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Ditto190
Ditto190COMMUNITY
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data-fetch

Fetch data from APIs, files, or databases into structured datasets.

Updated Jan 1, 2026
Search Tags:#automation#integration#ETL#data#fetch#APIs
One-click install
npx skills add https://github.com/Ditto190/modme-ui-01 --skill data-fetch
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-fetch
Source: https://github.com/Ditto190/modme-ui-01/tree/main/.github/skills/data-fetch
Command: npx skills add https://github.com/Ditto190/modme-ui-01 --skill data-fetch

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps automate the collection of data from multiple sources, reducing manual gathering and duplication across projects.

Core Features & Use Cases

  • Fetch data from APIs, files, or databases in a repeatable workflow.
  • Support simple scheduling, retry, and error handling for ingestion tasks.
  • Use Case: regularly pull inventory data from a REST API and store results in a single dataset for analytics.

Quick Start

Use this skill to fetch data from a target API or source and return a structured dataset ready for processing.

Frequently Asked Questions about data-fetch

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

FAQPage Schema
How do I automate data collection from multiple APIs?▼

Automating data collection from APIs requires a repeatable workflow that handles polling, retries, and error handling. This approach fetches data from diverse sources and returns a structured dataset ready for downstream processing.

Can I schedule regular ETL data fetching from a REST API?▼

Yes, you can schedule regular ETL data fetching from a REST API to automate data collection. This supports simple scheduling to consistently pull data, such as inventory levels, and store results in a single dataset for analytics.

What is needed to configure lightweight data syncing across teams?▼

Configuring lightweight data syncing requires only YAML frontmatter to define the source and behavior, with optional extension directories for customization. This setup enables repeatable ingestion tasks across files, APIs, or databases.

Does this data fetch approach support file ingestion and databases?▼

Yes, this data fetch approach supports file ingestion and databases alongside API polling. It is designed to fetch data from diverse sources into a repeatable workflow, reducing manual gathering and duplication across projects.

What is the best way to reduce manual data gathering across projects?▼

The best way to reduce manual data gathering is to implement automated data fetching workflows. By pulling data from APIs, files, or databases in a repeatable process, you eliminate duplication and ensure consistent dataset availability.

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