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Data Specification

Community
vaquarkhan
data-specification

Creates structured specifications for data products and pipeline changes. Use when starting a new pipeline, model, ingestion flow, or any significant change with unclear requirements.

Overview

Publishervaquarkhan
Repositorydata-engineering-agent-skills
Skill namedata-specification
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 Data Specification 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/data-specification
  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/data-specification .claude/skills/data-specification
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Data Specification 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 Data Specification 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 Data Specification 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.

Data Specification

Overview

Write the data specification before writing pipeline code. The spec should define business intent, source and destination expectations, quality rules, and success criteria so the agent is not forced to guess.

When to Use

  • new ingestion or transformation projects
  • schema or contract changes
  • major changes to data products, marts, or semantic models
  • requests that sound simple but leave operational details unclear

Do not use this for trivial spelling fixes or non-behavioral documentation edits.

Workflow

  1. State assumptions up front. Include:

    • business objective
    • source systems
    • destination systems
    • data grain
    • update cadence
    • retention expectations
    • security or privacy constraints

  2. Write the specification around required sections.

    • Objective
    • Business outcomes
    • Source systems and contracts
    • Destination tables, files, or streams
    • Freshness and SLA expectations
    • Data quality rules
    • Security and access boundaries
    • Backfill and replay expectations
    • Success criteria
    • Open questions

  3. Resolve ambiguity before planning. If the spec cannot answer frequency, grain, keys, slowly changing behavior, or null handling, pause and ask.

  4. Save the spec in version control. A data change without a written spec becomes tribal knowledge.

Common Rationalizations

RationalizationReality
"We just need the table built quickly."The wrong grain or contract creates expensive downstream rework.
"We can infer the business metric later."That usually creates multiple conflicting definitions of the same metric.
"The destination schema is enough."Schedules, freshness, backfills, and access rules matter just as much as columns.

Red Flags

  • no business owner is named
  • source-of-truth systems are unclear
  • success is defined as "pipeline runs"
  • backfill behavior is omitted
  • quality rules are implied instead of written

Verification

  • The spec states the business outcome and expected users
  • Source, destination, grain, cadence, and ownership are explicit
  • Quality, security, and replay expectations are defined
  • Open questions are listed instead of guessed
  • The spec is saved in the repository

Frequently asked questions

What does the Data Specification AI skill do?

Creates structured specifications for data products and pipeline changes. Use when starting a new pipeline, model, ingestion flow, or any significant change with unclear requirements.

Why use Data Specification on TypingMind?

Because you install it once and use it with any model. Data Specification 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 Data Specification in TypingMind?

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

Which AI models can use Data Specification?

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 Data Specification?

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

Is the Data Specification 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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