Astronomer Agents: An AI Agent Toolset for Data Engineering Workflows

Astronomer Agents is an AI agent toolset for data engineering workflows, designed to address challenges such as data pipeline authoring and maintenance, data quality issue localization, and data lineage tracking. Its core components include an MCP server, CLI tools, and over 20 professional skills. It supports mainstream AI coding tools like Claude Code and Cursor, enabling automation of DAG authoring, data warehouse analysis, data lineage tracking, etc., to help data teams improve efficiency.

In modern data-driven enterprises, data engineering teams face many challenges: data pipeline authoring and maintenance are time-consuming and labor-intensive, data quality issues are hard to locate quickly, cross-system data lineage relationships are difficult to track, and data warehouse schema management is cumbersome. Traditional workflows rely on manual operations, which are inefficient and error-prone. The Astronomer Agents project was created to solve these problems by deeply integrating AI capabilities into data engineering workflows.

Astronomer Agents consists of three core components:

1. MCP Server (astro-airflow-mcp)：Infrastructure layer, integrated with Airflow REST API, exposing tools for AI clients to query DAG status, trigger tasks, etc. It is compatible with clients like Claude Desktop and VS Code.
2. CLI Tool (af)：Terminal interaction tool that supports commands like af health and af dags list for quick Airflow operations without the need for a web interface.
3. Professional Skills Set (Skills)：Over 20 professional skills, installed via the skills.sh framework, which can integrate with more than 25 AI coding agents, covering various data engineering tasks.

The skill system is divided into four categories:

• Data Warehouse Analysis: warehouse-init (initialize schema), analyzing-data (SQL analysis), profiling-tables (table analysis), checking-freshness (data timeliness check).
• Data Lineage: tracing-downstream-lineage (downstream impact analysis), tracing-upstream-lineage (upstream source tracing), etc., helping to build a complete lineage graph.
• Airflow Development: setting-up-astro-project (project initialization), authoring-dags (DAG authoring), testing-dags (testing), etc., covering the entire DAG lifecycle.
• dbt Integration: using-dbt-for-analytics-engineering (dbt model building), running-dbt-commands (command execution), etc., enabling seamless integration between Airflow and dbt.

Installation Methods:

• General skills: npx skills add astronomer/agents --skill '*'
• Claude Code plugin: claude plugin marketplace add astronomer/agents etc.
• MCP server: uvx astro-airflow-mcp --transport stdio (manual configuration) Data Warehouse Connection: Define connections in ~/.astro/agents/warehouse.yml (supports Snowflake, PostgreSQL, etc.). CLI Tools: Commands like af health (system health) and af dags list (list DAGs) can be used; aliases can be set for simplified usage.

Supports multiple scenarios:

1. Exploratory Data Analysis: Analysts use the analyzing-data skill to automatically query the warehouse, generate SQL, and explain results.
2. DAG Development and Debugging: Engineers use authoring-dags to write code, testing-dags for local testing, and debugging-dags to diagnose issues.
3. Data Lineage Analysis: Before modifying a core table, use tracing-downstream-lineage to analyze the scope of impact.
4. Data Quality Monitoring: Regularly use checking-freshness and profiling-tables to generate quality reports.

Open Source Integration: Compatible with Airflow 2.x/3.x, dbt Core/Cloud, OpenLineage standards, and supports mainstream platforms like Snowflake and Databricks. Future Outlook: More intelligent automatic DAG generation, automated data quality monitoring, powerful natural language interfaces, cross-system intelligent data discovery and cataloging—helping teams free themselves from repetitive work and focus on innovation.