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Data Expert

Listed
oimiragieo·vlatest·data

Analyzes complex datasets, identifies trends, and generates actionable insights with expert precision.

Repository

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 oimiragieo/agent-studio --skill data-expert -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

oimiragieo

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

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

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Automated safety checks

scanned

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Not scanned. none of the 73 skills in the repository match this slug — it was most likely renamed or removed upstream after the registry indexed it Source recorded as oimiragieo/agent-studio.
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About this skill

What it does

The Data Expert skill provides guidance and best practices for developers working with data within an AI agent context, particularly when reviewing or writing code. It focuses on ensuring data quality, efficient fetching, reproducible pipelines (using tools like dvc), synchronization between UI and simulation components, robust validation (using Pydantic), and comprehensive data tracking through charts. This skill helps ensure the accuracy and reliability of data-driven applications.

When to use it

  • When reviewing code involving data exploration or summary statistics.
  • During development of server components that fetch data from external APIs.
  • When implementing data pipelines requiring reproducibility and version control (e.g., using dvc).
  • To ensure efficient synchronization of data between a JavaScript UI and a WASM simulation.
  • When creating dashboards or chart pages to track game-related metrics.

Key capabilities

  • Provides guidelines for initial data exploration and summary statistics.
  • Promotes the implementation of data quality checks early in the analysis process.
  • Offers strategies for handling missing data (imputation, removal, flagging).
  • Suggests best practices for fetching data in server components with caching (next: { revalidate: 3600 }).
  • Encourages using dvc for managing and reproducing data preprocessing steps.
  • Recommends efficient synchronization techniques between UI and WASM simulation environments (periodic updates, event-driven systems, optimized data structures).
  • Advocates for using Pydantic models for rigorous data validation.

Example prompts

  • "Review this code snippet for best practices in handling missing data."
  • "How should I fetch data from an API in a server component to ensure efficient caching?"
  • "What's the recommended approach for synchronizing data between my JavaScript UI and WASM simulation?"
  • "Suggest ways to validate incoming data using Pydantic."

Tips & gotchas

  • The skill primarily provides coding guidelines; it doesn’t perform data analysis itself.
  • It assumes familiarity with tools like dvc and libraries like Pydantic.
  • Pay close attention to the recommendations for efficient data transfer when synchronizing between UI and WASM environments, as performance can be critical.