Best use case
Data Catalog Enricher is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Enriches data catalog entries with automated metadata
Teams using Data Catalog Enricher should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/data-catalog-enricher/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Data Catalog Enricher Compares
| Feature / Agent | Data Catalog Enricher | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Enriches data catalog entries with automated metadata
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Data Catalog Enricher
## Overview
Enriches data catalog entries with automated metadata. This skill enhances data discoverability and governance through intelligent metadata augmentation.
## Capabilities
- Automated tag suggestion
- Business glossary term matching
- Owner/steward recommendation
- Usage pattern analysis
- Data classification (sensitivity, PII)
- Quality score integration
- Lineage enrichment
- Search optimization
## Input Schema
```json
{
"catalogEntry": "object",
"dataProfile": "object",
"existingGlossary": "object",
"organizationContext": "object"
}
```
## Output Schema
```json
{
"enrichedEntry": "object",
"suggestedTags": ["string"],
"glossaryMatches": ["object"],
"classificationResults": "object",
"ownerSuggestions": ["string"]
}
```
## Target Processes
- Data Catalog
- Data Lineage Mapping
- Data Quality Framework
## Usage Guidelines
1. Provide existing catalog entry for enrichment
2. Include data profile for classification analysis
3. Supply business glossary for term matching
4. Add organization context for owner recommendations
## Best Practices
- Regularly update glossary matches as glossary evolves
- Validate PII classifications with data stewards
- Integrate quality scores from quality framework
- Maintain consistent tagging taxonomy
- Review and approve automated classificationsRelated Skills
structured-data
JSON-LD schema markup and validation.
CVE/CWE Database Skill
CVE and CWE database querying and management
error-code-catalog
Manage and document SDK error codes and messages
test-data-generation
Synthetic test data generation and management using Faker.js and similar tools. Generate realistic test data, create data factories, implement database seeding, and manage test data anonymization.
iOS Persistence (Core Data/Realm)
Specialized skill for iOS local data persistence solutions
Room Database
Expert skill for Android Room persistence library
metadata-standards-implementation
Apply Dublin Core, METS, MODS, and other metadata schemas for digital collections and archival materials
health-data-integration
Facilitate interoperability between health IT systems including EHR, HIE, and clinical decision support through HL7, FHIR, and other healthcare data standards
data-versioning-manager
Skill for managing data versions and provenance
data-encoder
Classical data encoding skill for quantum machine learning applications
root-data-analyzer
ROOT/CERN data analysis skill for high-energy physics data processing, histogramming, and statistical analysis
bluesky-data-collection
Bluesky experimental orchestration skill for scan automation, data collection, and metadata management