Bigquery And Dataform Platform Engineering

Community

vaquarkhan

bigquery-and-dataform-platform-engineering

Guides agents through BigQuery- and Dataform-centered data engineering workflows. Use when designing BigQuery physical models, ingestion boundaries, Dataform transformation workflows, slot or cost controls, and platform decisions across BigQuery, Dataflow, Dataproc, and GCP orchestration services.

Overview

Publisher	vaquarkhan
Repository	`data-engineering-agent-skills`
Skill name	`bigquery-and-dataform-platform-engineering`
Stars	60
Forks	8
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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 Bigquery And Dataform Platform Engineering AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/vaquarkhan/data-engineering-agent-skills/tree/main/skills/bigquery-and-dataform-platform-engineering
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/vaquarkhan/data-engineering-agent-skills/tree/main/skills/bigquery-and-dataform-platform-engineering

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/bigquery-and-dataform-platform-engineering .claude/skills/bigquery-and-dataform-platform-engineering

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bigquery And Dataform Platform Engineering 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 Bigquery And Dataform Platform Engineering 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 Bigquery And Dataform Platform Engineering 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.

BigQuery And Dataform Platform Engineering

Overview

Use this skill when BigQuery is the center of gravity for analytics engineering and data delivery on GCP. It helps agents decide what should run in BigQuery, what belongs in Dataform, and when the workflow should move to Dataflow, Dataproc, or external orchestration.

When to Use

designing or reviewing BigQuery physical models
deciding between Dataform, dbt, Dataflow, or Dataproc responsibilities
tuning partitioning, clustering, slots, and cost behavior
defining ingestion and transformation boundaries on GCP
building platform-native analytics workflows around BigQuery

Do not treat BigQuery as a universal default for every preprocessing and orchestration need.

Workflow

Define the workload boundary. Clarify:
- landing pattern
- transformation complexity
- latency requirements
- governance and regional constraints
- cost sensitivity
Design BigQuery physical layout intentionally. Cover:
- partitioning
- clustering
- dataset boundaries
- publish layers
- data retention and serving expectations
Choose the transformation surface. Consider:
- Dataform for warehouse-native SQL transformation workflows
- dbt when the team already standardizes there
- Dataflow or Dataproc when preprocessing or runtime requirements exceed warehouse-native fit
Define orchestration and operations. Include:
- where orchestration runs
- slot and concurrency behavior
- failure and rerun expectations
- validation gates before publish
Validate cost and governance behavior. Require:
- slot or query cost awareness
- service-account and secret controls
- policy tags or governance metadata where needed
- publish-readiness evidence

Common Rationalizations

Rationalization	Reality
"Everything on GCP should run in BigQuery."	Some preprocessing, streaming, or protocol-heavy work belongs in `Dataflow`, `Dataproc`, or upstream services.
"Dataform is just a SQL wrapper."	It changes how transformation workflows, dependencies, testing, and deployment are managed.
"Partitioning and clustering can be tuned later."	Poor physical design often becomes a long-term cost and performance tax.

Red Flags

BigQuery physical design is implicit or copied from defaults
transformation boundaries between Dataform, Dataflow, and Dataproc are unclear
cost or slot behavior is not considered during model design
publish and validation behavior is undefined for warehouse-native pipelines
governance metadata and access are treated as separate cleanup work

Verification

BigQuery physical design choices are explicit and workload-aware
Dataform, BigQuery, and other GCP services have clear boundaries
Cost, slots, and concurrency implications are documented
Governance and access controls are accounted for
Publish and validation behavior are explicit before release

Frequently asked questions

What does the Bigquery And Dataform Platform Engineering AI skill do?

Why use Bigquery And Dataform Platform Engineering on TypingMind?

Because you install it once and use it with any model. Bigquery And Dataform Platform Engineering 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 Bigquery And Dataform Platform Engineering in TypingMind?

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

Which AI models can use Bigquery And Dataform Platform Engineering?

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 Bigquery And Dataform Platform Engineering?

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

Is the Bigquery And Dataform Platform Engineering 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

BigQuery And Dataform Platform Engineering

Overview

When to Use

Workflow

Common Rationalizations

Red Flags

Verification

Frequently asked questions

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