# Data Catalog

Create and enrich durable data catalogs using the native DS_CATALOG_V1 Markdown...

- Repository: https://github.com/microsoft/hve-core (1591 stars)
- Category: Business
- License: CC-BY-4.0
- Last updated: 2026-11-01
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add microsoft/hve-core --skill data-catalog -g -a claude-code -y`
Codex: `npx skills add microsoft/hve-core --skill data-catalog -g -a codex -y`
Cursor: `npx skills add microsoft/hve-core --skill data-catalog -g -a cursor -y`
Gemini CLI: `npx skills add microsoft/hve-core --skill data-catalog -g -a gemini-cli -y`

## Description

Create and enrich durable data catalogs using the native DS_CATALOG_V1 Markdown contract, declared entity relationships, privacy citation fields, and stable relationship IDs. Use when inventorying engagement data, recording semantic relationships, or preparing a catalog for ERD rendering.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Data Catalog Workflow

## Goal

Produce a customer-readable Markdown catalog whose YAML frontmatter is a valid `DS_CATALOG_V1` machine contract. Preserve uncertainty explicitly so inferred or assumed relationships never appear confirmed.

## Flow

1. Confirm the engagement name and the caller-approved durable output path.
2. Inventory entities at business grain. Record source access, tier, volume, profile pointer, classification, lineage, and open questions without copying column-level profile data.
3. Assign every relationship a stable `rel-*` identifier. Record endpoints, maximum cardinality, both endpoint minimums, one or more paired join-key fields, confidence, and evidence basis.
4. Reconcile coverage counts with the entity and relationship records.
5. Render the human-readable sections from the YAML facts, ending with the canonical Data Science and Engineering Coaching disclaimer footer. Narrative can explain facts but cannot redefine them.
6. Validate the artifact with `scripts/validate_catalog.py` before treating it as ready for review.

## Inputs

* Engagement context and a caller-approved output path
* Data source inventory and access status
* Business entity names, grain, and declared relationships
* Existing per-dataset profile paths, when available
* Privacy classifications or standards citations produced by the owning privacy workflow

## Success criteria

The rest: https://github.com/microsoft/hve-core/blob/HEAD/.github/skills/data-science-engineering/data-catalog/SKILL.md