Data Catalog And Discovery

Community

vaquarkhan

data-catalog-and-discovery

Guides agents through data catalog, discovery, and metadata quality workflows. Use when publishing datasets, improving discoverability, curating lineage metadata, or making data products easier for other teams to find and trust.

Overview

Publisher	vaquarkhan
Repository	`data-engineering-agent-skills`
Skill name	`data-catalog-and-discovery`
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 Data Catalog And Discovery 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/data-catalog-and-discovery
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/data-catalog-and-discovery

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/data-catalog-and-discovery .claude/skills/data-catalog-and-discovery

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

Use it in TypingMind

Enable Data Catalog And Discovery 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 Catalog And Discovery 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 Catalog And Discovery 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 Catalog And Discovery

Overview

Use this skill when the challenge is not only building data, but making it understandable and discoverable. It helps agents treat metadata, ownership, lineage, and usage context as delivery artifacts instead of afterthoughts.

When to Use

publishing a new shared dataset
improving catalog metadata quality
curating lineage, tags, or ownership information
reducing duplicate datasets created because teams cannot find trusted ones

Do not stop at filling in a title and description. Discovery quality requires operational context too.

Workflow

Define the discovery contract. Include:
- owner
- business description
- technical description
- grain
- freshness expectation
- intended consumers
Link the asset to its lineage. Show upstream sources, transformation layers, and major downstream uses where possible.
Add trust signals. Typical signals:
- quality status
- SLA or freshness status
- certification or review state
- deprecation state
Tag for real discovery, not taxonomy theater.
Revisit metadata when the contract changes.

Common Rationalizations

Rationalization	Reality
"The table name is descriptive enough."	Names alone do not explain grain, trust, or ownership.
"We can catalog it after people start using it."	Poor discovery usually leads to duplicate local copies first.
"Lineage is a platform problem, not a delivery problem."	Producers know the business meaning and must help make lineage useful.

Red Flags

shared datasets have no owner or description
certified and experimental assets are indistinguishable
metadata is copied from schema names without business meaning
deprecation state is absent for old assets

Verification

Ownership, description, grain, and freshness are documented
Lineage or source context is attached
Trust signals exist for consumers
Discovery metadata is updated when the contract changes

Frequently asked questions

What does the Data Catalog And Discovery AI skill do?

Why use Data Catalog And Discovery on TypingMind?

Because you install it once and use it with any model. Data Catalog And Discovery 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 Catalog And Discovery in TypingMind?

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

Which AI models can use Data Catalog And Discovery?

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 Catalog And Discovery?

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

Is the Data Catalog And Discovery 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

Discord Clawd

openclaw

OrganizationPopular

Use to talk to the Discord-backed OpenClaw agent/session; not for archive search.

Discrawl

Discord archive: search, sync freshness, DMs, summaries, TUI, repo/release work.

Gitcrawl

GitHub archive: issue/PR search, sync freshness, duplicate clusters, current GitHub handoff, and Gitcrawl repo work.

Graincrawl

Granola archive: search, sync freshness, notes, transcripts, panels, SQL counts, and Graincrawl repo work.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Data Catalog And Discovery

Overview

When to Use

Workflow

Common Rationalizations

Red Flags

Verification

Frequently asked questions

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