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SkillsMP는 databricks-solutions/vibe-coding-workshop-template에서 88개의 skill을 수집했습니다. skill을 열어 소스와 세부 정보를 확인하세요.
databricks-solutions/vibe-coding-workshop-template수집된 skill 88개 중 40개를 표시합니다.
Use when you need to measure agent quality or create scoring criteria for evaluation gates. Covers how to pick and configure scorers — even if you just want "is my agent safe and accurate?" without knowing which MLflow classes to use. Also use when building…
원문 언어: 영어
Use when setting up live quality monitoring for a deployed agent. Covers registered scorers with sampling, trace archival to Delta, metric backfill, and periodic human review — even if you just want "monitor my agent's safety score in production." Also use…
원문 언어: 영어
End-to-end orchestrator for setting up Databricks observability including Lakehouse Monitoring, Anomaly Detection, AI/BI Dashboards, and SQL Alerts. Guides users through monitor creation for Gold tables, schema-level anomaly detection for…
원문 언어: 영어
Comprehensive guide for Databricks SQL Alerts V2 - config-driven alerting framework with SDK deployment, hierarchical job architecture (5 atomic + 1 composite), proactive EXPLAIN-based query validation, and partial success patterns. Use when setting up SQL…
원문 언어: 영어
Schema-level anomaly detection for Databricks Unity Catalog using the Data Quality API (Public Preview). Automatically monitors table freshness and completeness using ML models. Use when setting up schema-wide data reliability monitoring, detecting stale or…
원문 언어: 영어
Databricks DQX framework patterns for advanced data quality validation with detailed failure insights and flexible quarantine strategies. Use when implementing Silver/Gold layer validation, needing richer diagnostics than DLT expectations, or requiring…
원문 언어: 영어
Scaffold new Databricks AppKit applications using the Databricks CLI and Agent Skills. Creates blank or plugin-enabled AppKit projects (Lakebase, Analytics, Genie, Files). Use when asked to create a Databricks app, scaffold an AppKit project, bootstrap a new…
원문 언어: 영어
Entry-point navigator for Databricks AppKit + Lakebase development. Routes tasks to the correct skill based on keyword detection: scaffolding, plugin integration, feature building, or deployment. This is a routing skill -- it does not generate code. It…
원문 언어: 영어
Build full-stack UI and backend features on a Databricks AppKit project from a PRD or feature spec. Covers SQL query design, type generation, React frontend with AppKit UI components, backend plugin wiring, and distinctive visual design. Use when asked to…
원문 언어: 영어
Wire a Lakebase PostgreSQL backend into an existing AppKit project. Covers database schema design from a PRD, idempotent DDL, Express API routes with mock fallback, React data hooks, and local testing. PRD-independent patterns that apply to any AppKit +…
원문 언어: 영어
Wire a Databricks Model Serving or Agent endpoint into an existing AppKit project using the Serving plugin. Covers endpoint registration, app.yaml resource binding, streaming and invoke React hooks, conversation state management, agent response mapping, and…
원문 언어: 영어
Wire an AppKit frontend (Databricks App) to a separate Agent App backend (Databricks App hosting mlflow.genai.agent_server or ResponsesAgent) with OBO token forwarding via x-forwarded-access-token, app-to-app service principal auth, dual-format SSE streaming,…
원문 언어: 영어
Add persistent chat history to an AppKit application using the Lakebase plugin and the Serving plugin (both AppKit-native, no Vercel AI SDK). Covers database schema, session auth middleware, a server-side proxy route that streams from…
원문 언어: 영어
Add user feedback (thumbs up/down) to an AppKit chat application, linked to MLflow assessments via the Databricks Assessments REST API. Covers the Vote table, feedback API routes (with AppKit-native auth via…
원문 언어: 영어
GenAI agent skill navigator for the `genai-agents/` folder. Start here to route agent-building, MLflow GenAI, tracing, Databricks Apps Track A, SDLC, evaluation, deployment, monitoring, prompt iteration, and vibecoding runtime tasks to the correct specialized…
원문 언어: 영어
Session-start behavioral manifest for running this workshop inside Databricks Genie Code. Read FIRST when the client is Genie Code (the coding agent embedded in the Databricks workspace) so you begin knowing how it behaves — surface/page tool-scoping, the…
원문 언어: 영어
Use when deriving a Databricks Agent Spec and Agent Tool Plan from docs/design_prd.md before building Track A agents. Covers web-researched MCP recommendations, Databricks managed MCP choices, dynamic SQL MCP catalog/schema inputs, optional Knowledge…
원문 언어: 영어
Use when adding MLflow tracing to a Node/TypeScript GenAI agent. Covers the official `mlflow-tracing` and `mlflow-openai` npm packages: `mlflow.init()`, `tracedOpenAI`, manual `mlflow.trace`/`@mlflow.trace`/`mlflow.withSpan`, nested spans, sessions/users, and…
원문 언어: 영어
Use when connecting any agent to data and external capabilities. Covers Databricks managed MCP servers (Vector Search, Genie, SQL, UC Functions), the databricks-mcp client library, _meta parameters, external MCP servers, code interpreter, non-MCP connections,…
원문 언어: 영어
Use when you need central governance, observability, guardrails, or rate limits across LLM endpoints and MCP servers used by agents. Covers creating and configuring Databricks AI Gateway endpoints, enabling usage tracking, inference tables (UC Delta audit),…
원문 언어: 영어
Use when building a custom agent with the OpenAI Agents SDK and MLflow ResponsesAgent. Covers Agent class, Runner, @function_tool, handoffs, streaming, ModelConfig, mlflow.models.set_model(), autolog, and manual tracing. Track A Step 2. Produces a customized,…
원문 언어: 영어
Use when wiring tools into an OpenAI Agents SDK agent: local Python function tools, DatabricksMCPClient connections, and MCPServerSse for MCP servers. Track A Step 3. Builds on shared concepts from F3 (Tools and Data Access).
원문 언어: 영어
Use when configuring agent permissions, implementing OBO, or granting access to Databricks resources. Covers App Authorization (service principal), User Authorization (OBO), auth passthrough for Model Serving, and MCP server authentication. Track A Step 4.
원문 언어: 영어
Use when implementing stateful agents that need conversation memory. Covers short-term continuity via AsyncDatabricksSession (prescribed for OpenAI Agents SDK), long-term preferences via DatabricksStore, graceful degradation, and thread_id resolution. Track A…
원문 언어: 영어
Use when evaluating an agent's response quality and safety before deployment. Covers running agent-evaluate, evaluation dataset format, built-in judges (relevance, groundedness, safety), interpreting results, and customizing eval datasets. Track A Step 6.…
원문 언어: 영어
Use when deploying an agent to Databricks Apps and querying the deployed endpoint. Covers databricks apps create, sync, deploy, OAuth token auth, PAT limitations, curl and Python SDK query methods, and the redeploy workflow. Track A Step 7. Consumes…
원문 언어: 영어
Scaffold a minimal MCP tool-calling agent with Genie Spaces and deploy it to Databricks Model Serving in 5 steps, following the canonical OpenAI MCP Tool Calling Agent notebook verbatim. Produces a working endpoint testable in AI Playground and consumable by…
원문 언어: 영어
Runtime schema validation patterns for Gold layer merge scripts. Use when creating merge scripts to ensure DataFrame columns match target DDL schemas, validate column mappings before MERGE operations, and catch schema issues before deployment. Includes the…
원문 언어: 영어
Create multi-phase project plans for Databricks data platform solutions with Agent Domain Framework and Agent Layer Architecture. Includes interactive Quick Start with key decisions, industry-specific domain patterns, complete phase document templates (Use…
원문 언어: 영어
Use when starting a new custom agent project on Databricks Apps. Clone the agent-openai-agents-sdk template, run quickstart, start the local dev server, verify the built-in chat UI. Track A Step 1. Produces a running local agent project ready for…
원문 언어: 영어
Transforms the assistant into a Senior Databricks Solutions Architect Agent that designs, implements, and reviews production-grade Databricks solutions following official best practices. Enforces Unity Catalog governance, Delta Medallion architecture, DLT…
원문 언어: 영어
Create dual-format ad-hoc exploration notebooks for Databricks workspace (.py) and local Jupyter (.ipynb) with Databricks Connect. Use when building data exploration tools, debugging data quality issues, or creating interactive analysis notebooks. Supports…
원문 언어: 영어
Use when a deployed Databricks Apps agent is failing, returning errors, or behaving unexpectedly. Covers systematic debugging of local dev, bundle configuration, deployment, runtime errors, authentication, resource permissions, and Lakebase memory. Track A…
원문 언어: 영어
Enforces documentation organization standards and provides comprehensive framework documentation authoring for Databricks projects. Operates in two modes: (1) Organizational Enforcement — validates file placement (root directory rules, docs/ hierarchy),…
원문 언어: 영어
Use when any agent or downstream skill needs Unity Catalog schemas and managed volumes (knowledge_sources, agent_outputs, memory tables, benchmark tables, etc.) provisioned idempotently. Foundation Step 0 — runs before MLflow tracing setup and before any…
원문 언어: 영어
Use when adding user, session, environment, or version context to MLflow traces — Python or TypeScript. Covers the reserved metadata fields (`mlflow.trace.user`, `mlflow.trace.session`), auto-populated environment metadata (`mlflow.source.*`, Git provenance,…
원문 언어: 영어
Use after evaluation (Step 4) passes but BEFORE deployment (Step 6). Covers translating technical eval metrics to business-meaningful terms, running a structured review with business and compliance stakeholders, capturing sign-off, and blocking promotion if…
원문 언어: 영어
Guides the creation of new Agent Skills following the AgentSkills.io specification. Covers required frontmatter (name, description), optional metadata, directory structure (SKILL.md, references/, scripts/, assets/), progressive disclosure principles, and…
원문 언어: 영어
Grain definition patterns for fact tables during the Gold layer design phase. Use when choosing grain types (transaction, aggregated, snapshot), documenting grain in YAML schemas, and applying the PK-grain decision tree to determine whether a fact table needs…
원문 언어: 영어
Dimension design patterns for Gold layer modeling. Covers role-playing dimensions, degenerate dimensions, junk dimensions, mini-dimensions, outrigger dimensions, denormalized hierarchies, null handling, and flags as textual attributes. Use when designing…
원문 언어: 영어