data-modeling

Name: data-modeling
Availability: InStock
Author: jacob-balslev

Design logical and physical data structures from validated conceptual models.

1|Updated May 6, 2026

Search Tags:#indexing #schema design #provenance #constraints #normalization #data-modeling #entity identity

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npx skills add https://github.com/jacob-balslev/skill-graph --skill data-modeling-jacob-balslev

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-modeling
Source: https://github.com/jacob-balslev/skill-graph/tree/main/marketplace/skills/data-modeling
Command: npx skills add https://github.com/jacob-balslev/skill-graph --skill data-modeling-jacob-balslev

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data modeling prevents invalid stored states and preserves meaning over time by turning business intent into logical and physical structures that enforce identity, relationships, and constraints.

Core Features & Use Cases

Identity and Constraints: Define primary keys, uniqueness, cardinality, and rule-enforcing structures so the stored data matches expectations.
Normalization vs Denormalization: Choose the shape of persisted records (and derived artifacts) based on write/read patterns and consistency tradeoffs.
Provenance and Lifecycle: Capture where data comes from, how derived fields are refreshed, and what long-lived obligations (retention/privacy) apply.

Quick Start

Use the data-modeling skill to design a logical and physical schema from a validated conceptual model before planning any database changes.

Frequently Asked Questions about data-modeling

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I design a database schema that enforces entity identity and constraints?▼

Database schema design defines primary keys, uniqueness, cardinality, and rule-enforcing structures so stored data matches expectations and prevents invalid states. This skill specifies logical and physical structures to enforce identity and relationships.

When should I use normalization versus denormalization in schema design?▼

Choose normalization versus denormalization based on write and read patterns alongside consistency tradeoffs. Schema design requires explicit tradeoffs between these approaches to shape persisted records and derived artifacts effectively.

How do I capture data provenance and lifecycle rules in a physical schema?▼

Data provenance is captured in a physical schema by defining source-of-truth rules for derived fields and specifying how derived data refreshes. Schema design also maps long-lived obligations like retention and privacy.

Does this schema design approach work for document, graph, and event warehouse systems?▼

Yes, this schema design translates validated conceptual models into physical structures across relational, document, graph, and event warehouse systems. It specifies entities, identity, keys, and constraints for each system type.

What is the best way to prepare a logical schema for database migration?▼

The best way to prepare for database migration is to design a complete physical schema with indexing and query-awareness. This skill provides handoff readiness by specifying entities, constraints, and relationships.

Why do I need indexing and query awareness when designing data structures?▼

Indexing and query awareness are needed during data structure design to ensure logical schemas translate to performant physical implementations. Schema design requires explicit indexing strategies to support anticipated read patterns.