Skill UI

Automates converting recordings into structured Markdown documentation, adding metadata, diarization, summaries, and captions via Whisper/Faster-Whisper for meetings, interviews, lectures, and content analysis.

Automates converting recordings into structured Markdown documentation, adding metadata, diarization, summaries, and captions via Whisper/Faster-Whisper for meetings, interviews, lectures, and content analysis.

A comprehensive workflow for production-grade, pre-recorded Speech-to-Text (STT) transcription using the Deepgram API. This skill supports transcribing audio from URLs or local files, offering advanced features such as speaker diarization, automatic summaries, sentiment analysis, and topic detection. It also includes robust batch processing methods for handling large datasets.

Provides production-ready coding patterns for integrating Deepgram using TypeScript and Python. This guide covers essential best practices, including implementing singleton client management, performing text-to-speech synthesis with Aura, and setting up advanced audio intelligence pipelines (e.g., summarization, topic detection, sentiment analysis). Use this when establishing robust Deepgram integration standards, refactoring existing SDK usage, or migrating to newer SDK versions.

This skill provides capabilities for bidirectional audio processing using fal.ai models. It supports Text-to-Speech (TTS), converting written text into natural-sounding speech, and Speech-to-Text (STT), accurately transcribing spoken audio into readable text. Use this skill for applications requiring high-quality audio input analysis or synthetic voice generation, such as virtual assistants, content digitization, or multilingual communication tools.

A comprehensive workflow solution for Groq, enabling powerful multimodal tasks beyond simple chat completions. It supports ultra-fast audio transcription (Whisper), detailed image understanding using vision models, and professional text-to-speech generation. Use this for real-time content processing, multimedia analysis, and automated content creation.

A comprehensive guide and setup for building a fast, reliable local development workflow for language learning applications. It includes setting up mock tutor responses for offline testing, generating audio test fixtures using TTS, and implementing a debug mode for detailed pronunciation scoring analysis. Ideal for engineers setting up a new Speak-based integration or refining existing test pipelines.

A comprehensive toolset for digital forensics, designed to detect, extract, and analyze covert data hidden within images, audio, and video files. It utilizes advanced steganalysis techniques (LSB, DCT, frequency domain analysis) and specialized tools (StegDetect, zsteg, binwalk) to uncover hidden communications channels, making it crucial for investigations involving data exfiltration or insider threats.

This skill detects AI-generated deepfake audio used in voice phishing (vishing) attacks. It functions by extracting critical spectral features (including MFCCs, spectral centroid, and zero-crossing rate) and passing them through advanced machine learning models (like Random Forest and Gradient Boosting). The system supports batch forensic analysis, provides confidence scores, and generates detailed reports to verify the authenticity of recorded voice calls, making it essential for cybersecurity incident response and fraud investigation.

Utilize advanced media intelligence to analyze audio, image, video, and text for synthetic manipulation and AI-generated content. This skill detects deepfakes, traces the original AI synthesis source, applies and detects watermarks, verifies speaker identity, and provides comprehensive media provenance analysis, ensuring media authenticity claims are rigorously supported.

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.

This skill provides a comprehensive guide to optimizing costs when using AssemblyAI for speech-to-text transcription. It details model selection (Best vs. Nano), guides feature budgeting (e.g., Diarization, Sentiment Analysis), and includes code examples for cost estimation and usage tracking. Use this when managing billing, reducing operational expenses, or implementing budget alerts for high-volume audio processing.