometheus, distributed tracing via OpenTelemetry, structured JSON logging, and defining critical alert rules. This ensures deep visibility into API performance, latency, and error rates for robust production monitoring.

This integration facilitates the secure deployment of Alchemy-powered decentralized applications (dApps) across major cloud platforms like Vercel and Google Cloud Run. It is designed to ensure that sensitive Alchemy API keys are treated as server-side secrets, preventing client-side exposure. Use this when setting up backend services, RPC proxies, or deploying production-grade Web3 applications requiring secure access to blockchain data.

A comprehensive checklist and script to validate the production readiness of decentralized applications (dApps) powered by Alchemy. It covers critical security best practices, API infrastructure validation, smart contract deployment checks, and performance optimization, ensuring a stable and secure mainnet launch.

This guide provides a comprehensive workflow for deploying Algolia-powered search applications to various cloud platforms, including Vercel, Fly.io, and Google Cloud Run. It covers best practices for API key separation (Admin vs. Search-Only), setting up environment variables, and implementing both backend API routes and advanced frontend components using React InstantSearch or vanilla JavaScript.

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 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.

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.