🎯Skills26
Manages Apache Airflow operations via the af CLI (installed via uvx from astro-airflow-mcp), including listing, testing, running, and debugging DAGs, viewing task logs, checking connections and variables, and monitoring system health. Supports multi-instance configuration with auto-discovery of Astro and local deployments.
Queries data warehouses to answer business questions by executing SQL, finding tables, and retrieving precise metrics and trends.
Guides developers in creating robust Apache Airflow DAGs using best practices and MCP tools.
Systematically diagnoses Airflow DAG failures by performing deep root cause analysis, identifying error sources, and providing structured prevention recommendations.
Guides users through migrating Apache Airflow 2.x projects to Airflow 3.x, addressing code changes, imports, operators, and compatibility issues.
Triggers and monitors Airflow DAG runs, automatically waiting for completion and providing immediate feedback on success or failure.
Traces data origins by investigating DAGs, source tables, and external systems to map the complete upstream lineage of a data asset.
Traces downstream data dependencies to reveal potential impacts and risks when modifying tables or data pipelines.
Data profiling agent skills for deep-dive table analysis including metadata inspection, column-level statistics, and data quality assessment using SQL queries.
Skill for managing local Airflow environments with Astro CLI, covering start/stop/restart, log viewing, container troubleshooting, and environment issue fixing
Verifies data freshness by identifying timestamp columns, checking last update times, and assessing data currency across tables.
Skill for initializing and configuring Astro/Airflow projects with Astro CLI, covering project structure, dependency setup, and connection/variable configuration
Annotates Airflow tasks with data lineage by specifying input and output datasets using inlets and outlets for operators without built-in OpenLineage extraction.
Captures lineage details for Airflow operators by creating custom OpenLineage extractors for unsupported or third-party operators.
Implementation checklist for turning a dbt Core project into an Airflow DAG or TaskGroup using Astronomer Cosmos 1.11+. Covers parsing strategies (manifest, dbt_ls, automatic), execution modes, profile configuration, and Airflow 3.x compatibility.
Implements human-in-the-loop workflows in Apache Airflow 3.1+ using deferrable HITL operators. Covers ApprovalOperator for approve/reject gates, HITLOperator for option selection with forms, HITLBranchOperator for human-driven task branching, and HITLEntryOperator for form submissions.
Implementation checklist for running dbt Fusion projects with Astronomer Cosmos 1.11+. Covers Fusion-specific constraints including LOCAL execution mode only, Snowflake and Databricks warehouse support, binary installation, and profile configuration.
Data profiling agent skills for deep-dive table analysis including metadata inspection, column-level statistics, and data quality assessment using SQL queries.
Provides deployment guidance for Apache Airflow, part of an AI agent tooling suite for data engineering workflows that includes an MCP server and CLI tool.
A troubleshooting astro deployments skill from Astronomer agents for Apache Airflow workflow orchestration.
A managing astro deployments skill from Astronomer agents for Apache Airflow workflow orchestration.
Initializes warehouse schema discovery by generating a comprehensive .astro/warehouse.md file with table metadata for instant data lookups.
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