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retailer-store-import-feasibility

Validates retailer store import feasibility using proxy-backed Java probes and generates Store.SStore-compatible JSON artifacts.

Updated May 15, 2026
Search Tags:#data normalization#store locator#store import#retailer feasibility#Store.SStore#proxy scraping#java probes
One-click install
npx skills add https://github.com/Pear-Commerce/pear-ai-skills --skill retailer-store-import-feasibility
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: retailer-store-import-feasibility
Source: https://github.com/Pear-Commerce/pear-ai-skills/tree/main/skills/retailer-store-import-feasibility
Command: npx skills add https://github.com/Pear-Commerce/pear-ai-skills --skill retailer-store-import-feasibility

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill eliminates the guesswork and wasted development effort when attempting to import retailer store data into Pear's production system, by proving whether a store loading route is viable before building production integration code.

Core Features & Use Cases

  • Feasibility Probing: Creates Java @Script probes to test store loading routes through production-like proxies, ensuring routes work in Pear's datacenter environment, not just local machines.
  • Artifact Generation: Produces normalized Store.SStore-compatible JSON data and saves it to the repo, along with a compact EXTRACTION.md rerun guide for future reuse.
  • Productionization Guidance: Provides clear rules for moving proven feasibility work into production store import code, including normalization standards, proxy requirements, and integration with Pear's existing store import job paths.
  • Use Case: When adding support for a new retailer like Tops or Dollar General, use this skill first to prove the store locator route works through production proxies, then use the $retailer-production-integration skill to build the production import code, avoiding wasted work on unviable routes.

Quick Start

Use the retailer-store-import-feasibility skill to prove the store import route for your target retailer, generate the required Store.SStore JSON artifacts, and create an EXTRACTION.md rerun guide for future reference.

Frequently Asked Questions about retailer-store-import-feasibility

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

FAQPage Schema
How do I validate a retailer store locator scraping route before writing production code?▼

Validate store locator scraping routes by deploying Java @Script probes through production-like proxies to test store loading. This proves route viability before building production integration code, saving wasted effort on unworkable paths.

What is store import feasibility probing and when do I need it?▼

Store import feasibility probing tests whether retailer store loading routes work through production datacenter proxies. You need it when adding new retailers to prevent building integration code on unviable store locator paths.

How do I generate normalized store JSON data for Store.SStore compatibility?▼

Generate Store.SStore-compatible JSON by running feasibility probes that produce normalized store artifacts saved to the repository. These probes extract and format store locator data according to established normalization rules.

Do I need production proxies to test store loading routes?▼

Yes, production-runnable proxies are required for store loading validation. The skill enforces proxy requirements to ensure routes work in Pear's datacenter environment rather than only on local development machines.

What's the best way to document a proven store extraction route for future reuse?▼

Document a proven extraction route by generating an EXTRACTION.md rerun guide alongside normalized store JSON artifacts. This compact guide captures the validated store loading steps for future reference and reuse.

Why does my store import route work locally but fail in production?▼

Store import routes fail in production because local machines lack the proxy environment used in the datacenter. Feasibility probing through production-like proxies reveals these discrepancies before productionization.

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