Skill UI

A comprehensive, expert-level skill for designing, analyzing, and optimizing robust database schemas. It covers normalization, index tuning, choosing between SQL/NoSQL, and implementing zero-downtime data migrations. Provides systematic workflows for schema analysis, performance modeling, and advanced SQL pattern generation.

Offers automated guidance for database query profiler tasks in performance testing, including best practices, code/config generation, and validation when working with load/stress benchmarking.

Automates guidance for date range analytics, covering SQL, visualization, statistics, and BI, with code generation and validation to support data analytics workflows.

Guides you through generating dbt models inside data pipelines, covering setup, code generation, configuration, and validation while following best practices for ETL, orchestration, and streaming workloads.

Automates dbt test creation within data pipelines, offering guided best practices, code generation, and validation to keep ETL, transformation, and streaming workflows reliable.

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.

Automates Django view generation by guiding through best practices, producing production-ready code, and validating backend-focused outputs whenever users mention related keywords or request assistance.

This expert skill specializes in creating comprehensive, maintainable technical documentation directly from existing codebase, configurations, and comments. It can generate various artifacts, including API specifications, system architecture diagrams, detailed user guides, and technical references, ensuring consistency and accuracy using AI-powered analysis and industry best practices. Ideal for building robust documentation pipelines and standardizing documentation across large repositories.

Provides a structured approach for creating, editing, and analyzing .docx files with tooling and best practices for tracked changes, comments, formatting, and text extraction, covering JavaScript document generation and Python OOXML editing plus markdown-based redlining.

Automated guidance for configuring and applying early stopping callbacks across ML training workflows, covering best practices, code generation, and validation for PyTorch, TensorFlow, and scikit-learn experiments.

Automates elasticsearch index manager activities inside advanced DevOps workflows, offering best-practice guidance, code/configuration generation, and validation for kubernetes, terraform, ci/cd, monitoring, and IaC needs.

A comprehensive guide for selecting and optimizing embedding models for advanced vector search and retrieval-augmented generation (RAG) applications. This guide covers model comparison, chunking strategies, dimension reduction techniques, and provides runnable Python templates for both OpenAI and local Sentence Transformer implementations.