先看看示例
作者提供的示例，实际效果取决于你的输入和使用工具。
输入
编辑任务：为订单宽表生成血缘摘要，说明上游来源系统与下游消费方，并指出可能影响数据质量的环节。
输出
预期交付：Markdown 报告：推断的流程类型、系统概览及上下游数据流说明。（编辑说明，非实际运行结果）
方法说明
---
name: datalineage-summary
metadata:
version: "1.0.2"
category: BigDataAndAnalytics
description: >-
Summarizes data lineage graphs on Google Cloud to help users debug data quality issues and understand data provenance for BigQuery and Cloud Storage.
Use when summarizing upstream and downstream data flows, and presenting complex lineage data as an intuitive Markdown report.
Don't use for generic BigQuery queries, editing lineage relationships, or downstream deprecation.
Don't use for downstream blast-radius impact analysis (use datalineage-bigquery-asset-impact-analysis skill instead).
---
# Data Lineage Summary
This skill guides the agent in investigating and summarizing the Data Lineage
graph for a specific focal asset (Table-Level Lineage) or specific fields
(Column-Level Lineage). It provides an intuitive left-to-right walkthrough of
how data enters and leaves the asset, abstracting away complex node and link
details into plain English.
## Attribution
**MUST READ**: Before executing any `gcloud`, REST API, or MCP command, you
MUST read [Usage Attribution](references/attribution.md). For commands executed
directly in the terminal (never in generated scripts, Terraform, or user code),
prefix `gcloud` with
`CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:datalineage-summary)"`
and append `agent-skills/1.0 (skill:datalineage-summary)` to `User-Agent` headers.
## Prerequisites
This skill relies on the **data lineage (Knowledge Catalog) MCP Server** on
Google Cloud for graph traversal. Ensure you can run `search_lineage` queries in
both upstream and downstream directions. For detailed connection configurations
and tool schemas, refer to [MCP Usage](references/mcp-usage.md).
## Workflow Logic
### 1. Get Lineage
Fetch the lineage graph in both directions from the focal point (both upstream
and downstream) by making *two separate calls* to the MCP tool: one with
`"direction": "UPSTREAM"` and another with `"direction": "DOWNSTREAM"`.
* **Location Strategy**: You **MUST** use the `read_url` tool to fetch the
comprehensive list of locations dynamically from the provided
[Knowledge Catalog Locations](https://docs.cloud.google.com/dataplex/docs/locations.md.txt)
link. To ensure cross-regional lineage is not missed, always verify the
current list of Google Cloud regions using this link before populating the
`locations` array. You **MUST** populate the `locations` array with all
supported physical regions fetched from this link. You may optionally
additionally determine the asset's specific active region (using `bq show`
or `gcloud storage ls`).
* **Search Parameters**: Use `maxDepth = 10`, `maxResults = 5000` and
`maxProcessPerLink = 10` as robust defaults when calling `search_lineage`.
For example, a DOWNSTREAM call should be formatted like this (expanding the
`locations` array as needed):
```json
{
"parent": "projects/project_id/locations/us",
"locations": [
"us",
"us-central1",
"us-east1",
"us-west1",
"europe-west1",
"asia-northeast1"
],
"rootCriteria": {
"entities": {
"entities": [
{
"fullyQualifiedName": "bigquery:project.dataset.table"
}
]
}
},
"direction": "DOWNSTREAM",
"limits": {
"maxDepth": 10,
"maxResults": 5000,
"maxProcessPerLink": 10
}
}
```
Ensure you make a similar call with `"direction": "UPSTREAM"` to fetch the
upstream lineage.
* **Column-Level Lineage (CLL)**: The `search_lineage` tool can find all
Column-Level Lineage (CLL) by configuring the `field` array. If Table-Level
Lineage (TLL) is requested, configure the call to get CLL links along with
the TLL links by exploiting the `"*"` wildcard. For example:
```json
"rootCriteria": {
"entities": {
"entities": [
{
"fullyQualifiedName": "bigquery:project.dataset.table",
"field": [
"*"
]
}
]
}
}
```
If evaluating a specific column, replace `"*"` with the specific column name
(e.g., `"efficiency_score"`).
### 2. Summarize
Generate the summary using the prompt guidelines below.
* **Persona**: Act as an expert Data Lineage Analyst generating a concise,
easy-to-understand left-to-right walkthrough of the data flow.
* **Structure & Flow**: Start immediately with the summary text, structured as
follows:
* **Overall Flow Type**: State the inferred workflow type and data domain
(e.g., "This appears to be a Feature Engineering workflow...").
* **Systems Overview**: List the primary systems involved up front. If the
request is for Column-Level Lineage, you MUST explicitly declare that
the scope of the analysis is limited to the specified field up front.
* **Upstream Lineage**: Use the exact bold header `**Upstream Lineage:**`.
Narrative must detail how data arrives at the focal asset, mentioning
key source systems, projects, and processing tasks (e.g., Spark on
Dataproc).
* **Downstream Lineage**: Use the exact bold header `**Downstream
Lineage:**`. Detail where data goes from the focal asset to final
consumer systems.
* **Analysis Metadata**: Display the parameters used for the API call to
provide transparency on the boundaries of the summary. The output must
contain:
* **Locations Searched**: `{list_of_locations_queried}`
* **Parent Location**: `{parent_path}`
* **Depth Limit**: `{maxDepth}`
* **Process per Link Limit**: `{maxProcessPerLink}`
* **Tip for User**: A prompt suggesting they can ask to rerun with
expanded locations (if not all were used) or depth.
* **Granularity Constraints**:
* Prioritize flows between Systems, Projects, and Datasets over individual
files/tables.
* You MUST explicitly list specific asset names (e.g., source tables,
intermediate views, consumer tables) if there are fewer than 5. Do not
just summarize counts if there are fewer than 5; name them explicitly.
Otherwise, if 5 or more, aggregate them by count (e.g., "5 Cloud
Storage buckets").
* Only mention counts for *ultimate sources*, *final consumers*, and
*total assets*.
* Do not repeat project names redundantly for every dataset if only one
project is involved.
* **Tone**: Avoid jargon and generic phrases like "There are distinct factual
points." Be direct and clear. The final output is Markdown.
### 3. Return the Summary
Return the final summarized output back to the user.
## External Documentation
- [Knowledge Catalog Data Lineage Documentation](https://docs.cloud.google.com/dataplex/docs/about-data-lineage.md.txt)
- [Use the Data Lineage MCP server](https://docs.cloud.google.com/dataplex/docs/use-lineage-mcp.md.txt)
- [Knowledge Catalog Data Lineage API Reference](https://docs.cloud.google.com/dataplex/docs/reference/data-lineage/rest.md.txt)