Skill UI

A guide to production-ready patterns for dbt (data build tool). It covers essential practices for robust data pipeline development, including model organization (staging, intermediate, marts), implementing advanced testing strategies, ensuring data quality, and optimizing incremental processing for large datasets.

This skill guides the architectural definition of complex business domains using Domain-Driven Design (DDD). It helps in defining core subdomains, separating services by bounded contexts, and establishing a unified ubiquitous language. It is essential for decomposing monoliths, structuring microservices, and aligning team ownership with clear domain boundaries.

This skill guides the application of Domain-Driven Design (DDD) tactical patterns, including defining aggregates, modeling value objects, managing repositories, and utilizing domain events. It helps developers translate complex domain rules into clean, robust, and behavior-rich code structures, ensuring invariants are explicitly enforced.

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.

A comprehensive runbook for standardizing incident response to Deepgram production issues. It guides users through automated triage (checking status, API connectivity, and model performance), severity classification (SEV1-SEV4), and implementing mitigation strategies, including building fallback services and replaying failed requests to ensure business continuity during outages or degradation.

This skill provides a comprehensive and isolated setup for managing Deepgram API integrations across different deployment stages (Development, Staging, Production). It includes typed configuration management, a client factory pattern, and Docker Compose profiles, ensuring that each environment uses specific credentials, models, and feature settings (like diarization or PII redaction) without conflict. Ideal for robust, multi-stage application deployment.

This guide provides advanced techniques to optimize Deepgram API performance. Learn how to preprocess audio using ffmpeg (resampling, noise reduction), select optimal transcription models (speed vs. accuracy), and implement efficient streaming or parallel batch processing to achieve high throughput and low latency.

A comprehensive guide for developers planning Deepgram SDK version upgrades (v3/v4 to v5) and model migrations (Nova-2 to Nova-3). This resource details breaking changes, provides side-by-side API comparisons, and includes automated validation scripts to ensure smooth integration of new features and improved speech recognition capabilities.

Deploying Machine Learning Models automates serving trained models to production by analyzing requirements, generating API and infrastructure code, and orchestrating deployment workflows with validation, error handling, and monitoring baked in.

Orchestrates PyTorch-native TorchTitan 4D parallelism (FSDP2, TP, PP, CP) for large-scale LLM pretraining, scaling from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing to boot Llama 3.1, DeepSeek V3, or custom models.

This skill provides deep expertise in building robust Django web applications and powerful RESTful APIs using Django REST Framework (DRF). It covers designing optimized Django models with proper indexes, implementing serializers and viewsets, optimizing ORM queries (select_related/prefetch_related), and setting up secure authentication (JWT). Ideal for full-stack backend development tasks requiring production-grade reliability.

Provides Django testing strategies combining pytest-django, factory_boy, DRF helpers, mocking, and coverage so teams can adopt TDD workflows for models, views, serializers, and APIs.