How to install tracing-upstream-lineage
npx skills add https://github.com/astronomer/agents --skill tracing-upstream-lineageFull instructions (SKILL.md)
Source of truth, from astronomer/agents.
name: tracing-upstream-lineage description: Trace upstream data lineage. Use when the user asks where data comes from, what feeds a table, upstream dependencies, data sources, or needs to understand data origins.
Upstream Lineage: Sources
Trace the origins of data - answer "Where does this data come from?"
Lineage Investigation
Step 1: Identify the Target Type
Determine what we're tracing:
- Table: Trace what populates this table
- Column: Trace where this specific column comes from
- DAG: Trace what data sources this DAG reads from
Step 2: Find the Producing DAG
Tables are typically populated by Airflow DAGs. Find the connection:
-
Search DAGs by name: Use
af dags listand look for DAG names matching the table nameload_customers->customerstableetl_daily_orders->orderstable
-
Explore DAG source code: Use
af dags source <dag_id>to read the DAG definition- Look for INSERT, MERGE, CREATE TABLE statements
- Find the target table in the code
-
Check DAG tasks: Use
af tasks list <dag_id>to see what operations the DAG performs
On Astro
If you're running on Astro, the Lineage tab in the Astro UI provides visual lineage exploration across DAGs and datasets. Use it to quickly trace upstream dependencies without manually searching DAG source code.
On OSS Airflow
Use DAG source code and task logs to trace lineage (no built-in cross-DAG UI).
Step 3: Trace Data Sources
From the DAG code, identify source tables and systems:
SQL Sources (look for FROM clauses):
# In DAG code:
SELECT * FROM source_schema.source_table # <- This is an upstream source
External Sources (look for connection references):
S3Operator-> S3 bucket sourcePostgresOperator-> Postgres database sourceSalesforceOperator-> Salesforce API sourceHttpOperator-> REST API source
File Sources:
- CSV/Parquet files in object storage
- SFTP drops
- Local file paths
Step 4: Build the Lineage Chain
Recursively trace each source:
TARGET: analytics.orders_daily
^
+-- DAG: etl_daily_orders
^
+-- SOURCE: raw.orders (table)
| ^
| +-- DAG: ingest_orders
| ^
| +-- SOURCE: Salesforce API (external)
|
+-- SOURCE: dim.customers (table)
^
+-- DAG: load_customers
^
+-- SOURCE: PostgreSQL (external DB)
Step 5: Check Source Health
For each upstream source:
- Tables: Check freshness with the checking-freshness skill
- DAGs: Check recent run status with
af dags stats - External systems: Note connection info from DAG code
Lineage for Columns
When tracing a specific column:
- Find the column in the target table schema
- Search DAG source code for references to that column name
- Trace through transformations:
- Direct mappings:
source.col AS target_col - Transformations:
COALESCE(a.col, b.col) AS target_col - Aggregations:
SUM(detail.amount) AS total_amount
- Direct mappings:
Output: Lineage Report
Summary
One-line answer: "This table is populated by DAG X from sources Y and Z"
Lineage Diagram
[Salesforce] --> [raw.opportunities] --> [stg.opportunities] --> [fct.sales]
| |
DAG: ingest_sfdc DAG: transform_sales
Source Details
| Source | Type | Connection | Freshness | Owner |
|---|---|---|---|---|
| raw.orders | Table | Internal | 2h ago | data-team |
| Salesforce | API | salesforce_conn | Real-time | sales-ops |
Transformation Chain
Describe how data flows and transforms:
- Raw data lands in
raw.ordersvia Salesforce API sync - DAG
transform_orderscleans and dedupes intostg.orders - DAG
build_order_factsjoins with dimensions intofct.orders
Data Quality Implications
- Single points of failure?
- Stale upstream sources?
- Complex transformation chains that could break?
Related Skills
- Check source freshness: checking-freshness skill
- Debug source DAG: debugging-dags skill
- Trace downstream impacts: tracing-downstream-lineage skill
- Add manual lineage annotations: annotating-task-lineage skill
- Build custom lineage extractors: creating-openlineage-extractors skill
Related skills
More from astronomer/agents and the wider catalog.
analyzing-data
Queries data warehouse and answers business questions about data. Handles questions requiring database/warehouse queries including "who uses X", "how many Y", "show me Z", "find customers", "what is the count", data lookups, metrics, trends, or SQL analysis.
airflow
Queries, manages, and troubleshoots Apache Airflow using the af CLI. Covers listing DAGs, triggering runs, reading task logs, diagnosing failures, debugging DAG import errors, checking connections, variables, pools, and monitoring health. Also routes to sub-skills for writing DAGs, debugging, deploying, and migrating Airflow 2 to 3. Use when user mentions "Airflow", "DAG", "DAG run", "task log", "import error", "parse error", "broken DAG", or asks to "trigger a pipeline", "debug import errors", "check Airflow health", "list connections", "retry a run", or any Airflow operation. Do NOT use for warehouse/SQL analytics on Airflow metadata tables — use analyzing-data instead.
authoring-dags
Workflow and best practices for writing Apache Airflow DAGs. Use when the user wants to create a new DAG, write pipeline code, or asks about DAG patterns and conventions. For testing and debugging DAGs, see the testing-dags skill.
debugging-dags
Comprehensive DAG failure diagnosis and root cause analysis. Use for complex debugging requests requiring deep investigation like "diagnose and fix the pipeline", "full root cause analysis", "why is this failing and how to prevent it". For simple debugging ("why did dag fail", "show logs"), the airflow entrypoint skill handles it directly. This skill provides structured investigation and prevention recommendations.
migrating-airflow-2-to-3
Guide for migrating Apache Airflow 2.x projects to Airflow 3.x. Use when the user mentions Airflow 3 migration, upgrade, compatibility issues, breaking changes, or wants to modernize their Airflow codebase. If you detect Airflow 2.x code that needs migration, prompt the user and ask if they want you to help upgrade. Always load this skill as the first step for any migration-related request.
testing-dags
Complex DAG testing workflows with debugging and fixing cycles. Use for multi-step testing requests like "test this dag and fix it if it fails", "test and debug", "run the pipeline and troubleshoot issues". For simple test requests ("test dag", "run dag"), the airflow entrypoint skill handles it directly. This skill is for iterative test-debug-fix cycles.