القائمة
جمع SkillsMP عدد ٨٨ من skills من databricks-solutions/vibe-coding-workshop-template. افتح أي skill لمراجعة مصدره وتفاصيله.
عرض ٤٠ من أصل ٨٨ skills مجمعة.
End-to-end orchestrator for designing complete Gold layer schemas with ERDs, YAML files, lineage tracking, and comprehensive business documentation. Guides users through dimensional modeling, ERD creation (master/domain/summary based on table count), YAML…
لغة النص الأصلي: الإنجليزية
Cross-validation of Gold layer design artifacts during the design phase. Use when validating that YAML schemas, ERDs, lineage CSVs, and PK/FK references are internally consistent before handing off to implementation. Catches design-time inconsistencies (e.g.,…
لغة النص الأصلي: الإنجليزية
Active, source-bounded alignment of a Gold dimensional design to Databricks Industry Vibe Data Models (and other canonical industry reference models — TM Forum SID, ARTS, ACORD, HL7, BIAN). Use when the customer operates in a recognizable vertical (retail,…
لغة النص الأصلي: الإنجليزية
Intelligent skill navigation system with tiered loading and orchestrator-first routing for context-efficient agent operation. Routes tasks to the correct domain skill based on keyword detection with orchestrator priority. Each skill uses progressive…
لغة النص الأصلي: الإنجليزية
Deploy a Databricks AppKit application to Databricks Apps. Covers config validation, build verification, deployment, UI verification, error diagnosis with fix loop, and workspace app limit handling. Use when asked to deploy an AppKit app, push to production,…
لغة النص الأصلي: الإنجليزية
Add plugins to an existing Databricks AppKit project. Covers Lakebase (PostgreSQL), Analytics (SQL queries + dashboards), Genie (natural language AI/BI), Files (UC Volumes), and Serving (Model Serving / Agent endpoints). Guides through plugin registration,…
لغة النص الأصلي: الإنجليزية
Enables agent self-reflection and learning from mistakes through systematic skill updates. Prioritizes updating existing skills over creating new ones - always searches existing skills first, creates new skills only when justified. Includes upstream source…
لغة النص الأصلي: الإنجليزية
Systematic skill freshness auditing with verification anchors, volatility classification, staleness detection, and upstream lineage tracking across every Agent Skill in the repository (data_product_accelerator/, genai-agents/, apps_lakebase/). Fetches…
لغة النص الأصلي: الإنجليزية
End-to-end Bronze layer creation for testing and demos. Creates table DDLs, generates fake data with Faker, copies from existing sources, and configures Asset Bundle jobs. Covers Unity Catalog compliance, Change Data Feed, automatic liquid clustering, and…
لغة النص الأصلي: الإنجليزية
Generate synthetic data with Faker for Bronze layer testing with configurable data corruption. Use when creating test data for data quality validation, testing DLT expectations, or simulating production-like datasets. Supports realistic data generation with…
لغة النص الأصلي: الإنجليزية
End-to-end autonomous deployment and operations skill. Deploys Databricks Asset Bundles, runs jobs and pipelines, polls for completion, diagnoses failures, applies fixes, redeploys, and verifies — all without human intervention. Also serves as SDK/CLI/REST…
لغة النص الأصلي: الإنجليزية
Patterns for sharing code between Databricks notebooks using pure Python files and standard imports. Covers Asset Bundle path setup (rsplit canonical pattern), notebook-to-module conversion, import patterns vs %run magic commands, job submission context vs…
لغة النص الأصلي: الإنجليزية
Provides standard TBLPROPERTIES and metadata patterns for Unity Catalog Delta tables across Bronze, Silver, and Gold medallion layers. Ensures governance compliance, performance optimization, and proper metadata tagging for all table creation operations.…
لغة النص الأصلي: الإنجليزية
Enforces enterprise naming conventions (snake_case, table prefixes, approved abbreviations), dual-purpose COMMENT formats for tables/columns/TVFs/metric views/dashboards/Genie Spaces, and config-aware tagging standards. Scans context/ for customer tagging…
لغة النص الأصلي: الإنجليزية
Provides schema management patterns for Databricks Asset Bundles with Unity Catalog. Enables programmatic schema creation and configuration for medallion architecture layers. Covers CREATE SCHEMA IF NOT EXISTS patterns, DLT pipeline schema configuration,…
لغة النص الأصلي: الإنجليزية
Unity Catalog Primary Key and Foreign Key constraint patterns for proper relational modeling in Databricks. Use when implementing star schema dimensional models with PK/FK relationships in Gold layer tables. Covers surrogate keys as PRIMARY KEYS (not business…
لغة النص الأصلي: الإنجليزية
End-to-end orchestrator for implementing Gold layer tables, merge scripts, FK constraints, and Asset Bundle jobs from YAML schema definitions. Guides users through Silver contract validation, YAML-driven table creation, Silver-to-Gold MERGE operations (SCD…
لغة النص الأصلي: الإنجليزية
Patterns for creating Gold layer tables dynamically from YAML schema definitions at runtime. Use when managing 10+ Gold layer tables across multiple domains, when schema evolves frequently, or when you want to avoid embedded SQL DDL strings in Python.…
لغة النص الأصلي: الإنجليزية
MLflow and ML Model patterns for Databricks including experiment creation, model training, batch inference, and Unity Catalog integration. Use when implementing ML pipelines, training models with Feature Store, or deploying batch inference jobs. Includes 19…
لغة النص الأصلي: الإنجليزية
Comprehensive guide for Databricks Lakehouse Monitoring (Data Profiling) with quick-start workflow (2 hours), fill-in-the-blank requirements template, concrete fact/dimension monitor examples, and complete deployment patterns. Uses the new Data Quality API…
لغة النص الأصلي: الإنجليزية
Production-grade patterns for Databricks AI/BI (Lakeview) dashboards. Prevents visualization errors, deployment failures, and maintenance issues through widget-query alignment, number formatting, parameter configuration, monitoring table patterns, chart scale…
لغة النص الأصلي: الإنجليزية
End-to-end orchestrator for building the Databricks semantic layer including Metric Views, Table-Valued Functions (TVFs), and Genie Spaces. Guides users through metric view creation, TVF development, Genie Space setup, and API-driven deployment. Orchestrates…
لغة النص الأصلي: الإنجليزية
Standard patterns for creating Databricks Metric Views with semantic metadata for Genie and AI/BI. Use when creating metric views, troubleshooting metric view creation errors, validating schema references before deployment, implementing joins (including…
لغة النص الأصلي: الإنجليزية
End-to-end guide for planning, creating, deploying, and validating Table-Valued Functions (TVFs) in Databricks optimized for Genie Space natural language queries. Use when creating TVFs for Genie Spaces, planning TVF requirements from business questions,…
لغة النص الأصلي: الإنجليزية
Patterns for setting up Databricks Genie Spaces with comprehensive agent instructions, data assets, SQL expressions, and benchmark questions. Use when creating Genie Spaces, configuring agent behavior, selecting data assets, defining SQL expressions…
لغة النص الأصلي: الإنجليزية
Comprehensive patterns for Databricks Genie Space Export/Import API - JSON schema, serialization format, and programmatic deployment. Use when programmatically creating, exporting, or importing Genie Spaces via REST API, troubleshooting API deployment errors,…
لغة النص الأصلي: الإنجليزية
End-to-end orchestrator for creating Silver layer pipelines using Spark Declarative Pipelines (SDP, formerly DLT) with Delta table-based data quality rules, quarantine patterns, and monitoring views. Orchestrates mandatory dependencies on common skills…
لغة النص الأصلي: الإنجليزية
Spark Declarative Pipeline (SDP, formerly DLT) expectations patterns for data quality with Unity Catalog Delta table storage. Use when implementing Silver layer SDP/DLT pipelines, creating portable data quality rules, or needing runtime-updateable…
لغة النص الأصلي: الإنجليزية
Use when creating, syncing, or managing a Databricks Agent Bricks Knowledge Assistant (KA) as a document Q&A source for an agent. Covers source mode selection (UC Files vs Vector Search index), Python SDK lifecycle (create/update/sync/delete), REST API…
لغة النص الأصلي: الإنجليزية
Use when you need to version, manage, or load prompts for any GenAI agent on Databricks. Covers how to register prompts in Unity Catalog, set up aliases for safe deployments, link prompts to traces, and load them in production apps — even if you just want…
لغة النص الأصلي: الإنجليزية
Use when you need to create, manage, or load evaluation datasets for testing agent quality. Covers the MLflow GenAI data format, persisting benchmarks in Unity Catalog, merging records without duplicates, and validating data before evaluation — even if you…
لغة النص الأصلي: الإنجليزية
Use when running mlflow.genai.evaluate() to test agent quality before deployment. Covers the predict_fn contract, answer-sheet mode for re-scoring existing outputs, threshold gates, retry wrappers, human feedback sessions, and conversation evaluation — even…
لغة النص الأصلي: الإنجليزية
Use when wiring **end-user feedback** (thumbs up/down, ratings, free-form comments) from a deployed app back into MLflow as Assessments on the originating trace. Covers the canonical `mlflow.log_feedback(...)` pattern, trace-id vs `client_request_id`…
لغة النص الأصلي: الإنجليزية
Use when you need to log an agent to MLflow, register it in Unity Catalog, or promote a new version as champion. Covers the full model lifecycle — even if you just want "log my agent and make it the production version." Also use when setting up…
لغة النص الأصلي: الإنجليزية
Use when deploying an agent to production or setting up CI/CD automation. Covers Databricks Apps deployment, Asset Bundles, service principal permissions, and evaluate-then-promote pipelines — even if you just want "deploy my agent and set up a release gate."…
لغة النص الأصلي: الإنجليزية
Use when prompt quality has plateaued on your evaluation dataset and you need to programmatically improve one or more registered prompts. Covers `mlflow.genai.optimize_prompts()` and `GepaPromptOptimizer`, `predict_fn` contracts, single and multi-prompt…
لغة النص الأصلي: الإنجليزية
Default reusable MLflow workshop path for instruction-shaped prompt iteration. Hand-authored prompt revisions guided by failing-scorer rationales from the first scored eval, with full-dataset re-eval and alias-gated promotion. Use when Step 4 evaluation…
لغة النص الأصلي: الإنجليزية
Standard patterns for Databricks Asset Bundles configuration files for serverless jobs, DLT pipelines, dashboards, alerts, apps, and workflows. Use when creating, configuring, or deploying DABs for infrastructure-as-code deployments. Covers mandatory…
لغة النص الأصلي: الإنجليزية
Use when starting any new GenAI agent project on Databricks. Covers MLflow 3.x GenAI setup: install dependencies, enable autolog, configure connection pooling, understand model signatures, detect runtime environment. Foundation Step 1. Produces the configured…
لغة النص الأصلي: الإنجليزية
Use when setting up MLflow experiments, tracing, or UC OTEL trace storage for a GenAI agent. Covers structured experiment paths, tracing decorators, manual spans, tags, connection pooling, and Unity Catalog OTEL storage for SQL-queryable trace retention.…
لغة النص الأصلي: الإنجليزية