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Deployment, cloud, containers, CI/CD, monitoring, and infrastructure
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- 01 Prompt RegistryUse 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 "register my prompt and give me a stable URI." Also use when A/B testing prompt versions or connecting prompt changes to evaluation runs. SDLC Step 1.Votes: 0GitHub stars: 6
- 02c Trace Context And EnvironmentsUse 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, model id), the `APP_ENVIRONMENT` override pattern for production, custom deployment metadata, and how this composes with `client_request_id` and `gen_ai.*`. Foundation Step 2c. Sibling to F2 (Python tracing) and F2b (TS tracing).Votes: 0GitHub stars: 6
- 02b Typescript TracingUse 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 when to choose a custom OTLP fallback. Foundation Step 2b. Sibling to the Python tracing skill (Foundation Step 2). Consumes the MLflow environment from Step 1.Votes: 0GitHub stars: 6
- 02 Experiment Tracing And Uc StorageUse 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. Foundation Step 2. Consumes MLflow environment from Step 1.Votes: 0GitHub stars: 6
- 01 Mlflow Genai FoundationUse 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 MLflow environment consumed by all subsequent skills.Votes: 0GitHub stars: 6
- 00 Course OrchestratorGenAI 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 skill. Uses tiered loading and keyword routing so agents read only the skills needed for the current task.Votes: 0GitHub stars: 6
- 01 Dlt Expectations PatternsSpark 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 expectations without code deployment. Supports severity-based filtering (critical vs warning) and quarantine patterns. Standardizes on `import dlt` for the DQ-rules framework; the modern `dp` API (`from pyspark import pipelines as dp`) also supports...Votes: 0GitHub stars: 6
- 03 Sql Alerting PatternsComprehensive 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 alerts, creating alert configuration tables, deploying alerts via Databricks SDK (V2 dict-based or typed classes), or troubleshooting alert failures. Includes config-driven patterns, fully qualified table names (no parameters), severity-bas...Votes: 0GitHub stars: 6
- 00 Observability SetupEnd-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 freshness/completeness, dashboard design with monitoring widgets, and config-driven alerting. Orchestrates mandatory dependencies on monitoring skills (lakehouse-monitoring-comprehensive, anomaly-detection, databricks-aibi-dashboards, sql-alerting-patterns) ...Votes: 0GitHub stars: 6
- 00 Ml Pipeline SetupMLflow 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 non-negotiable rules covering experiment paths, dataset logging, UC model registration, NaN handling, label binarization, feature engineering workflows, and signature-driven preprocessing.Votes: 0GitHub stars: 6
- 03 DeduplicationDelta MERGE deduplication pattern for Gold layer to prevent duplicate source key errors. Use when merging from Silver to Gold tables where Silver may contain duplicate business keys due to incremental DLT streaming, CDC patterns, SCD Type 2 tracking, or multiple batch loads. Prevents DELTA_MULTIPLE_SOURCE_ROW_MATCHING_TARGET_ROW_IN_MERGE errors by deduplicating before merge.Votes: 0GitHub stars: 6
- 02 Merge PatternsProvides production-grade patterns for Gold layer MERGE operations from Silver to Gold tables. Covers column mapping, schema evolution, SCD Type 1/2 patterns, fact table aggregation, and preventing variable naming conflicts with PySpark functions. Use when creating Gold layer MERGE operations, handling column name differences between Silver and Gold, implementing SCD Type 1/2 dimensions, aggregating fact tables, or troubleshooting MERGE errors. Triggers on "Gold merge", "MERGE operation", "up...Votes: 0GitHub stars: 6
- 03 Fact Table PatternsAdvanced fact table design patterns for Gold layer modeling. Covers measure additivity classification (additive, semi-additive, non-additive), factless fact tables, accumulating snapshot facts, consolidated fact tables, header/line fact patterns, late-arriving facts and dimensions, and NULL handling in measures. Use when designing fact tables beyond basic transaction/aggregate patterns, classifying measure types, or handling complex fact scenarios. Triggers on "fact pattern", "factless", "acc...Votes: 0GitHub stars: 6
- 09 Simple Agent ScaffoldScaffold 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 06-appkit-serving-wiring. No evaluation, memory, or prompt registry — add those later via the existing worker skills. Use when creating a simple agent, scaffolding a new agent, building a quick tool-calling agent, or connecting an agent t...Votes: 0GitHub stars: 6
- Unity Catalog ConstraintsUnity 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 keys), facts referencing surrogate PKs via FOREIGN KEY constraints, NOT NULL requirements for PK columns, proper dimensional modeling patterns (SCD Type 1/2, date dimensions), production deployment error prevention (never define FK inlin...Votes: 0GitHub stars: 6
- Schema Management PatternsProvides 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, enabling Predictive Optimization at schema level using ALTER SCHEMA ENABLE PREDICTIVE OPTIMIZATION (not TBLPROPERTIES), schema variable usage, and common pitfalls to avoid. Use when creating Unity Catalog schemas programmatically in setup scr...Votes: 0GitHub stars: 6
- Databricks Python ImportsPatterns 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 notebook context, MLflow model packaging path requirements, and troubleshooting ModuleNotFoundError. Use when creating shared modules, deploying jobs that import local code, or packaging MLflow models with local dependencies.Votes: 0GitHub stars: 6
- Databricks Autonomous OperationsEnd-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 API reference. Use when deploying bundles, running jobs, monitoring pipelines, troubleshooting ANY failure, or operating in a self-healing deploy-fix-redeploy cycle. Triggers on "deploy", "bundle deploy", "run job", "run pipeline", "make it...Votes: 0GitHub stars: 6
- Skill Freshness AuditSystematic 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 official documentation URLs embedded in skills, compares patterns against live docs, tracks lineage to databricks/databricks-agent-skills (the single upstream registry), and reports drift. Use when auditing skill currency, verifying skills again...Votes: 0GitHub stars: 6
- 03 Appkit DeployDeploy 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, ship the app, or troubleshoot a failed deploy. Triggers on "deploy app", "push to databricks", "ship app", "deploy appkit", "databricks apps deploy", "fix deploy error", "app won't start".Votes: 0GitHub stars: 6
- Sp Display Device`get_display_settings` / `set_display_settings`Votes: 0GitHub stars: 7
- Pc Dev Environment`get_host_environment`、`pc_launch_replay`、`pc_launch_plotjuggler`、`git_*`、`run_scons_build`Votes: 0GitHub stars: 7
- Konik Vs Comma帮助用户在 **Konik** 与 **Comma Connect** 之间选型与配对。Votes: 0GitHub stars: 7
- Github Runner适用于 **comma three** 自建 Actions Runner：编译 `master-c3` → 发布 `master-c3-prebuilt`，以及 GUI / Param 启停 Runner。 > 事实来源：`ai/docs/GITHUB_RUNNER.md`、`release/ci/README.md`Votes: 0GitHub stars: 7