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Dataplex And Bigquery Governance

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vaquarkhan
dataplex-and-bigquery-governance

Guides agents through GCP-native data governance workflows with Dataplex and BigQuery. Use when designing lakes, zones, policy tags, metadata quality, lineage, discovery, and governed publishing across Cloud Storage, BigQuery, Dataflow, Dataproc, and Google Cloud analytics platforms.

Overview

Publishervaquarkhan
Repositorydata-engineering-agent-skills
Skill namedataplex-and-bigquery-governance
Stars
60
Forks
8
Bundled files
Instructions only
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by vaquarkhan on GitHub. Read the source before you install it.

Installation

Install the Dataplex And Bigquery Governance AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/vaquarkhan/data-engineering-agent-skills/tree/main/skills/dataplex-and-bigquery-governance
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/vaquarkhan/data-engineering-agent-skills.git /tmp/data-engineering-agent-skills
mkdir -p .claude/skills
cp -r /tmp/data-engineering-agent-skills/skills/dataplex-and-bigquery-governance .claude/skills/dataplex-and-bigquery-governance
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dataplex And Bigquery Governance in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Dataplex And Bigquery Governance on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Dataplex And Bigquery Governance is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Dataplex And BigQuery Governance

Overview

Use this skill when governance on GCP is centered on Dataplex, BigQuery, and related Google Cloud metadata controls. It helps agents define governance zones, policy-tag boundaries, lineage expectations, and trusted publishing patterns for warehouse and lake platforms.

When to Use

  • designing Dataplex lakes, zones, or governance domains
  • defining BigQuery policy tags and governed publish boundaries
  • improving metadata quality, lineage, and discovery on GCP
  • aligning warehouse and lake governance across Cloud Storage, BigQuery, Dataflow, and Dataproc
  • making governed analytics delivery work with regional or regulated-data controls

Do not treat BigQuery governance as only a permissions problem. Trusted publishing also needs metadata, ownership, and policy boundaries.

Workflow

  1. Define the governance boundary. Decide:

    • lakes and zones
    • datasets and domains
    • producer versus consumer boundaries
    • ownership expectations

  2. Define metadata and policy controls. Include:

    • policy tags
    • classifications
    • lineage coverage
    • discovery metadata
    • trusted versus exploratory asset signaling

  3. Align lake and warehouse governance. Make explicit how Cloud Storage, BigQuery, and processing services fit the same control model.

  4. Design governed publish behavior. Require:

    • clear serving boundaries
    • ownership visibility
    • release or validation evidence where needed
    • rules for schema and policy change

  5. Validate day-2 operations. Review whether onboarding, schema evolution, and new domains remain manageable under the governance model.

Common Rationalizations

RationalizationReality
"Policy tags are the whole governance design."Tags help, but they do not replace ownership, zone design, trusted publishing, or metadata quality.
"Dataplex is only for lake governance."Many teams need a joined governance model across storage, warehouse, and processing surfaces.
"Discovery will happen automatically once assets are scanned."Useful discovery requires curated metadata and trust signals.

Red Flags

  • lake, dataset, and domain boundaries are inconsistent
  • policy tags exist without ownership or publish context
  • warehouse and lake governance behave differently with no clear rule
  • certification or trusted-asset behavior is missing
  • schema and policy changes are operationally unclear

Verification

  • Governance boundaries are explicit across lake and warehouse surfaces
  • Policy tags, metadata, and lineage expectations are defined
  • Publish behavior is governed and reviewable
  • Ownership and discovery expectations are visible
  • Day-2 operations fit the governance design

Frequently asked questions

What does the Dataplex And Bigquery Governance AI skill do?

Guides agents through GCP-native data governance workflows with Dataplex and BigQuery. Use when designing lakes, zones, policy tags, metadata quality, lineage, discovery, and governed publishing across Cloud Storage, BigQuery, Dataflow, Dataproc, and Google Cloud analytics platforms.

Why use Dataplex And Bigquery Governance on TypingMind?

Because you install it once and use it with any model. Dataplex And Bigquery Governance is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Dataplex And Bigquery Governance in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vaquarkhan/data-engineering-agent-skills/tree/main/skills/dataplex-and-bigquery-governance. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Dataplex And Bigquery Governance?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Dataplex And Bigquery Governance?

As many as you like. As long as a model supports skills, you can use Dataplex And Bigquery Governance with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Dataplex And Bigquery Governance AI skill free?

Yes. It is published on GitHub by vaquarkhan under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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