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SkillsMP は databrickslabs/coding-agents-databricks-apps から 33 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
収集済み skill 33 件中 33 件を表示しています。
Use after merging CoDA PRs, before a release, or when asked whether coda-main still works. Drives coda-main through an authenticated Chrome session and CoDA's JSON terminal API, runs the structured scripts/verify_coda_live.py smoke test, and returns evidence…
原文の言語: 英語
Create and manage Databricks Agent Bricks: Knowledge Assistants (KA) for document Q&A, Genie Spaces for SQL exploration, and Supervisor Agents (MAS) for multi-agent orchestration. Use when building conversational AI applications on Databricks.
原文の言語: 英語
Use Databricks built-in AI Functions (ai_classify, ai_extract, ai_summarize, ai_mask, ai_translate, ai_fix_grammar, ai_gen, ai_analyze_sentiment, ai_similarity, ai_parse_document, ai_prep_search, ai_query, ai_forecast) to add AI capabilities directly to SQL…
原文の言語: 英語
Create Databricks AI/BI dashboards. Use when creating, updating, or deploying Lakeview dashboards. CRITICAL: You MUST test ALL SQL queries via execute_sql BEFORE deploying. Follow guidelines strictly.
原文の言語: 英語
Builds Databricks applications. Prefers AppKit (TypeScript + React SDK) for new apps; falls back to Python frameworks (Dash, Streamlit, Gradio, Flask, FastAPI, Reflex) when Python is required. Handles OAuth authorization, app resources, SQL warehouse and…
原文の言語: 英語
Create and configure Declarative Automation Bundles (formerly Asset Bundles) with best practices for multi-environment deployments (CICD). Use when working with: (1) Creating new DAB projects, (2) Adding resources (dashboards, pipelines, jobs, alerts), (3)…
原文の言語: 英語
Manage Databricks workspace connections: check current workspace, switch profiles, list available workspaces, or authenticate to a new workspace. Use when the user mentions "switch workspace", "which workspace", "current profile", "databrickscfg", "connect to…
原文の言語: 英語
Databricks documentation reference via llms.txt index. Use when other skills do not cover a topic, looking up unfamiliar Databricks features, or needing authoritative docs on APIs, configurations, or platform capabilities.
原文の言語: 英語
Execute code and manage compute on Databricks. Use this skill when the user mentions: "run code", "execute", "run on databricks", "serverless", "no cluster", "run python", "run scala", "run sql", "run R", "run file", "push and run", "notebook run", "batch…
原文の言語: 英語
Create and query Databricks Genie Spaces for natural language SQL exploration. Use when building Genie Spaces, exporting and importing Genie Spaces, migrating Genie Spaces between workspaces or environments, or asking questions via the Genie Conversation API.
原文の言語: 英語
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…
原文の言語: 英語
Use this skill proactively for ANY Databricks Jobs task - creating, listing, running, updating, or deleting jobs. Triggers include: (1) 'create a job' or 'new job', (2) 'list jobs' or 'show jobs', (3) 'run job' or'trigger job',(4) 'job status' or 'check job',…
原文の言語: 英語
Patterns and best practices for Lakebase Autoscaling (next-gen managed PostgreSQL). Use when creating or managing Lakebase Autoscaling projects, configuring autoscaling compute or scale-to-zero, working with database branching for dev/test workflows,…
原文の言語: 英語
Patterns and best practices for Lakebase Provisioned (Databricks managed PostgreSQL) for OLTP workloads. Use when creating Lakebase instances, connecting applications or Databricks Apps to PostgreSQL, implementing reverse ETL via synced tables, storing agent…
原文の言語: 英語
Unity Catalog metric views: define, create, query, and manage governed business metrics in YAML. Use when building standardized KPIs, revenue metrics, order analytics, or any reusable business metrics that need consistent definitions across teams and tools.
原文の言語: 英語
Deploy and query Databricks Model Serving endpoints. Use when (1) deploying MLflow models or AI agents to endpoints, (2) creating ChatAgent/ResponsesAgent agents, (3) integrating UC Functions or Vector Search tools, (4) querying deployed endpoints, (5)…
原文の言語: 英語
Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. Use when working with databricks-sdk, databricks-connect, or Databricks APIs.
原文の言語: 英語
Creates, configures, and updates Databricks Lakeflow Spark Declarative Pipelines (SDP/LDP) using serverless compute. Handles data ingestion with streaming tables, materialized views, CDC, SCD Type 2, and Auto Loader ingestion patterns. Use when building data…
原文の言語: 英語
Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, working with Kafka ingestion, implementing Real-Time Mode (RTM), configuring triggers (processingTime, availableNow), handling stateful…
原文の言語: 英語
Generate realistic synthetic data using Spark + Faker (strongly recommended). Supports serverless execution, multiple output formats (Parquet/JSON/CSV/Delta), and scales from thousands to millions of rows. For small datasets (<10K rows), can optionally…
原文の言語: 英語
Unity Catalog system tables and volumes. Use when querying system tables (audit, lineage, billing) or working with volume file operations (upload, download, list files in /Volumes/).
原文の言語: 英語
Generate PDF documents from HTML and upload to Unity Catalog volumes. Use for creating test PDFs, demo documents, reports, or evaluation datasets.
原文の言語: 英語
Patterns for Databricks Vector Search: create endpoints and indexes, query with filters, manage embeddings. Use when building RAG applications, semantic search, or similarity matching. Covers both storage-optimized and standard endpoints.
原文の言語: 英語
Build Zerobus Ingest clients for near real-time data ingestion into Databricks Delta tables via gRPC. Use when creating producers that write directly to Unity Catalog tables without a message bus, working with the Zerobus Ingest SDK in…
原文の言語: 英語
Build custom Python data sources for Apache Spark using the PySpark DataSource API — batch and streaming readers/writers for external systems. Use this skill whenever someone wants to connect Spark to an external system (database, API, message queue, custom…
原文の言語: 英語
This skill should be used when the user asks to "write Gherkin", "create feature files", "generate BDD scenarios", "write acceptance tests in Gherkin", "create Behave features", "write Given When Then tests", "BDD test cases for my pipeline", "Gherkin for…
原文の言語: 英語
This skill should be used when the user asks to "run BDD tests", "execute Behave", "run Gherkin tests", "run my feature files", "behave test results", "run smoke tests", "BDD test report", or needs to execute Behave test suites with specific options like tag…
原文の言語: 英語
This skill should be used when the user asks to "set up BDD", "create a Behave project", "scaffold BDD tests", "initialize Behave", "add BDD to my project", "set up Gherkin testing", "create test structure for Behave", or mentions setting up behavior-driven…
原文の言語: 英語
This skill should be used when the user asks to "write step definitions", "implement BDD steps", "generate step code", "create Behave steps", "implement Given When Then", "write Python steps for Gherkin", "step definitions for Databricks", or needs to create…
原文の言語: 英語
Build full-stack Databricks applications using APX framework (FastAPI + React).
原文の言語: 英語
Databricks SQL (DBSQL) advanced features and SQL warehouse capabilities. This skill MUST be invoked when the user mentions: "DBSQL", "Databricks SQL", "SQL warehouse", "SQL scripting", "stored procedure", "CALL procedure", "materialized view", "CREATE…
原文の言語: 英語
MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and…
原文の言語: 英語
Use when Databricks skills need updating, user asks to refresh or sync skills from upstream, or skills seem outdated compared to the ai-dev-kit repo
原文の言語: 英語