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Found 386 Skills
Apache Iceberg tables on Databricks — Managed Iceberg tables, External Iceberg Reads (fka Uniform), Compatibility Mode, Iceberg REST Catalog (IRC), Iceberg v3, Snowflake interop, PyIceberg, OSS Spark, external engine access and credential vending. Use when creating Iceberg tables, enabling External Iceberg Reads (uniform) on Delta tables (including Streaming Tables and Materialized Views via compatibility mode), configuring external engines to read Databricks tables via Unity Catalog IRC, integrating with Snowflake catalog to read Foreign Iceberg tables
Train ML models on Databricks. Use for: classification/regression/deep-learning (XGBoost, scikit-learn, LightGBM, PyTorch) with Optuna, @prod/@challenger aliases, batch scoring (spark_udf for plain models, fe.score_batch for feature-store-backed), custom PyFunc, custom ResponsesAgent (LangGraph + UC Function/Vector Search); UC feature tables + FeatureLookup + point-in-time joins + Lakebase online store; declarative Feature Views (create_feature, DeltaTableSource, RollingWindow/SlidingWindow/TumblingWindow, materialize_features, streaming Kafka features). NOT for: endpoint ops (databricks-model-serving), MLflow evaluation (databricks-mlflow-evaluation).
Refactor Next.js code to improve maintainability, readability, and adherence to App Router best practices. Identifies and fixes God Components, prop drilling, inappropriate 'use client' usage, outdated Pages Router patterns, missing Suspense boundaries, incorrect caching strategies, and useEffect data fetching anti-patterns. Applies modern Next.js 15 patterns including Server Components, Client Components, Server Actions, streaming with Suspense, proper caching strategies, Container-Presentational pattern, layout composition, parallel routes, and intercepting routes.
Write, review, or integrate Apple's on-device FoundationModels framework (iOS 26.0+, macOS 26.0+). Use when building generative AI features, structured data extraction, tool calling, or streaming text generation natively on Apple Silicon devices.
Call Exa Search directly with cURL or raw HTTP. Use when an agent needs Exa semantic web retrieval from POST /search without an SDK, including ranked results, domain or category filters, freshness-aware result content, highlights or text extraction, structured output, or streaming search responses.
Collect live streams from YouTube — channel, category, viewers, title. Use when the user wants to track live activity or research streaming trends.
Integrates the SAP Cloud SDK for AI for Python (sap-ai-sdk-gen, formerly generative-ai-hub-sdk) into Python applications. Use when building Python apps with SAP AI Core, Generative AI Hub, or the Orchestration Service: chat completion, embeddings, streaming, LangChain integration, templating, content filtering, data masking, and document grounding. Supports OpenAI GPT models, Llama, Gemini, Amazon Nova, and other foundation models via SAP BTP.
Design patterns for building AI-powered interfaces like chatbots and intelligent assistants in React.
Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library
Integrate PICA into an application using the OpenAI Agents SDK. Use when adding PICA tools to an OpenAI agent via @openai/agents, setting up PICA MCP with the OpenAI Agents SDK, or when the user mentions PICA with OpenAI Agents.
This skill should be used when the user asks to "create an API route", "add an endpoint", "build a REST API", "handle POST requests", "create route handlers", "stream responses", or needs guidance on Next.js API development in the App Router.
Pyspark Transformer - Auto-activating skill for Data Pipelines. Triggers on: pyspark transformer, pyspark transformer Part of the Data Pipelines skill category.