Skill UI

This specialized skill acts as an expert debugger, designed for deep root cause analysis of software errors, test failures, and unexpected behavior. It guides the user through systematic debugging processes, including capturing stack traces, isolating failure points, implementing minimal fixes, and providing comprehensive reports detailing the root cause, evidence, specific code patches, and prevention recommendations. Ideal for complex bug resolution and quality assurance.

This skill provides a structured methodology for tackling complex bugs, performance bottlenecks, and production incidents. It guides users through reproducing issues, forming hypotheses, performing controlled experiments, and narrowing down the scope using proven strategies and tools. Ideal for deep dive troubleshooting and root cause analysis in complex software systems.

A comprehensive, expert-level workflow for debugging complex software issues in production environments. This skill guides users through initial triage, advanced observability data collection (APM, distributed traces, logs), intelligent hypothesis generation, and full root cause analysis. Use it when dealing with intermittent failures, performance bottlenecks, or needing a complete fix and prevention plan.

This expert guide provides a systematic, structured methodology for debugging complex software issues. It teaches users how to analyze stack traces, correlate logs, establish consistent reproduction steps, and apply hypothesis-driven testing to isolate and resolve bugs, crashes, or unexpected behavior in any codebase. Essential for professional troubleshooting and quality assurance.

Creates step-by-step decomposition plans and migration roadmaps to guide the transition from monolithic applications to distributed architectures, prioritizing risk, value, and dependencies while tracking progress through pattern-based phases.

This comprehensive guide sets up a robust CI/CD pipeline for Deepgram integrations using GitHub Actions. It covers unit tests (mocked SDK), full integration tests against the live Deepgram API (including transcription, diarization, and TTS), and smoke tests. Use this when establishing automated testing, ensuring deployment stability, or implementing continuous delivery workflows for Deepgram-powered applications.

A comprehensive guide for diagnosing and fixing common Deepgram API errors. Covers HTTP error codes, WebSocket issues, optimal audio preparation using ffmpeg, and implementing robust retry patterns for reliable Speech-to-Text integration.

Implements best practices for secure audio transcription using Deepgram. This guide covers PII redaction (built-in and custom patterns), secure audio upload to S3 with encryption, managing data retention policies, and ensuring compliance with major regulations like GDPR and HIPAA. It provides a comprehensive pipeline for handling sensitive audio data in enterprise applications.

Provides comprehensive instructions for deploying Deepgram transcription services into various production environments. This guide covers building production-ready Docker images, configuring Docker Compose, setting up robust Kubernetes manifests (including HPA and secrets), and implementing serverless handlers for platforms like AWS Lambda. Essential for scaling Deepgram services reliably and securely in the cloud.

This guide provides minimal working examples for Deepgram speech-to-text transcription. It demonstrates how to transcribe audio from a URL or a local file using both TypeScript and Python. Users can learn basic API patterns, implement advanced features like diarization (speaker identification), and compare various model options (e.g., nova-3, nova-2). Ideal for initial integration and testing.

This guide establishes a robust local development environment for Deepgram integrations. It covers setting up test fixtures using sample audio, mocking API responses for isolated unit testing, configuring watch-mode development servers, and running comprehensive unit and integration tests using Vitest. Use this setup when initiating a Deepgram feature or requiring a reliable, fast development cycle.

This guide offers a comprehensive deep dive into migrating existing voice transcription workflows from multiple major providers, including AWS Transcribe, Google Cloud STT, OpenAI Whisper, and others, to Deepgram. It details feature mapping, best practices, and implements an adapter pattern, enabling seamless, reliable, and validated service switching.