nimble

Extracts structured data from multiple websites into normalized JSON tables.

25|10|Updated Apr 1, 2026
One-click install
npx skills add https://github.com/neuron-one/GODMODE --skill nimble
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: nimble
Source: https://github.com/neuron-one/GODMODE/tree/main/skills/data/nimble
Command: npx skills add https://github.com/neuron-one/GODMODE --skill nimble

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Extract structured data from multiple websites into a normalized, consistent schema, reducing manual data wrangling and errors.

Core Features & Use Cases

  • Multi-site extraction: fetches data from several sites in parallel and parses common fields.
  • Normalization: maps diverse site schemas into a single unified structure.
  • Output formats: returns JSON tables (and CSV if needed) with stable keys for downstream analytics.
  • Use Case: build centralized datasets (e.g., product prices or rankings) from multiple sources for dashboards and reporting.

Quick Start

Provide a URL list and have the tool return a normalized JSON table.

Frequently Asked Questions about nimble

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

FAQPage Schema
How do I extract structured data from multiple websites into a single JSON table?

To extract structured data from multiple websites, provide a URL list to fetch and parse common fields in parallel, normalizing diverse site schemas into a single unified JSON table for downstream analytics.

What is web data normalization and when do I need it for cross-site aggregation?

Web data normalization maps diverse site schemas into a consistent structure with stable keys. You need it for cross-site aggregation tasks like building centralized datasets of product prices or rankings from multiple sources.

Can I output CSV instead of JSON when extracting data from multiple sites?

Yes, you can output CSV if needed. The tool returns normalized JSON tables by default for deterministic extraction across sources, with CSV available as an alternate output format for reporting and dashboards.

What's the best way to build centralized datasets from multiple sources for dashboards?

The best way to build centralized datasets is fetching data from several sites in parallel and parsing common fields into a normalized, consistent schema, reducing manual data wrangling and errors for your dashboards.

Does multi-site extraction require deterministic field mapping across different sources?

Yes, multi-site extraction requires deterministic field mapping to ensure stable keys across different sources. This normalization process maps diverse site schemas into a single unified structure for consistent downstream analytics.