tracing-upstream-lineage

Trace upstream data lineage by identifying sources and producing DAGs.

419|61|Updated Jan 13, 2026
One-click install
npx skills add https://github.com/astronomer/agents --skill tracing-upstream-lineage
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: tracing-upstream-lineage
Source: https://github.com/astronomer/agents/tree/main/skills/tracing-upstream-lineage
Command: npx skills add https://github.com/astronomer/agents --skill tracing-upstream-lineage

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps data teams trace upstream data lineage by identifying where tables and columns originate, and which DAGs or external sources populate them.

Core Features & Use Cases

  • Lineage discovery: Identify target's upstream sources and producing DAGs.
  • Provenance reasoning: Determine source types (tables, external APIs, files) and their relationships.
  • Auditing & governance: Provide traceable lineage for data quality checks.
  • Use Case: Audit a table to answer what feeds this table?

Quick Start

Run the lineage trace for a target table or column, e.g., 'Trace lineage for analytics.orders'.

Frequently Asked Questions about tracing-upstream-lineage

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I trace upstream data lineage for a specific table in Airflow?

Tracing upstream data lineage involves recursively inspecting Airflow DAG definitions and parsing SQL transformation hints to identify source origins. It outputs a concise lineage narrative mapping dependencies across DAGs and external systems.

What is data provenance and how does it apply to column lineage?

Data provenance identifies the origins and relationships of data elements. This skill determines source types like tables or external APIs and maps column provenance by parsing transformation hints within DAGs to support governance.

Can I audit data origins for a table using Airflow DAGs?

Yes, you can audit data origins by running a lineage trace for a target table. The skill inspects DAG definitions and external sources to provide traceable upstream lineage for data quality checks and governance.

What's the best way to map dependencies across DAGs and external systems?

The best way to map dependencies is to trace lineage for a target dataset. This approach recursively inspects DAG definitions and parses SQL hints to present a concise narrative of upstream sources and producing DAGs.

Do I need to parse SQL manually to find upstream sources for my dataset?

No, you do not need to parse SQL manually. The skill automatically parses SQL and transformation hints within DAG definitions to identify upstream sources and produce a concise lineage narrative for governance.