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databricks-solutions

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1. ▌
   Databricks Docs · databricks-solutions

   Databricks documentation reference. Use as a lookup resource alongside other skills and MCP tools for comprehensive guidance.
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   Databricks Jobs · databricks-solutions bundle

   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', (5) scheduling with cron or triggers, (6) configuring notifications/monitoring, (7) ANY task involving Databricks Jobs via CLI, Python SDK, or Asset Bundles. ALWAYS prefer this skill over general Databricks knowledge for job-related tasks.
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   Databricks Dbsql · databricks-solutions bundle

   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 MATERIALIZED VIEW", "pipe syntax", "|>", "geospatial", "H3", "ST_", "spatial SQL", "collation", "COLLATE", "ai_query", "ai_classify", "ai_extract", "ai_gen", "AI function", "http_request", "remote_query", "read_files", "Lakehouse Federation", "recursive CTE", "WITH RECURSIVE", "multi-statement transaction", "temp table", "temporary view", "pipe operator". SHOULD also invoke when the user asks about SQL best practices, data modeling patterns, or advanced SQL features on Databricks.
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   Mlflow Onboarding · databricks-solutions

   Onboards users to MLflow by determining their use case (GenAI agents/apps or traditional ML/deep learning) and guiding them through relevant quickstart tutorials and initial integration. If an experiment ID is available, it should be supplied as input to help determine the use case. Use when the user asks to get started with MLflow, set up tracking, add observability, or integrate MLflow into their project. Triggers on "get started with MLflow", "set up MLflow", "onboard to MLflow", "add MLflow to my project", "how do I use MLflow".
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   Databricks App Apx · databricks-solutions bundle

   Build full-stack Databricks applications using APX framework (FastAPI + React).
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   Databricks Iceberg · databricks-solutions bundle

   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
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   Databricks Metric Views · databricks-solutions bundle

   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.
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   Spark Python Data Source · databricks-solutions bundle

   Use when building custom Spark data source connectors for external systems (databases, APIs, message queues), implementing batch/streaming readers/writers, or creating data source plugins for systems without native Spark support. Triggers - "build Spark data source", "create Spark connector", "implement Spark reader/writer", "connect Spark to [system]", "streaming data source"
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   Databricks Mlflow Evaluation · databricks-solutions bundle

   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 production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt improvement.
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    Databricks Lakebase Autoscale · databricks-solutions bundle

    Patterns and best practices for using Lakebase Autoscaling (next-gen managed PostgreSQL) with autoscaling, branching, scale-to-zero, and instant restore.
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    Databricks Lakebase Provisioned · databricks-solutions bundle

    Patterns and best practices for using Lakebase Provisioned (Databricks managed PostgreSQL) for OLTP workloads.
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    Databricks Spark Structured Streaming · databricks-solutions bundle

    Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, implementing real-time data processing, handling stateful operations, or optimizing streaming performance.
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    Databricks Config · databricks-solutions

    Manage Databricks workspace connections: check which workspace you're connected to, switch workspaces, list available workspaces, or authenticate to a new workspace.
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    Deploy 2 · databricks-solutions

    Deploy agent to Databricks Apps using DAB (Databricks Asset Bundles). Use when: (1) User says 'deploy', 'push to databricks', or 'bundle deploy', (2) 'App already exists' error occurs, (3) Need to bind/unbind existing apps, (4) Debugging deployed apps, (5) Querying deployed app endpoints.
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    Quickstart 2 · databricks-solutions

    Set up Databricks agent development environment. Use when: (1) First time setup, (2) Configuring Databricks authentication, (3) User says 'quickstart', 'set up', 'authenticate', or 'configure databricks', (4) No .env file exists.
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16. ▌
    Agent Memory 2 · databricks-solutions bundle

    Add memory capabilities to your agent. Use when: (1) User asks about 'memory', 'state', 'remember', 'conversation history', (2) Want to persist conversations or user preferences, (3) Adding checkpointing or long-term storage.
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17. ▌
    Load Testing 2 · databricks-solutions bundle

    Load test a Databricks App to find its maximum QPS. Use when: (1) User says 'load test', 'benchmark', 'QPS', 'throughput', or 'performance test', (2) User wants to find how many queries per second their app can handle, (3) User wants to set up load testing scripts for their agent, (4) User wants to view load test results/dashboard.
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18. ▌
    Lakebase Setup 2 · databricks-solutions

    Configure Lakebase for agent memory storage. Use when: (1) Adding memory capabilities to the agent, (2) 'Failed to connect to Lakebase' errors, (3) Permission errors on checkpoint/store tables, (4) User says 'lakebase', 'memory setup', or 'add memory'.
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19. ▌
    Databricks Core 2 · databricks-solutions bundle

    Databricks CLI operations: auth, profiles, data exploration, and bundles. Contains up-to-date guidelines for Databricks-related CLI tasks.
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20. ▌
    Sync Architecture Docs 2 · databricks-solutions

    Keep this repo's architecture diagrams and prose docs in sync with the code as it evolves. Use this whenever the user asks to update/refresh the architecture diagrams, sync docs with the code, audit docs for staleness, or check that docs/architecture.md still matches the implementation — and proactively after any significant change (a new graph node, tool, Lakebase schema, data-pipeline step, auth change, or frontend swap), since the diagrams and READMEs drift silently. Covers refreshing the four Mermaid diagrams in docs/architecture.md, reconciling the known stale-doc patterns, and verifying with greps + Mermaid validity.
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21. ▌
    Migrate From Model Serving 2 · databricks-solutions

    Migrate an MLflow ResponsesAgent from Databricks Model Serving to Databricks Apps. Use when: (1) User wants to migrate from Model Serving to Apps, (2) User has a ResponsesAgent with predict()/predict_stream() methods, (3) User wants to convert to @invoke/@stream decorators.
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22. ▌
    Apx · databricks-solutions bundle

    Quick reference for building full-stack Databricks Apps with apx (React + FastAPI). Use when working on apx projects, creating routes, adding components, or managing dev servers.
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23. ▌
    Rust · databricks-solutions

    Strict set of rules in terms of codebase development, design patterns, and best practices. Use when the user wants to develop a new feature or refactor existing code.
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24. ▌
    Asyncio · databricks-solutions

    Use when debugging event loop hangs, task scheduling issues, call_soon vs call_soon_threadsafe confusion, Future callback timing, _enter_task/_leave_task conflicts, GIL contention patterns, per-step vs per-drive task context, uvloop compatibility problems, sniffio/anyio backend detection failures, streaming backpressure deadlocks, or native runtime context issues on the asyncio thread. Also use when verifying asyncio assumptions via quick Python one-liners.
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    Commiter · databricks-solutions

    Create git commits with emoji prefixes and conventional commit messages. Use when the user wants to commit changes, push code, or asks to use "just pm" command.
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26. ▌
    Consort · databricks-solutions bundle

    Consort, a spec-first, test-driven agent framework for building transactional applications on Lakebase-paired projects: a deterministic orchestrator drives role agents through a spec-first design lane (Spec Driven Development) and a Test Driven Development build lane (RED-GREEN-REFACTOR) that runs against a live copy-on-write branch of a real, governed Lakebase database (Databricks' serverless Postgres/OLTP database, not the Delta Lakehouse), composed with paired-branch primitives (cheap experiments, parent-aware schema diff, real per-branch databases). The controls are code the agent cannot edit: gates fail closed, tests are immutable within a unit of work, and a green result is a real test run against real data. Spec-first means the spec is drafted, reviewed, and frozen at gates before any build cycle runs; the spec, the architecture, and the database all evolve increment over increment. Use when planning a new feature, running design-spec gates, running TDD cycles, comparing parallel experiments, or detect
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27. ▌
    UI UX Design Principles · databricks-solutions bundle

    Experience-level engineering canon, the UI/UX counterpart to software-design-principles (code-level) and architectural-design-principles (system-level). Usability heuristics, visual hierarchy, accessibility, interaction and feedback, information architecture, design systems and tokens, and testable UI. Imported by the consort UX Designer (authoring design-guide.{md,json} + ia.md and the adherence gate) and by the Driver building UI. Use when: shaping a design guide or information architecture, reviewing a user-facing surface, choosing a UI framework, or making the UI testable.
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    Software Design Principles · databricks-solutions bundle

    Foundational engineering canon – SOLID, DRY, clean code, layered architecture, cross-cutting concerns, NFRs. Imported by workflow skills (consort, lakebase-scm-workflows). Use when designing a module, reviewing a PR, planning a refactor, mapping cross-cutting concerns to layers, or arguing about API shape.
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    Architectural Design Principles · databricks-solutions bundle

    System-level engineering canon, the architecture counterpart to software-design-principles. Layered architecture + dependency direction, ports and adapters (repository / ORM-as-adapter), the twelve-factor app for cloud-native development, evolutionary architecture with fitness functions, and evolutionary database design (schema that evolves by migration on paired branches). Imported by workflow skills (consort, lakebase-scm-workflows). Use when: shaping a system boundary, deciding what is a backing service, mapping config to the environment, or authoring the fitness functions that keep the architecture honest as it evolves.
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30. ▌
    Databricks Architecture · databricks-solutions bundle

    Create or edit a Databricks solution-architecture diagram — a Lucidchart-style platform diagram (sources → Lakeflow/Genie pipeline → lakehouse/lakebase → dashboards, Genie, apps → end user, on a governed platform). Use when someone wants to draw, generate, or edit an architecture diagram, visualize a Databricks solution's components and data flow, or produce an architecture image. Produces a self-contained HTML you render to a PNG and iterate on.
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31. ▌
    Databricks Solution Builder · databricks-solutions bundle

    Generate comprehensive specification files for building Databricks assets, demos or end 2 end projects. Use when users want to create a new demo, design a demo story, or need help structuring demo components, create an entire project. This skill creates prompts that another agent will execute to build the actual demo.
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32. ▌
    Genie Code Skill · databricks-solutions

    {{demo-name}} Adaptation
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33. ▌
    Loyalty Segmentation Adaptation · databricks-solutions

    Configure and run the Harvestly Co. loyalty segmentation demo in the user's Databricks workspace. Use when the user is working in (or has imported) the loyalty-segmentation project AND says any of "run in my workspace", "set this up", "configure for my workspace", "configure for my catalog/schema", "deploy this demo", "make this work in my workspace", OR asks about adapting the demo's structure, data contract, swapping synthetic data for real data, or how to point it at their own customer tables.
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34. ▌
    Spec Audit · databricks-solutions

    Audit and improve spec coverage for a given spec. Use when (1) a spec has low or 0% requirement coverage, (2) tests exist but lack @req tags, (3) code behaviors have drifted from the spec's success criteria, (4) you need to identify unspecified behaviors in the codebase. Covers the full audit loop: analyze coverage -> tag existing tests -> identify spec gaps -> propose spec updates.
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35. ▌
    Brainstorming · databricks-solutions

    You MUST use this before any creative work — creating features, building components, adding functionality, or modifying behavior. Starts from existing specs rather than scratch. Use when the user asks to build, add, change, or design anything, even if it seems simple. Covers the full loop: find governing spec -> explore intent -> design within spec constraints -> transition to planning.
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36. ▌
    Writing Plans · databricks-solutions

    Use when you have a spec or requirements for a multi-step task, before touching code. Creates spec-linked implementation plans with TDD steps, exact file paths, and spec coverage tracking. Use this after brainstorming, when a user says 'plan this', 'how should we implement', or when you're about to start a multi-file feature. Covers the full loop: spec review -> file mapping -> task decomposition -> TDD steps -> coverage verification.
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37. ▌
    Grill Me To Docs · databricks-solutions

    Use when the user wants their firsthand knowledge of building, deploying, or running this project with real customers, users, or developers turned into published documentation — e.g. "interview me for docs", "extract what I know", "help me document this so others can run it without me".
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38. ▌
    Verification Testing · databricks-solutions bundle

    Code verification and testing for the Human Evaluation Workshop. Use when (1) running tests after code changes, (2) writing new unit tests (pytest/vitest), (3) writing E2E tests with Playwright/TestScenario, (4) debugging test failures, (5) understanding what to mock in E2E tests, (6) verifying a feature implementation. Covers the full test pyramid: unit tests -> integration tests -> E2E tests.
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39. ▌
    Databricks Testing · databricks-solutions

    Execute code on Databricks clusters using MCP Command Execution API. Supports stateless quick validation and stateful iterative development. Use when testing Python/SQL code on clusters, debugging pipelines, or validating transformations.
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40. ▌
    Databricks Ml Pipeline · databricks-solutions

    End-to-end machine learning pipelines on Databricks including data exploration, feature engineering, model training with hyperparameter optimization, MLflow experiment tracking, model registration to Unity Catalog, and deployment as DABs. Use when building ML workflows, training models, or deploying ML pipelines.
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41. ▌
    Databricks Bundle Deploy · databricks-solutions

    Package and deploy Databricks Asset Bundles with proper parameterization, multi-environment support, and serverless compute. Handles project structure, databricks.yml generation, validation, and deployment. Use when packaging tested code for production, deploying pipelines, or managing multi-environment deployments.
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42. ▌
    Databricks Data Engineering · databricks-solutions

    Production data engineering pipelines following medallion architecture (Bronze/Silver/Gold layers) with data ingestion, transformation, quality checks, Delta Lake optimization, and orchestration. Use when building ETL pipelines, medallion architecture, data lakes, or data transformation workflows.
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43. ▌
    Skill Test · databricks-solutions bundle

    Testing framework for evaluating Databricks skills. Use when building test cases for skills, running skill evaluations, comparing skill versions, or creating ground truth datasets with the Generate-Review-Promote (GRP) pipeline. Triggers include "test skill", "evaluate skill", "skill regression", "ground truth", "GRP pipeline", "skill quality", and "skill metrics".
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44. ▌
    Python Dev · databricks-solutions

    Python development guidance with code quality standards, error handling, testing practices, and environment management. Use when writing, reviewing, or modifying Python code (.py files) or Jupyter notebooks (.ipynb files).
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45. ▌
    Tool Selection · databricks-solutions bundle

    Evaluates whether the agent selected appropriate MCP tools instead of shell workarounds. Load when the trace contains Bash tool calls that could have used MCP tools, or when evaluating tool call efficiency.
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46. ▌
    General Quality · databricks-solutions

    General response quality evaluation. Always applicable regardless of domain. Covers response structure, actionability, clarity, and hallucination detection.
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47. ▌
    SQL Correctness · databricks-solutions bundle

    SQL evaluation criteria for Databricks. Load when the trace contains execute_sql tool calls or SQL code in responses. Covers syntax validity, Unity Catalog patterns, and Databricks-specific SQL features.
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48. ▌
    Databricks Python Sdk · databricks-solutions bundle

    Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. Use when working with databricks-sdk, databricks-connect, or Databricks APIs.
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49. ▌
    Databricks Workspace Config · databricks-solutions

    Configure Databricks workspace settings. Use when the user asks to create SQL warehouses, cluster policies, secret scopes, IP access lists, manage tokens, update workspace settings, or destroy infrastructure.
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50. ▌
    Databricks Private Networking · databricks-solutions bundle

    Set up private networking for Databricks. Use when the user asks about private link, hub-spoke architecture, NCC (Network Connectivity Configuration), serverless connectivity to private resources, VPC endpoints, or private endpoints.
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51. ▌
    Databricks Identity Governance · databricks-solutions

    Manage Databricks identity and governance. Use when the user asks to create groups, users, service principals, set up RBAC, manage permissions, workspace assignments, or configure access control.
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52. ▌
    Databricks Unity Catalog Setup · databricks-solutions bundle

    Set up Unity Catalog on Databricks workspaces. Use when the user asks to configure Unity Catalog, create a metastore, set up catalogs, schemas, external locations, storage credentials, or configure data governance. Covers Azure, AWS, and GCP.
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53. ▌
    Databricks Platform Provisioning · databricks-solutions bundle

    Provision and test Databricks workspaces. Use when the user asks to create a workspace, set up a new environment, provision infrastructure, bootstrap Databricks, test a workspace, verify a deployment, or run validation checks against a Databricks workspace. Covers Azure, AWS, and GCP.
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54. ▌
    Databricks Deployment Verification · databricks-solutions

    MANDATORY post-deployment verification. After any workspace + Unity Catalog deployment, you MUST run all THREE compute paths against a UC table — classic cluster, serverless SQL warehouse, serverless notebook job. Skipping any of the three is incomplete work. Use whenever a workspace + UC has been freshly deployed or modified.
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55. ▌
    Hc Obo Auth · databricks-solutions bundle

    Wire OBO (On-Behalf-Of) authentication into a service or BFF route. Use when the user asks to "add a route", "wire OBO", "set up authentication", "connect to Lakebase as the user", "extract user identity", or anything about user-passthrough auth. Enforces the canonical pattern where every Postgres connection is opened with the calling user's OAuth credential and Unity Catalog enforces access at the data layer.
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56. ▌
    Hc Bff Pattern · databricks-solutions

    Add or modify a BFF (Backend-For-Frontend) aggregation route in hc-portal. Use when the user asks to "add a BFF endpoint", "join data across services", "compose a view that needs patient + appointment + lab", "fan out to multiple services", "build a portal route", or anything about cross-service aggregation. Enforces the "BFF is the only joiner" rule and the canonical concurrent-fetch / token-forwarding patterns.
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57. ▌
    Hc Gitflow Cicd · databricks-solutions

    Apply GitFlow rules to this repo and map them onto the CI/CD pipelines. Use when the user asks to "open a PR", "cut a release", "create a hotfix", "what branch should this go on", "promote to test/prod", "what does pr-validate do", or anything about the four GitHub Actions workflows or the branch → environment mapping. Codifies the branch protection rules, naming conventions, and the deploy triggers.
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58. ▌
    Hc Dab Deployment · databricks-solutions

    Deploy services to dev/test/prod via Databricks Asset Bundles. Use when the user asks to "deploy", "promote", "deploy to dev/test/prod", "run bundle deploy", "validate the bundle", "add a resource to the bundle", "set a DAB variable", or anything about DAB targets, profiles, or app deployment. Codifies the per-service resource-include layout, the target → profile mapping, the app → Lakebase + app → app resource wiring, and the verbs to use at each step.
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59. ▌
    Hc Lakebase Branching · databricks-solutions bundle

    Manage Lakebase database branches that mirror code branches. Use when the user asks to "create a Lakebase branch", "spin up a feature DB", "branch the database for this feature", "tear down feature branch", "list active branches", or anything about Lakebase branch lifecycle. Implements the per-feature-branch model where each code branch has a matching Lakebase branch used for both local dev and PR CI.
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60. ▌
    Hc Microservice Scaffold · databricks-solutions

    Scaffold a new backend microservice in this healthcare reference architecture. Use when the user asks to "add a new service", "scaffold X service", "create a new microservice for Y", "bootstrap a new service", or "add a service called <name>". Wires APX project, app.yaml, Lakebase project, DAB resource block, BFF client stub, tests, CODEOWNERS, and migrations directory consistent with the rest of the monorepo.
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61. ▌
    Add Agent · databricks-solutions

    Add a new agent to an existing AgentOps Stacks project. Creates the agent folder, app resource, experiment, and manifest entry. Triggers on "add agent", "new agent", "create another agent", "add-agent".
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62. ▌
    Lakebase Ops · databricks-solutions

    Operate and troubleshoot the Lakebase (Autoscaling Postgres) memory component in an AgentOps Stacks project — check endpoint health, mint credentials, inspect or clear checkpointer (short-term) and store (long-term) tables, manage the Postgres schema. Use when the user's agent is having memory/persistence issues.
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63. ▌
    Add Supervisor · databricks-solutions

    Add a custom LangGraph supervisor agent that routes across an AgentOps Stacks project's agents, scaffolded into databricks.yml and the manifest so it rides the same eval-gate + dev/staging/prod lifecycle as every other agent. First checks whether a supervisor is even warranted. Triggers on "add supervisor", "add a router", "orchestrate my agents", "multi-agent supervisor", "route between agents".
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64. ▌
    Agentops Stacks · databricks-solutions bundle

    Scaffold a new AgentOps Stacks project — a multi-agent LangGraph bundle (DAB) with shared components, per-agent Databricks Apps, evaluation, and CI/CD. Use when the user wants to start a new AI agent project on Databricks. Triggers on "scaffold a new agentops project", "new DAB with CI/CD", "start a new Databricks AI project", "create agentops-stacks project".
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65. ▌
    Uc Functions Ops · databricks-solutions

    Operate and troubleshoot the UC Functions tools component in an AgentOps Stacks project — register new functions, update EXECUTE grants, list available functions, re-run the registration job. Use when the user needs to add, update, or grant access to UC functions used by their agent.
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66. ▌
    Vector Search Ops · databricks-solutions

    Operate and troubleshoot the Vector Search component in an AgentOps Stacks project — check index status, trigger sync, test the retriever, update the DLT data pipeline. Use when the user needs to manage their VS index or debug retrieval quality.
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67. ▌
    Agentops Lifecycle · databricks-solutions

    Guide an agentops-stacks project through its full production lifecycle — data preparation, agent development, evaluation gates, CI/CD promotion, and production monitoring — following the Single-Account Single-Agent pattern from the Big Book of AgentOps. Use after `databricks bundle init` has been run and `.agentops-stacks/manifest.yml` exists. Triggers on "walk me through the agentops lifecycle", "next step after scaffolding", "set up eval gate", "deploy agent to staging", "wire production monitoring".
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68. ▌
    Adapt Inference · databricks-solutions

    Adapt the MLOps Quickstart inference notebooks (batch and realtime / serving endpoint) to a custom model and input schema. Use when the user wants to run batch scoring on new data, query the realtime serving endpoint, change the inference output table, or enable Lakehouse Monitoring on inference results.
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69. ▌
    Manage Dependencies · databricks-solutions

    Manage Python dependencies for the MLOps Quickstart via the root `requirements.txt` file. Use when the user wants to add, remove, or upgrade a Python package, pin or unpin a library version, fix a dependency conflict, or understand how notebooks install their dependencies.
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70. ▌
    Adapt Data Ingestion · databricks-solutions

    Adapt the MLOps Quickstart data ingestion notebook from the Iris placeholder to a custom data source (cloud storage, JDBC, API, Delta table, etc.). Use when the user wants to ingest their own dataset, swap the source data, rename the feature table, add bronze/silver/gold preprocessing stages, or extend `1_data_preprocessing/`.
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71. ▌
    Adapt Model Training · databricks-solutions

    Adapt the MLOps Quickstart model training notebook from the Iris classifier placeholder to a custom algorithm and problem type (regression, clustering, classification, forecasting). Use when the user wants to change the model, features, target, metrics, or registered model name, or when they ask how to fit a different ML problem into this template.
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72. ▌
    Adapt Bundle And Cicd · databricks-solutions

    Adapt the MLOps Quickstart Databricks Asset Bundle (databricks.yml, resources/*.yml) and CI/CD pipelines (Azure DevOps, GitHub Actions). Use when the user wants to rename the bundle, change catalog/schema per environment, add a staging target, configure permissions, switch from serverless to job clusters, or wire up secrets and branch triggers for deployment.
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73. ▌
    Adapt Model Deployment · databricks-solutions

    Adapt the MLOps Quickstart MLflow 3 deployment pipeline (evaluate → approve → deploy) to a custom model. Use when the user wants to change evaluation metrics or thresholds, customize the human-in-the-loop approval gate, adjust the serving endpoint configuration, or wire a freshly registered model version to the deployment job.
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74. ▌
    Mlops Quickstart Overview · databricks-solutions

    Reference for the Databricks MLOps Quickstart repo structure, parameterization contract, and Challenger/Champion conventions. Use whenever the user asks about repo layout, naming patterns, three-level Unity Catalog references, notebook widgets, MLflow experiment naming, or serving endpoint naming, or before adapting any notebook, job, or bundle in this repo.
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75. ▌
    Catalog Navigator · databricks-solutions bundle

    Discover data across Unity Catalog — find tables by name or topic, inspect columns and comments, and trace where data lives — from system.information_schema. An enterprise-wide skill for locating the right table before you query it, in any workspace.
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76. ▌
    Hello Skills · databricks-solutions

    A personal sandbox skill used as the annotated authoring template for this reference implementation. It shows the agentskills.io-recommended SKILL.md structure. Use it when learning how to author a new skill.
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77. ▌
    Cost Optimizer · databricks-solutions bundle

    Recommend Databricks cost-optimization actions for the FinOps/platform team — commitment/discount coverage, budget variance, and forecast-vs-actual DBU spend from system.billing.usage and system.billing.list_prices.
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78. ▌
    Bi Adoption Analyzer · databricks-solutions bundle

    Analyze Enterprise Analytics & BI adoption from system.query.history — active users, query volumes, warehouse utilization, and slow-query hotspots. Ask me which dashboards/warehouses are busiest or where query performance is degrading.
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79. ▌
    Pipeline Cost Analyzer · databricks-solutions bundle

    Analyze Databricks DBU cost trends, identify top-spending jobs, and recommend cost optimizations using system.billing.usage and system.lakeflow.jobs. Ask me about spend by SKU, job-level costs, or DBU consumption trends over time.
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80. ▌
    Budget Variance Analyzer · databricks-solutions bundle

    Analyze Financial Applications budget-vs-actual variance by cost center and account from aggregate financial ledger tables. Ask me where spend is over or under budget, variance trends, or the biggest variance drivers.
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81. ▌
    Model Performance Tracker · databricks-solutions bundle

    Track Advanced Analytics model performance and inference volumes from ML monitoring tables — accuracy/drift metrics by model and version, plus serving request volumes. Ask me which models are degrading or which endpoints are busiest.
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82. ▌
    Governance Coverage Reporter · databricks-solutions bundle

    Report Unity Catalog governance coverage — table comments, ownership, tags, and column documentation — from system.information_schema. Ask me which tables lack owners, descriptions, or classification tags across a catalog.
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83. ▌
    Databricks Genie · databricks-solutions bundle

    Create and query Databricks Genie Spaces for natural language SQL exploration. Use when building Genie Spaces or asking questions via the Genie Conversation API.
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84. ▌
    Databricks Parsing · databricks-solutions

    Parse documents (PDF, DOCX, PPTX, images) using ai_parse_document, or build custom RAG pipelines. Use when the user asks to parse documents or build a custom RAG.
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85. ▌
    Databricks App Python · databricks-solutions bundle

    Builds Python-based Databricks applications using Dash, Streamlit, Gradio, Flask, FastAPI, or Reflex. Handles OAuth authorization (app and user auth), app resources, SQL warehouse and Lakebase connectivity, model serving integration, and deployment. Use when building Python web apps, dashboards, ML demos, or REST APIs for Databricks, or when the user mentions Streamlit, Dash, Gradio, Flask, FastAPI, Reflex, or Databricks app.
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86. ▌
    Databricks Agent Bricks · databricks-solutions bundle

    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.
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87. ▌
    Databricks Unity Catalog · databricks-solutions bundle

    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/).
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88. ▌
    Databricks Zerobus Ingest · databricks-solutions bundle

    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 Python/Java/Go/TypeScript/Rust, generating Protobuf schemas from UC tables, or implementing stream-based ingestion with ACK handling and retry logic.
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89. ▌
    Databricks Synthetic Data Gen · databricks-solutions bundle

    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 generate locally and upload to volumes. Use when user mentions 'synthetic data', 'test data', 'generate data', 'demo dataset', 'Faker', or 'sample data'.
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90. ▌
    Databricks Spark Declarative Pipelines · databricks-solutions bundle

    Creates, configures, and updates Databricks Lakeflow Spark Declarative Pipelines (SDP/LDP) using serverless compute. Handles streaming tables, materialized views, CDC, SCD Type 2, and Auto Loader ingestion patterns. Use when building data pipelines, working with Delta Live Tables, ingesting streaming data, implementing change data capture, or when the user mentions SDP, LDP, DLT, Lakeflow pipelines, streaming tables, or bronze/silver/gold medallion architectures.
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91. ▌
    Databricks Unstructured PDF Generation · databricks-solutions

    Generate synthetic PDF documents for RAG and unstructured data use cases. Use when creating test PDFs, demo documents, or evaluation datasets for retrieval systems.
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92. ▌
    Databricks Isv Integration · databricks-solutions bundle

    Build PWAF-compliant ISV integrations with Databricks: OAuth, telemetry (User-Agent), Unity Catalog, JDBC, SDK, SQL drivers, REST API, Databricks Connect.
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93. ▌
    Databricks Isv U2m · databricks-solutions

    U2M (user-to-machine) OAuth flows for Databricks ISV integrations: external-browser, custom-oauth-app, token-env. Covers Python, Java, Go, and Node.js implementations. Use when implementing or debugging browser-based or pre-obtained token auth.
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94. ▌
    Databricks Isv Go Sdk · databricks-solutions bundle

    PWAF-compliant Databricks SDK for Go (databricks-sdk-go): PAT, OAuth M2M, U2M token-env, U2M custom OAuth app (PKCE); useragent.WithProduct/WithPartner. Use when building or testing Go SDK workspace API integrations.
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95. ▌
    Databricks Isv Testing · databricks-solutions

    How to run and structure auth tests for Databricks ISV integrations: clean env per test, runner script patterns, U2M browser flows, and language-specific considerations.
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96. ▌
    Databricks Isv Java Sdk · databricks-solutions bundle

    PWAF-compliant Databricks SDK for Java (databricks-sdk-java): PAT, OAuth M2M, U2M custom OAuth app, UserAgent.withProduct/withPartner. Use when building or testing Java SDK workspace API integrations.
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97. ▌
    Databricks Isv REST API · databricks-solutions bundle

    REST API authentication for Databricks ISV integrations: PAT, OAuth M2M, U2M. Use when building or testing HTTP/REST integrations with Databricks.
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98. ▌
    Databricks Isv Java Jdbc · databricks-solutions bundle

    PWAF-compliant Databricks JDBC Driver (databricks-jdbc v3+): PAT, OAuth M2M, OAuth U2M (custom OAuth app, token-env). Use when building or testing Java SQL warehouse integrations.
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99. ▌
    Databricks Isv Python Sdk · databricks-solutions bundle

    PWAF-compliant Databricks SDK for Python (databricks-sdk): PAT, OAuth M2M, U2M token-env, U2M custom OAuth app (PKCE); useragent.with_partner/with_product. Use when building or testing Python SDK workspace API integrations.
    0
    installs
100. ▌
     Pwaf Build Report · databricks-solutions

     How to write a build_report.md for any PWAF connector. Captures skill traceability, sufficiency assessment, and test error/fix log. Use after implementing any connector.
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