Skill UI

This guide details how to implement robust content safety guardrails for applications powered by Claude. It covers essential techniques such as system prompt hardening, input validation to detect injection patterns and length limits, and output validation to prevent system prompt leakage. It ensures compliance with Anthropic's policies, making the AI integration secure and reliable.

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 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.

This workflow provides comprehensive audio processing capabilities, covering both real-time streaming transcription (for live captions and voice agents) and advanced post-processing using LeMUR. Use it for summarizing long meetings, answering questions based on transcripts, extracting action items, and running custom LLM analyses on recorded audio.

Implement robust handling for AssemblyAI's asynchronous transcription completion events via webhooks. This guides users on how to set up secure endpoints that receive POST requests upon job completion or failure. It covers submitting transcripts with webhook URLs, verifying authentication headers, and processing the final transcript data, including running subsequent analyses like summarization.

This workflow implements Abridge's end-to-end clinical documentation pipeline. It captures ambient audio from patient encounters, performs real-time transcription, utilizes generative AI to structure the medical notes (e.g., SOAP, H&P), generates required medical codes (ICD-10, CPT), and finally inserts the structured data directly into the EHR system. Designed for primary clinical encounters, minimizing manual charting time.

This minimal working example demonstrates Abridge's ambient AI clinical documentation capabilities. It simulates a complete patient encounter session, allowing users to submit transcripts and receive a highly structured clinical note. Ideal for testing EHR connectivity, verifying AI workflow, or learning how structured medical data (SOAP notes, ICD/CPT codes) is automatically generated from free-text conversation.

This guide details the process of setting up authentication and integration credentials for the Abridge clinical AI platform. It covers prerequisites for connecting to major EHR systems (Epic, Athena) using FHIR R4 APIs and implements OAuth 2.0 (SMART on FHIR) flows, ensuring secure, HIPAA-compliant data handling for ambient documentation.

This pattern library provides production-ready, structured patterns for integrating with the Abridge clinical AI API. It establishes type-safe client wrappers, implements mandatory HIPAA-compliant audit logging (ensuring no Protected Health Information, or PHI, is logged), and incorporates robust error handling with exponential backoff retries. Use this when building secure, scalable, and standards-compliant healthcare AI applications.

This skill provides comprehensive guidance on implementing HIPAA-compliant security measures for Abridge clinical AI integrations. It covers critical areas such as enforcing TLS 1.3 encryption in transit, AES-256 encryption at rest, implementing role-based access controls (RBAC), detailed PHI-safe audit logging, and best practices for secrets management, ensuring full compliance with HIPAA regulations.

This guide provides a comprehensive framework for debugging and analyzing AgentCore runtime sessions using CloudWatch Logs Insights. It details advanced techniques like OpenTelemetry (OTEL) span correlation, resolving session IDs to trace IDs, and structuring complex query queries. Use this when investigating agent execution flows, diagnosing tool call failures, analyzing latency, or building detailed behavioral timelines.

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.