Skill UI

This workflow connects to the Fireflies.ai API via GraphQL to fetch and process detailed meeting transcripts. It handles speaker-diarized data, extracting structured insights such as action items, key topics, sentiment analysis, and speaker breakdowns. It enables the creation of comprehensive meeting intelligence reports and robust downstream pipelines. Use this when you need to automate the retrieval, summarization, or deep analysis of recorded meeting content.

Automate Gong conversation intelligence by using Composio MCP natural-language commands to fetch call recordings, transcripts, analytics, speaker stats, and workspace metadata across date ranges.

Use Azure OpenAI Realtime Mini to turn text prompts into streamed PCM audio, convert it to WAV, and deliver base64 payloads for frontend playback, handling transcripts and voice tuning for podcast-style narration.

A comprehensive guide for setting up and authenticating access to the Fathom AI API. This skill walks users through generating API keys, configuring environment variables, and testing connectivity to retrieve meeting transcripts, summaries, and action items. Essential for integrating Fathom's meeting intelligence features into custom applications.

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 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 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.

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.

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.

Transcribe audio/video files into timestamped Markdown transcripts and optional LLM-generated executive summaries using local Whisper inference.