# Datalineage Summary

> Resume grafos de Google Cloud Data Lineage para depurar problemas de calidad de datos y entender la procedencia de datos en BQ/GCS mediante reportes Markdown.

Fuente: https://skillsagentes.com/skills/google/skills/datalineage-summary
Markdown: https://skillsagentes.com/skills/google/skills/datalineage-summary.md

Repositorio: https://github.com/google/skills
Autor: google
Licencia: Apache-2.0
Actualizado: hace 3 meses
Coste de contexto: 117 tok instalada, 1.7k tok al activarse, 2k tok con todos los archivos del bundle
Bundle: 2 archivos, 8 KB
Permisos que pide: ninguno declarado

## Instalación

Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina.

```bash
# Claude Code
npx -y skills add google/skills --skill datalineage-summary --agent claude-code
# Cursor
npx -y skills add google/skills --skill datalineage-summary --agent cursor
# Codex
npx -y skills add google/skills --skill datalineage-summary --agent codex
# Gemini CLI
npx -y skills add google/skills --skill datalineage-summary --agent gemini
# Windsurf
npx -y skills add google/skills --skill datalineage-summary --agent windsurf
# Cline
npx -y skills add google/skills --skill datalineage-summary --agent cline
```

## Qué hace

- Consulta el grafo de Data Lineage de Google Cloud en dirección UPSTREAM y DOWNSTREAM para un activo o columna concreta
- Genera un reporte en Markdown con flujo de datos de izquierda a derecha, en lenguaje llano
- Determina dinámicamente la lista de regiones GCP para no perder lineage entre regiones
- Estructura el resumen con secciones Upstream Lineage, Downstream Lineage y metadatos del análisis

## Cuándo usarla

- Necesitas resumir flujos de datos upstream y downstream de una tabla o columna en BigQuery/GCS
- Quieres presentar datos de lineage complejos como un reporte Markdown intuitivo
- Estás depurando problemas de calidad de datos o entendiendo la procedencia de un dato

## Cuándo no

- Para consultas genéricas de BigQuery
- Para editar relaciones de lineage o gestionar deprecaciones downstream
- Para análisis de impacto de radio de explosión downstream (usar datalineage-bigquery-asset-impact-analysis)

## Qué la activa

- "Resume el lineage upstream y downstream de esta tabla de BigQuery"
- "Explícame de dónde viene y a dónde va el dato de esta columna"
- "Genera un reporte de procedencia de datos para este dataset de GCS"

## Antes de instalar

- Requiere el Google Cloud Data Lineage (Knowledge Catalog) MCP Server con capacidad de ejecutar consultas search_lineage en ambas direcciones.

## Archivos

- SKILL.md — 7 KB
- references/mcp-usage.md — 1 KB

## SKILL.md

Reproducido tal cual desde google/skills bajo Apache-2.0. Esta sección es el documento original y está en inglés.

# 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)

## Dónde encaja

- Categoría: [Datos y analítica](https://skillsagentes.com/categorias/datos-analitica.md) — Consulta, limpia y visualiza datos sin salir del agente.
- Creador: [google](https://skillsagentes.com/creators/google.md) — 138 skills en el directorio
- [Todas las skills](https://skillsagentes.com/skills.md)
- [Ranking de instalaciones](https://skillsagentes.com/ranking.md)

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- [Google Cloud Solution Architecture](https://skillsagentes.com/skills/google/skills/google-cloud-solution-architecture.md): Descubre requisitos de forma interactiva y diseña arquitecturas de solución multiproducto, planos de solución y recomendaciones de despliegue para cargas de trabajo complejas en Google Cloud.
- [Gke Basics](https://skillsagentes.com/skills/google/skills/gke-basics.md): Gestiona la provisión de clústeres GKE, credenciales, elección entre Autopilot y Standard, y despliegue de workloads en Google Kubernetes Engine.
- [Agent Platform Prompt Management](https://skillsagentes.com/skills/google/skills/agent-platform-prompt-management.md): Gestiona y orquesta prompts en Agent Platform: crear, listar, recuperar, versionar o eliminar prompts gestionados; no sirve para entrenamiento ni despliegue de modelos.
- [Agent Platform Tuning](https://skillsagentes.com/skills/google/skills/agent-platform-tuning.md): Ajuste fino de modelos en Agent Platform: para afinar modelos abiertos o Gemini. No usar para entrenar fuera de Agent Platform, desplegar a endpoints o gestionar endpoints de serving.

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