Official agent skill

Datalineage Summary

Summarizes Google Cloud Data Lineage graphs to help users debug data quality issues and understand data provenance for BQ/GCS.

OfficialApache-2.0Auto-check passedData & Analytics

Install Datalineage Summary

skills CLI

$ npx skills add google/skills --skill datalineage-summary -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install google/skills datalineage-summary --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cloud/datalineage-summary .claude/skills/datalineage-summary && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "datalineage-summary" agent skill from https://github.com/google/skills/tree/main/skills/cloud/datalineage-summary into .claude/skills/datalineage-summary/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datalineage-summary", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/google/skills/tree/main/skills/cloud/datalineage-summary

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add google/skills --skill datalineage-summary -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install google/skills datalineage-summary --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/cloud/datalineage-summary .agents/skills/datalineage-summary && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "datalineage-summary" agent skill from https://github.com/google/skills/tree/main/skills/cloud/datalineage-summary into .agents/skills/datalineage-summary/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datalineage-summary", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add google/skills --skill datalineage-summary -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install google/skills datalineage-summary --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/cloud/datalineage-summary .cursor/skills/datalineage-summary && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "datalineage-summary" agent skill from https://github.com/google/skills/tree/main/skills/cloud/datalineage-summary into .cursor/skills/datalineage-summary/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datalineage-summary", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/google/skills.git --path skills/cloud/datalineage-summary

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add google/skills --skill datalineage-summary -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install google/skills datalineage-summary --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/cloud/datalineage-summary .gemini/skills/datalineage-summary && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "datalineage-summary" agent skill from https://github.com/google/skills/tree/main/skills/cloud/datalineage-summary into .gemini/skills/datalineage-summary/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datalineage-summary", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install google/skills datalineage-summary

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add google/skills --skill datalineage-summary -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/cloud/datalineage-summary .github/skills/datalineage-summary && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "datalineage-summary" agent skill from https://github.com/google/skills/tree/main/skills/cloud/datalineage-summary into .github/skills/datalineage-summary/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datalineage-summary", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add google/skills --skill datalineage-summary -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install google/skills datalineage-summary --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/google/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/cloud/datalineage-summary .opencode/skills/datalineage-summary && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "datalineage-summary" agent skill from https://github.com/google/skills/tree/main/skills/cloud/datalineage-summary into .opencode/skills/datalineage-summary/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "datalineage-summary", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: datalineage-summary
GitHub stars: 21k
Token cost: ~1.7k tokens
SKILL.md length: 682 words
Files: 2 (incl. references)
Skills in repo: 147
Repo updated: Oct 9, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Summarizes Google Cloud Data Lineage graphs to help users debug data quality issues and understand data provenance for BQ/GCS.

Works in 3 steps: Get Lineage → Summarize → Return the Summary
Summarizing upstream and downstream data flows
SKILL.md covers Prerequisites, Workflow Logic and External Documentation
Calls bq and gcloud

What it does

Datalineage Summary is an agent skill from google/skills, published by the product's own GitHub organization. Summarizes Google Cloud Data Lineage graphs to help users debug data quality issues and understand data provenance for BQ/GCS. 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).

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/mcp-usage.md`).

It sits in Data & Analytics, covering Data governance, Data warehousing and Data cleaning. It works with Google Cloud, Google BigQuery and Model Context Protocol. The repository describes itself as: Agent Skills for Google products and technologies. The licence is Apache-2.0.

When your agent uses it

Summarizing upstream and downstream data flows
Presenting complex lineage data as an intuitive Markdown report
Generic BigQuery queries
Editing lineage relationships

Example prompts

“Use the datalineage-summary skill to summariz Google Cloud Data Lineage graphs to help users debug data quality issues and understand data…”
“/datalineage-summary”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

Get Lineage
Summarize
Return the Summary

What it can do on your machine

Read from SKILL.md and the folder at commit 5120a76. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- bq
- gcloud
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
- docs.cloud.google.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Datalineage Summary loads about 1.7k tokens when it runs, and up to ~2k if it reads all its reference files. Until then it costs about 122 tokens; SKILL.md has 682 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~122
When it runs · the whole SKILL.md, loaded when a task matches: ~1.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from google/skills at commit 5120a76, republished under its Apache-2.0 licence (© google). 682 words, ~1,688 tokens.

Download SKILL.mdSave it as .claude/skills/datalineage-summary/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.

name: datalineage-summary
description: Summarizes Google Cloud Data Lineage graphs to help users debug data quality issues and understand data provenance for BQ/GCS. 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).
metadata.version: 1.0.0
metadata.category: BigDataAndAnalytics

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.

Prerequisites

This skill relies on the Google Cloud Data Lineage (Knowledge Catalog) MCP Server 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.

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 link. To ensure cross-regional lineage is not missed, always verify the current list of GCP 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").

Show full SKILL.md (343 more words)Show less

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 GCS 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

Files

SKILL.md and 1 other file (references) in skills/cloud/datalineage-summary of google/skills.

SKILL.md
references/mcp-usage.md

Open the folder on GitHubat commit 5120a76

Compare with similar skills

Datalineage Summary next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Datalineage Summary compared with similar skills
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Datalineage Summary this skillgoogle/skills	21k	—	~1.7k	Automated safety check: Pass	Apache-2.0	today
Imaging Data CommonsK-Dense-AI/scientific-agent-skills	48k	1 repos	~7.8k	Automated safety check: Pass	MIT	4 days ago
Ga4 Auditcognyai/claude-code-marketing-skills	104	—	~1.6k	Automated safety check: Pass	None	3 mo ago
Monte Carlo Context Detectionsickn33/agentic-awesome-skills	47k	1 repos	~2.6k	Automated safety check: Warn	MIT	today
Data Lineage TrackerDrchronx/ai-agent-research-starter-kit	137	—	~324	Automated safety check: Pass	Custom licence	4 mo ago
Finding Data Lake Assetsaws/agent-toolkit-for-aws	2.8k	—	~4.2k	Automated safety check: Warn	Apache-2.0	today

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Questions about Datalineage Summary

What does Datalineage Summary do?

Summarizes Google Cloud Data Lineage graphs to help users debug data quality issues and understand data provenance for BQ/GCS. Datalineage Summary is an agent skill from google/skills, published by the product's own GitHub organization. Summarizes Google Cloud Data Lineage graphs to help users debug data quality issues and understand data provenance for BQ/GCS.

When should I use Datalineage Summary?

Datalineage Summary fits situations like: summarizing upstream and downstream data flows; presenting complex lineage data as an intuitive Markdown report; generic BigQuery queries; editing lineage relationships.

How do I install Datalineage Summary in Claude Code?

Run `npx skills add google/skills --skill datalineage-summary -a claude-code`. Or copy the skill folder (skills/cloud/datalineage-summary in google/skills) into .claude/skills/datalineage-summary in your project. Claude Code loads it when a task matches its description.

How do I install Datalineage Summary in Codex?

Run `npx skills add google/skills --skill datalineage-summary -a codex`. Or copy the skill folder (skills/cloud/datalineage-summary in google/skills) into .agents/skills/datalineage-summary in your project. Codex loads it when a task matches its description.

Can I use Datalineage Summary in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add google/skills --skill datalineage-summary -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/datalineage-summary, .gemini/skills/datalineage-summary, .github/skills/datalineage-summary and .opencode/skills/datalineage-summary in your project.

What does Datalineage Summary need to run?

Going by SKILL.md and its folder, Datalineage Summary needs the command-line tools its instructions call (bq and gcloud).

Does Datalineage Summary access the network?

SKILL.md names 1 domain. As links in the text: docs.cloud.google.com. This is read from the text; nothing was executed.

Is Datalineage Summary safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Datalineage Summary use?

Datalineage Summary is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Datalineage Summary use?

About 1.7k tokens (SKILL.md is roughly 6.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 313 tokens, read only when the agent opens those files.

What are the alternatives to Datalineage Summary?

Skills that share tags, products or a category with Datalineage Summary: Imaging Data Commons (K-Dense-AI/scientific-agent-skills, 48k stars), Ga4 Audit (cognyai/claude-code-marketing-skills, 104 stars), Monte Carlo Context Detection (sickn33/agentic-awesome-skills, 47k stars) and Data Lineage Tracker (Drchronx/ai-agent-research-starter-kit, 137 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Datalineage Summary?

google (a GitHub organization, an official publisher) maintains it in google/skills, which has 21,069 GitHub stars. The repository holds 147 skills in this directory. The repository was last updated on October 9, 2026.

Source: google/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Datalineage Summary

Install Datalineage Summary

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Data Lineage Summary

Prerequisites

Workflow Logic

1. Get Lineage

2. Summarize

3. Return the Summary

External Documentation

Files

Compare with similar skills

Similar skills

Imaging Data Commons

Ga4 Audit

Monte Carlo Context Detection

Data Lineage Tracker

Finding Data Lake Assets

Erd Studio Setup

More from google/skills

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Google Cloud PAM Helper

Agent Platform Alert Configuration

Model Garden Deployment

Agent Platform Skill Registry

GCP Application Design Center Deploy

Questions about Datalineage Summary

Install Datalineage Summary

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Data Lineage Summary

Prerequisites

Workflow Logic

1. Get Lineage

2. Summarize

3. Return the Summary

External Documentation

Files

Compare with similar skills

Similar skills

More from google/skills

Related

Questions about Datalineage Summary