Dbt And Analytics Engineering

Community

vaquarkhan

dbt-and-analytics-engineering

Guides agents through analytics engineering workflows with dbt. Use when building or modifying staging models, marts, tests, snapshots, documentation, exposures, or semantic-layer-facing models.

Overview

Publisher	vaquarkhan
Repository	`data-engineering-agent-skills`
Skill name	`dbt-and-analytics-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 Dbt And Analytics 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/dbt-and-analytics-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/dbt-and-analytics-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/dbt-and-analytics-engineering .claude/skills/dbt-and-analytics-engineering

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

Use it in TypingMind

Enable Dbt And Analytics 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 Dbt And Analytics 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 Dbt And Analytics 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.

dbt And Analytics Engineering

Overview

Use this skill when the job is analytics engineering rather than raw ingestion. It helps agents build trustworthy dbt projects with clear layering, reusable models, tests, documentation, and publish-safe business definitions.

When to Use

creating or changing dbt models
building staging, intermediate, or mart layers
adding tests, snapshots, or exposures
organizing business logic for analysts and BI tools
preparing semantic-layer-friendly outputs

Do not use this to justify putting ingestion or orchestration logic inside dbt.

Workflow

Confirm the model's role. Decide whether it belongs in:
- staging
- intermediate
- marts
- snapshot or semantic-serving layers
Define the business grain and contract. Capture:
- keys
- metric intent
- filter logic
- null handling
- freshness expectations
Add tests and documentation with the model. Typical checks:
- unique
- not null
- relationships
- accepted values
- source freshness where relevant
Keep model boundaries clean. Avoid mixing raw cleanup, business logic, and publish semantics in one model.
Validate downstream usability. Make sure the output is understandable to analysts, dashboards, and metric consumers.

Common Rationalizations

Rationalization	Reality
"It is only SQL, we do not need model structure."	Poor layering creates brittle marts and duplicate business logic.
"We can add tests after the dashboard is working."	Untested metrics create trust problems that are hard to unwind.
"A giant model is easier to maintain."	Monolithic models hide grain changes, assumptions, and reuse opportunities.

Red Flags

model grain is unclear
business logic is duplicated across marts
no YAML tests or documentation accompany the change
publish models depend directly on raw sources without clear staging

Verification

The model has a clear layer and business purpose
Grain, keys, and metric assumptions are explicit
Tests and documentation ship with the model
Output usability for downstream consumers has been considered

Frequently asked questions

What does the Dbt And Analytics Engineering AI skill do?

Guides agents through analytics engineering workflows with dbt. Use when building or modifying staging models, marts, tests, snapshots, documentation, exposures, or semantic-layer-facing models.

Why use Dbt And Analytics Engineering on TypingMind?

Because you install it once and use it with any model. Dbt And Analytics 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 Dbt And Analytics Engineering in TypingMind?

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

Which AI models can use Dbt And Analytics 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 Dbt And Analytics Engineering?

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

Is the Dbt And Analytics 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.

View all

Comps Analysis

NousResearch

OrganizationPopular

Build comparable-company valuation workbooks in Excel.

Weights And Biases

W&B: log ML experiments, sweeps, model registry, dashboards.

Faiss

Fast vector similarity search at billion scale.

Serving Llms Vllm

vLLM: high-throughput LLM serving, OpenAI API, quantization.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

dbt And Analytics Engineering

Overview

When to Use

Workflow

Common Rationalizations

Red Flags

Verification

Frequently asked questions

Related AI Skills

Set up your own AI workspace now