How do I install the Cosmosdb Data Modeling skill?

Run npx skillmds@latest add atc-net/cosmosdb-data-modeling in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Cosmosdb Data Modeling safe to use?

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Which AI agents work with Cosmosdb Data Modeling?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Cosmosdb Data Modeling free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Cosmosdb Data Modeling?

atc-net (@atc-net) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Cosmosdb Data Modeling by atc-net — Agent Skill

Name: Cosmosdb Data Modeling
Rating: 4.2 (5 reviews)
Author: atc-net

Cosmosdb Data Modeling

Azure Cosmos DB data modeling best practices: embedding vs referencing, document size limits, schema versioning, type discriminators, JSON serialization, denormalization, and relationship patterns. USE FOR: Cosmos DB document design, embedding related data, referencing large data, 2MB item limit, nesting depth, numeric precision, denormalize reads, schema versions, type discriminator, polymorphic containers, JSON serialization, relationship references. DO NOT USE FOR: partition key design (use cosmosdb-partition-key), query optimization (use cosmosdb-query-optimization), SDK client code (use cosmosdb-sdk).

atc-net Updated Sep 8, 2026 3 repo stars

File contents

Azure Cosmos DB Data Modeling

Best practices for designing document schemas in Azure Cosmos DB, prioritized by impact.

When to Apply

Reference these guidelines when:

Designing document schemas for Cosmos DB containers
Deciding between embedding and referencing related data
Handling polymorphic data in shared containers
Planning schema evolution and versioning
Configuring JSON serialization for Cosmos DB documents

Rules

model-embed-related - Embed related data retrieved together
model-reference-large - Reference data when items get too large
model-avoid-2mb-limit - Keep items well under 2MB limit
model-id-constraints - Follow ID value length and character constraints
model-nesting-depth - Stay within 128-level nesting depth limit
model-numeric-precision - Understand IEEE 754 numeric precision limits
model-denormalize-reads - Denormalize for read-heavy workloads
model-schema-versioning - Version your document schemas
model-type-discriminator - Use type discriminators for polymorphic data
model-json-serialization - Handle JSON serialization correctly
model-relationship-references - Use ID references with transient hydration

---name: cosmosdb-data-modelingdescription: Azure Cosmos DB data modeling best practices: embedding vs referencing, document size limits, schema versioning, type discriminators, JSON serialization, denormalization, and relationship patterns. USE FOR: Cosmos DB document design, embedding related data, referencing large data, 2MB item limit, nesting depth, numeric precision, denormalize reads, schema versions, type discriminator, polymorphic containers, JSON serialization, relationship references. DO NOT USE FOR: partition key design (use cosmosdb-partition-key), query optimization (use cosmosdb-query-optimization), SDK client code (use cosmosdb-sdk).---# Azure Cosmos DB Data ModelingBest practices for designing document schemas in Azure Cosmos DB, prioritized by impact.## When to ApplyReference these guidelines when:- Designing document schemas for Cosmos DB containers- Deciding between embedding and referencing related data- Handling polymorphic data in shared containers- Planning schema evolution and versioning- Configuring JSON serialization for Cosmos DB documents## Rules- [model-embed-related](references/model-embed-related.md) - Embed related data retrieved together- [model-reference-large](references/model-reference-large.md) - Reference data when items get too large- [model-avoid-2mb-limit](references/model-avoid-2mb-limit.md) - Keep items well under 2MB limit- [model-id-constraints](references/model-id-constraints.md) - Follow ID value length and character constraints- [model-nesting-depth](references/model-nesting-depth.md) - Stay within 128-level nesting depth limit- [model-numeric-precision](references/model-numeric-precision.md) - Understand IEEE 754 numeric precision limits- [model-denormalize-reads](references/model-denormalize-reads.md) - Denormalize for read-heavy workloads- [model-schema-versioning](references/model-schema-versioning.md) - Version your document schemas- [model-type-discriminator](references/model-type-discriminator.md) - Use type discriminators for polymorphic data- [model-json-serialization](references/model-json-serialization.md) - Handle JSON serialization correctly- [model-relationship-references](references/model-relationship-references.md) - Use ID references with transient hydration

Frequently asked questions