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Cosmosdb Datamodeling

Official
github·vlatest·cloud·MIT

This skill helps you design efficient Cosmos DB data models for optimal performance and cost savings by leveraging its flexible schema.

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add github/awesome-copilot --skill cosmosdb-datamodeling -a claude-code
  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

github

Matched to the vendor's own GitHub organisation.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • Declares what it does: failed

    No SKILL.md was found in the skill's directory.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned github/awesome-copilot at 6c4d33b9cf.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Scanned
0 files
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The cosmosdb-datamodeling skill enables AI agents to design and optimize data models for Azure Cosmos DB, a globally distributed NoSQL database. It supports schema design, query optimization, and partition key selection tailored for high-performance, scalable applications.

When to use it

  • Designing a new data model for an application that requires low-latency access across multiple regions.
  • Optimizing existing queries to reduce cost and improve performance in Cosmos DB.
  • Preparing data structures for time-series or hierarchical data storage in a NoSQL environment.

Key capabilities

  • Schema design for document, key-value, and graph data models
  • Partition key selection and indexing strategies
  • Query optimization for efficient data retrieval

Example prompts

  • "Design a Cosmos DB data model for storing user activity logs with time-series data."
  • "Optimize this query to reduce RU consumption in Azure Cosmos DB."
  • "Suggest partition key options for a global e-commerce application using Cosmos DB."

Tips & gotchas

  • Ensure you have a clear understanding of your data access patterns before modeling.
  • Use the skill alongside Azure Cosmos DB documentation for best practices and limitations.