Create an operating checklist
Turn a recurring process into a clear checklist with ownership and exception handling.
5,475,879 AI resources indexedWorking toward the world’s largest AI resource index.
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.
---
name: tracing-upstream-lineage-558d4d0008c6
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 and answer "Where does this data come from?"
## Lineage Investigation
### Step 1: Identify the Target Type
Determine what we are tracing:
- Table
- Column
- DAG
### Step 2: Find the Producing DAG
1. List DAGs: use `list_active_dags` and `list_paused_dags`
2. Read DAG source: use `get_dag_source_code`
3. If a run exists, use `analyse_dag_latest_run` to see tasks and logs
### Step 3: Trace Data Sources
From the DAG code, identify source tables and systems:
- SQL sources in FROM or JOIN clauses
- External sources via operator hooks or connection IDs
- Files in object storage
Use `go_to_connections_view` to inspect connection metadata.
### Step 4: Build the Lineage Chain
Example:
```
TARGET: analytics.orders_daily
^
+-- DAG: etl_daily_orders
^
+-- SOURCE: raw.orders
|
+-- SOURCE: dim.customers
```
### Step 5: Check Source Health
- Use `get_dag_runs` or `get_dag_history` on upstream DAGs
- For logs, use `go_to_dag_log_view`
## Lineage for Columns
1. Find the column in the target table schema
2. Search DAG source for references
3. Trace transformations and mappings
## Output: Lineage Report
Include:
- Summary of sources
- Lineage diagram
- Source details (connections, freshness)
- Transformation chain
- Data quality implications
## Related Skills
- checking-freshness
- debugging-dags
- tracing-downstream-lineage
- annotating-task-lineage
- creating-openlineage-extractors
---
name: tracing-upstream-lineage-558d4d0008c6
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 and answer "Where does this data come from?"
## Lineage Investigation
### Step 1: Identify the Target Type
Determine what we are tracing:
- Table
- Column
- DAG
### Step 2: Find the Producing DAG
1. List DAGs: use `list_active_dags` and `list_paused_dags`
2. Read DAG source: use `get_dag_source_code`
3. If a run exists, use `analyse_dag_latest_run` to see tasks and logs
### Step 3: Trace Data Sources
From the DAG code, identify source tables and systems:
- SQL sources in FROM or JOIN clauses
- External sources via operator hooks or connection IDs
- Files in object storage
Use `go_to_connections_view` to inspect connection metadata.
### Step 4: Build the Lineage Chain
Example:
```
TARGET: analytics.orders_daily
^
+-- DAG: etl_daily_orders
^
+-- SOURCE: raw.orders
|
+-- SOURCE: dim.customers
```
### Step 5: Check Source Health
- Use `get_dag_runs` or `get_dag_history` on upstream DAGs
- For logs, use `go_to_dag_log_view`
## Lineage for Columns
1. Find the column in the target table schema
2. Search DAG source for references
3. Trace transformations and mappings
## Output: Lineage Report
Include:
- Summary of sources
- Lineage diagram
- Source details (connections, freshness)
- Transformation chain
- Data quality implications
## Related Skills
- checking-freshness
- debugging-dags
- tracing-downstream-lineage
- annotating-task-lineage
- creating-openlineage-extractors
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.