datalineage-summary

datalineage-summary is a skill for Claude Code, Codex from hamzabellouch/agent-skills. It costs 92 tokens per session (1,541 once invoked), scanned A, a copy of datalineage-summary, MIT.

A reporting skill that summarizes Google Cloud Data Lineage graphs, which show where BigQuery and Cloud Storage data comes from and where it goes.

In plain words
What is it for?
Use it to describe upstream and downstream flows for tables or fields in BigQuery and Cloud Storage.
Why use it?
It makes complex data flows easier to understand when debugging data quality or tracing data provenance.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to describe upstream and downstream flows for tables or fields in BigQuery and Cloud Storage.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/hamzabellouch/agent-skills/datalineage-summary
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add hamzabellouch/agent-skills --skill datalineage-summary
Clone the repo
git clone --depth 1 https://github.com/hamzabellouch/agent-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for datalineage-summary

README.md
[![agentmods](https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/datalineage-summary/github.svg)](https://agentmods.dev/skills/hamzabellouch/agent-skills/datalineage-summary)
Your own site
<a href="https://agentmods.dev/skills/hamzabellouch/agent-skills/datalineage-summary"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/datalineage-summary/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for datalineage-summary

Your own site · 80×15
<a href="https://agentmods.dev/skills/hamzabellouch/agent-skills/datalineage-summary"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/datalineage-summary.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 92 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,541 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00092 $0.01541
Opus 5 $0.00046 $0.00771
Sonnet 5 $0.00018 $0.00308
Haiku 4.5 $0.00009 $0.00154

Measured 9d ago against content hash f8a8bcb0d3b9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

datalineage-summary scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to datalineage-summary — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

Google Cloud and GKE/datalineage-summary/SKILL.md · 157 lines

How it starts

The opening of the file, as written. The whole thing — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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):

    {
      "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
      }
    }
    

Read the full file on GitHub · 157 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 157 lines · 92 tokens per session scan A f8a8bcb0d3b9

Subscribe to this mod's changes

datalineage-summary is a skill published in the GitHub repository hamzabellouch/agent-skills (4 stars, last pushed 1mo ago), licensed MIT. It adds 92 tokens to every session and 1,541 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to datalineage-summary, differing in 0 lines, and is treated as a copy.

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