Which data providers are supported by this framework?

It includes optimization strategies for over 150 sources, including Apollo, ZoomInfo, Hunter, Clearbit, RocketReach, and specialized industry databases.

What is the data-sourcing skill for Claude Code?

It is a specialized skill that provides guidance and implementation patterns for selecting and optimizing B2B data providers to improve lead enrichment accuracy and cost-efficiency.

How does it help reduce API credit costs?

The skill implements 'waterfall' logic, which attempts to find data using cheaper or cached sources first before escalating to expensive premium providers.

Does it provide code for implementation?

Yes, the skill includes templates for JavaScript/Node.js integration and Python-based quality scoring and cost calculation.

Can this skill help with international data sourcing?

Yes, it provides a provider selection matrix that highlights strengths and weaknesses for international coverage versus US-centric data.

Data Sourcing & Provider Optimization

Name: Data Sourcing & Provider Optimization
Author: Activer007

byActiver007

Automatización de Marketing

Optimizes B2B lead enrichment by intelligently routing data requests across 150+ providers to maximize accuracy and minimize credit costs.

Acerca de

This skill transforms Claude into a GTM and RevOps expert capable of architecting sophisticated data enrichment workflows. It provides a comprehensive framework for selecting the best data providers—such as Apollo, ZoomInfo, Hunter, and Clearbit—based on specific input scenarios like LinkedIn URLs or domain names. By implementing multi-step 'waterfall' logic and smart caching strategies, the skill helps teams achieve up to 95% data success rates while drastically reducing API spend through tiered cost management and batch processing optimizations.

Características Principales

Industry-specific provider recommendations (SaaS, Healthcare, E-commerce)
Credit usage optimization across five distinct cost tiers
Scenario-based provider selection for emails and phone numbers
Data quality scoring framework for multi-source validation
0 GitHub stars
Intelligent waterfall logic for sequential provider attempts

Casos de Uso

Designing resilient data engineering workflows for company intelligence
Building automated GTM enrichment pipelines for sales and marketing teams
Auditing and reducing monthly credit burn across multiple data vendors

Acerca de

Características Principales

Industry-specific provider recommendations (SaaS, Healthcare, E-commerce)
Credit usage optimization across five distinct cost tiers
Scenario-based provider selection for emails and phone numbers
Data quality scoring framework for multi-source validation
0 GitHub stars
Intelligent waterfall logic for sequential provider attempts

Casos de Uso

Designing resilient data engineering workflows for company intelligence
Building automated GTM enrichment pipelines for sales and marketing teams
Auditing and reducing monthly credit burn across multiple data vendors