Data & AI skills

Generate comprehensive BIM model validation reports. Check data quality, completeness, and compliance with standards.

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Browse and search the OpenConstructionERP cost database: classification tree, SQL and semantic search, autocomplete, certainty badges, and the resource catalog. Use when a user wants to explore cost data without building a BOQ yet.

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Analyze weather data impact on construction schedules. Predict weather delays, optimize work scheduling based on forecasts, and calculate weather-related risk factors for project planning.

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Automated safety compliance verification for construction sites. Check PPE usage, zone access, working at heights regulations, and generate compliance reports using rule-based and ML approaches.

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Apply machine learning for construction project risk assessment. Predict schedule delays, cost overruns, and safety incidents using historical data and project characteristics.

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Extract structured data from IFC (Industry Foundation Classes) files using IfcOpenShell. Parse BIM models, extract quantities, properties, spatial relationships, and export to various formats.

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Multi-project portfolio analytics dashboard. Aggregate KPIs across projects, track portfolio health, compare performance, and support executive decision-making.

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Material passports and circular construction: generate per-element material inventories from BOQ/BIM, mark reuse potential and recycled content, and prepare deconstruction data. Use for circular-economy and EU Taxonomy-aligned projects.

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Analyze construction labor productivity using data analytics. Track worker performance, identify inefficiencies, predict resource needs, and optimize crew allocation for maximum efficiency.

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Aggregate and analyze IoT sensor data from construction sites. Collect data from multiple sensor types, detect anomalies, and trigger alerts for safety and quality monitoring.

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Integrate and analyze telematics data from heavy construction equipment. Track location, utilization, fuel consumption, maintenance needs, and operator behavior.

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Analyze and predict construction change orders using ML. Classify change order types, predict costs and schedule impacts, identify patterns, and optimize approval workflows.

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Create interactive KPI dashboards for construction projects. Track schedule, cost, quality, and safety metrics in real-time.

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Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards.

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Generate automated daily progress reports from site data. Track work completed, labor hours, equipment usage, and weather conditions.

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Push records from apps, services, devices, or scripts directly into Unity Catalog Delta tables with Zerobus Ingest: SDKs (Python, TypeScript, Go, Java, Rust, C++, .NET), REST, OTLP, MQTT, Arrow Flight, or Apache Kafka-compatible producer APIs, with no message bus to run. Use to stream events, telemetry, logs, IoT data, CDC, or columnar batches into Databricks in near real time; connect existing collectors and log shippers (OpenTelemetry, Debezium, Vector, Telegraf, Cribl); replace a message bus that only feeds the lakehouse; or debug Zerobus auth, schema, throughput, or delivery issues.

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Databricks Vector Search endpoints and indexes for RAG and semantic search; covers index types, search modes, end-to-end RAG patterns

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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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Develop Lakeflow Spark Declarative Pipelines (formerly Delta Live Tables) on Databricks. Use when building batch or streaming data pipelines with Python or SQL, including Auto CDC from event streams or periodic complete snapshots. Invoke BEFORE starting implementation.

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Train ML models on Databricks. Use for: classification/regression/deep-learning (XGBoost, scikit-learn, LightGBM, PyTorch) with Optuna, @prod/@challenger aliases, batch scoring (spark_udf for plain models, fe.score_batch for feature-store-backed), custom PyFunc, custom ResponsesAgent (LangGraph + UC Function/Vector Search); UC feature tables + FeatureLookup + point-in-time joins + Lakebase online store; declarative Feature Views (create_feature, DeltaTableSource, RollingWindow/SlidingWindow/TumblingWindow, materialize_features, streaming Kafka features). NOT for: endpoint ops (databricks-model-serving), MLflow evaluation (databricks-mlflow-evaluation).

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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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Databricks Lakebase Postgres: projects, scaling, connectivity, Lakebase synced tables, and Data API. Use when asked about Lakebase databases, OLTP storage, or connecting apps to Postgres on Databricks.

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Create, manage, and query Databricks Genie Agents — curated, per-data natural-language agents (formerly Genie Spaces): build, export/import, migrate across workspaces, and ask questions of a *specific* Agent via the Conversation API. For general data questions or finding data across your workspace, use databricks-data-discovery (Genie One) instead.

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Execute code and manage compute on Databricks: run Python/Scala/SQL/R via serverless, classic, or interactive clusters, and create/resize/delete clusters and SQL warehouses.

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