Skill UI

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.

A comprehensive guide to using AssemblyAI for rich audio processing. This guide covers basic speech-to-text transcription from URLs or local files, along with advanced AI features. Capabilities include speaker diarization, sentiment analysis, key phrase extraction, entity detection, and powerful LLM-powered analysis (LeMUR) for summarization and Q&A. Ideal for integrating deep, multi-faceted audio insights into applications.

This guide provides advanced techniques to optimize AssemblyAI's transcription performance for large-scale applications. Learn how to maximize throughput and minimize latency by implementing caching strategies (Redis/LRU), utilizing webhooks instead of polling, selecting the optimal speech model based on needs, and parallelizing batch processing workloads.

A comprehensive workflow suite for professional audio manipulation using ElevenLabs API. This module supports multiple advanced AI features, including Speech-to-Speech voice cloning, generating realistic sound effects from text prompts, isolating and removing background noise, and accurate Speech-to-Text transcription with diarization. Ideal for content creators, podcast producers, and audio engineers.

This guide provides a robust implementation for handling asynchronous webhooks from ElevenLabs. It covers the critical steps of receiving POST notifications (such as transcription completion, post-call audio, and failure events), performing secure HMAC-SHA256 signature verification, and routing the event data to specific processing handlers. Ideal for integrating ElevenLabs into conversational AI pipelines, ensuring data integrity and reliable background processing.

This skill provides comprehensive diagnosis and optimization for agent skill libraries. By analyzing historical session transcripts from platforms like Claude Code and Codex, alongside static skill file checks, it scores each skill across 8 dimensions. It identifies issues such as low trigger rates, poor post-invocation user reaction, and incomplete workflow execution, generating a prioritized report (P0/P1/P2) to significantly improve the agent's overall performance and reliability.

Inspect and audit GEPA genomes via the @metaharness/darwin/gepa library. Load, validate, render system prompts, and analyze failure modes in run transcripts. Ideal for adopting evolved policies, CI gates, and debugging evolution runs.

A comprehensive guide for authors to determine the optimal conference venue for their speech-related research. It utilizes a 'modality test' to assess whether a contribution relies on audio signal characteristics (acoustics, prosody, etc.) or merely text transcriptions. The guide helps authors choose between major venues like INTERSPEECH, ACL, ICASSP, and specialized workshops, ensuring the claim is reviewed by relevant experts.

Compiles documents, notes, web pages, transcripts, research materials, or code repositories into an evidence-grounded, visualization-ready knowledge graph of typed entity nodes with statement-level provenance and typed, directional relationship edges.

DeepSpot-M is a multimodal foundation model that generates virtual spatial transcriptomics from H&E histology images. It maps 224x224 tiles to spatial gene expression using deep learning, supporting over 19k protein-coding genes. Ideal for research in pathology and cancer genomics, requiring gated model access and Python environment.

This skill helps identify issues in Superpowers sessions by analyzing transcripts and evidence. It assists in troubleshooting problems like repeated work, ignored plans, or unexpected costs. The process involves locating session files, triaging dimensions, generating detailed reports, and optionally creating GitHub issues or export bundles for maintainers. All findings must cite specific paths and lines.