Skills in this repository
SkillsMP has collected 93 skills from databricks-solutions/vibe-coding-workshop-template. Open a skill to review its source and details.
databricks-solutions/vibe-coding-workshop-templateShowing 40 of 93 collected skills.
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…
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…
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…
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`…
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…
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."…
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…
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…
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…
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.…
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…
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…
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…
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…
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…
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…
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…
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…
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,…
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…
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…
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…
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…
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…
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,…
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),…
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,…
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).
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.
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…
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.…
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…
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…
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…
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…
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…
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…
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…
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…
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),…