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Found 96 Skills
Develops data processing pipelines, integrations, and machine learning scenarios in SAP Data Intelligence Cloud. Use when building graphs/pipelines with operators, integrating ABAP/S4HANA systems, creating replication flows, developing ML scenarios with JupyterLab, or using Data Transformation Language functions. Covers Gen1/Gen2 operators, subengines (Python, Node.js, C++), structured data operators, and repository objects.
Expert guidance for working with Dagster and the dg CLI. ALWAYS use before doing any task that requires knowledge specific to Dagster, or that references assets, materialization, or data pipelines. Common tasks may include creating a new project, adding new definitions, understanding the current project structure, answering general questions about the codebase (finding asset, schedule, sensor, component or job definitions), debugging issues, or providing deep information about a specific Dagster concept.
Prefect Flow Builder - Auto-activating skill for Data Pipelines. Triggers on: prefect flow builder, prefect flow builder Part of the Data Pipelines skill category.
Data Quality Checker - Auto-activating skill for Data Pipelines. Triggers on: data quality checker, data quality checker Part of the Data Pipelines skill category.
Database development and operations workflow covering SQL, NoSQL, database design, migrations, optimization, and data engineering.
Build and deploy new Goldsky Turbo pipelines from scratch. Triggers on: 'build a pipeline', 'index X on Y chain', 'set up a pipeline', 'track transfers to postgres', or any request describing data to move from a chain/contract to a destination (postgres, clickhouse, kafka, s3, webhook). Covers the full workflow: requirements → dataset selection → YAML generation → validation → deploy. Not for debugging (use /turbo-doctor) or syntax lookups (use /turbo-pipelines).
Fix broken data scrapers and pipelines. Use when data acquisition fails, a scraper breaks, an API returns errors, or data format has changed. Also handles submitting upstream issues or PRs when the problem is in a dependency like soccerdata or kloppy.
Football data analytics — the single entry point. Use whenever the user mentions football data, xG, expected goals, match analysis, player stats, scouting, match reports, shot maps, passing networks, Premier League data, Champions League stats, scraping FBref/Understat/Transfermarkt, building football charts, or anything football analytics related. Routes to specialised sub-skills automatically. Also handles first-time setup and profile management.
Choose how and where to store football data. Use when the user asks about database choices, file formats, cloud storage, data pipelines, or how to organise their football data project. Also covers publishing and sharing outputs (Streamlit, Observable, GitHub Pages).
Astronomer integration. Manage data, records, and automate workflows. Use when the user wants to interact with Astronomer data.
RudderStack HTTP integration. Manage data, records, and automate workflows. Use when the user wants to interact with RudderStack HTTP data.
Google Cloud Dataflow integration. Manage data, records, and automate workflows. Use when the user wants to interact with Google Cloud Dataflow data.