aidentity / index

google/skills · Apache-2.0

datalineage-bigquery-asset-impact-analysis

Analyzes the downstream impact (blast radius) when a BigQuery table or view is broken, stale, or modified. Identifies all downstream tables, dashboards, and processes that will be affected. Use when: - Performing a blast radius or impact analysis for a BigQuery table or view. - Assessing the consequences of modifying, deleting, or pausing updates to a BigQuery asset. - Identifying downstream dependencies (tables, dashboards, processes) of a BigQuery asset. Don't use for: - General BigQuery querying or data analysis (use BigQuery-related tools instead). - Non-BigQuery assets (e.g., Cloud Storage files) unless they are part of the BigQuery lineage. - Creating or modifying lineage links directly.

cloud stable

Contents

BigQuery Asset Impact Analysis

This skill guides the agent in performing a downstream impact analysis (blast

radius assessment) when a BigQuery table or view is reported as broken, stale,

missing, or when a user is planning maintenance and wants to know the

consequences of modifying or pausing updates to an asset.

It relies primarily on the Google Cloud Data Lineage (Knowledge Catalog) MCP Server

to discover relationships between assets.

Prerequisites

This skill requires access to the Google Cloud Data Lineage API and an active

client connection to the Data Lineage MCP Server. For detailed connection

configurations and tool schemas, refer to [MCP Usage](references/mcp-usage.md).

Analysis Workflow

1. Resolve the Asset's Fully Qualified Name (FQN)
  • Ensure you have the correct FQN format for the BigQuery asset:
  • Format: bigquery:{project_id}.{dataset_id}.{table_or_view_id}
  • Example: bigquery:my-prod-project.analytics.orders
2. Determine Locations and Parent Path

Identify the locations to search and construct the Data Lineage API request:

  • Discover Asset Location: Run the command `bq show --format=json

{project_id}:{dataset_id} and extract the location` field (e.g.,

us-central1 or us). If location discovery fails due to permissions or

missing tools, prompt the user for the dataset's location.

  • Set Parent Path: Set the parent path using the project ID and the

MCP server's location. Consult the DataLineageServer tool definition

to find the configured region or location (e.g., us). The format is:

projects/{project_id}/locations/{mcp_server_location}.

  • Configure Search Scope: Include the discovered asset location in the

locations array of the payload (e.g., ["us-central1"] or `["us",

"us-central1"]`).

3. Retrieve the Downstream Lineage Graph

Call the DataLineageServer:search_lineage tool to fetch downstream

relationships.

  • Direction: Set to DOWNSTREAM.
  • Search Parameters: Use max_depth = 10 and max_process_per_link = 5

as robust defaults.

4. Identify the Blast Radius

Traverse the returned lineage links to build the impact graph:

  • Affected Assets: The target of each link represents a downstream asset

that depends on your source asset.

  • Transform Processes: Inspect the processes field on each link. This

identifies the ETL pipelines, BigQuery Views, or Scheduled Queries that

propagate the data.

  • Direct vs. Indirect Impact:
  • Direct Impact (Depth 1): Assets directly consuming the source asset.

If a link has dependency_type: EXACT_COPY, mark the target as

"Directly Stale / Identical Copy".

  • Indirect Impact (Depth > 1): Assets further down the stream that

will experience cascading stale data or failures.

5. Summarize and Format the Output

Present your findings clearly to the user using the following structure:

  1. Executive Summary: State the total number of downstream assets affected

and the maximum depth of the impact.

  1. Critical Path: Highlight high-priority downstream assets (e.g., assets

containing "prod", "dashboard", "reporting", or "master" in their names).

  1. Blast Radius Table: A clean Markdown table listing the dependencies. You

MUST include all columns:

Downstream AssetTransform ProcessDepthImpact Type
bigquery:project.dataset.tableprojects/p/locations/l/processes/proc1Direct
bigquery:project.dataset.viewprojects/p/locations/l/processes/view2Indirect
  1. Analysis Metadata: Provide transparency on the parameters and boundaries

of your search so the user can choose to expand them:

  • Locations Searched: {list_of_locations_queried}
  • Parent Location: {parent_path}
  • Depth Limit: {max_depth}
  • Process per Link Limit: {max_process_per_link}
  • Tip for User: Let the user know they can request to rerun the analysis

with expanded locations or larger depth limits.

Crucial Constraints & Guardrails

  1. Interpret Empty Responses Correctly:
  • If the lineage response is empty, immediately assume that no

dependencies exist in the queried locations and report this to the

user.

  1. Strictly Banned Bypasses:
  • Exclusively retrieve downstream relationships using the

DataLineageServer:search_lineage tool.

  1. Verify Asset Existence First:
  • If bq show indicates the source table does not exist, stop and report

this directly to the user. Do not attempt to guess alternative table

names unless the user explicitly instructs you to do so.

  1. No Output Shortcutting or Hallucinated Artifacts:
  • Present the complete downstream blast radius table directly in your

final response. Avoid telling the user you have created a separate

Markdown file or artifact containing the details unless you have

explicitly executed file-writing tools to create it.

Reference Directory

  • [MCP Usage](references/mcp-usage.md): Using the Google Cloud Data Lineage

remote MCP server and tool preferences.

External Documentation

Provenance

Authored by Google LLC in google/skills. The content is mirrored here under its upstream license.

Related

agent-platform-alert-configuration Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. Don't use for standard infrastructure monitoring unrelated to AI agents, or when the agent is not instrumented with OpenTelemetry (for Reliability, Cost, Safety, Security alerts). NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (such as Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI deployments. google/skills Apache-2.0 agent-platform-deploy Deploy open models or custom weights from Model Garden to Agent Platform endpoints, check the status of an in-progress deployment operation, or clean up resources by undeploying models and deleting endpoints. Use when asked to actively deploy a model, list the Model Garden CATALOG of available models, check if a specific model is deployable (`gcloud ai model-garden models list-deployment-config`), query deployment cost, troubleshoot deployment errors (like quota limits), or undeploy/clean up endpoints. Also use when copying and deploying a 1P Tuned Model. Don't use for pure listing/discovery questions of the form "is X deployed?", "list my endpoints", or "which regions have models running?" — for those use `agent-platform-endpoint-management`. Don't use for public Vertex AI deployments (use the `vertex-deploy` skill) or for running model evaluations (use the `agent-platform-eval-flywheel` skill). google/skills Apache-2.0 agent-platform-endpoint-management Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model evaluations. google/skills Apache-2.0 agent-platform-eval-flywheel Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology. Use when evaluating an agent or model, building an eval dataset, picking or writing evaluation metrics, analyzing failures, comparing results before and after a fix, or when guidance is needed on Agent Platform eval methodology — including dataset schema, LLM-as-judge scoring, and common failure causes. For fine-tuning, use agent-platform-tuning. For general production deployment, use agent-platform-deploy. google/skills Apache-2.0 agent-platform-inference Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations. google/skills Apache-2.0