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instacart-list-scraper

Document the Instacart prewarm and list-scraper subsystem for operational guidance.

Updated May 15, 2026
Search Tags:#instacart#scraper#datadog#quartz#prewarm#appconfig#urza
One-click install
npx skills add https://github.com/Pear-Commerce/pear-ai-skills --skill instacart-list-scraper
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: instacart-list-scraper
Source: https://github.com/Pear-Commerce/pear-ai-skills/tree/main/skills/instacart-list-scraper
Command: npx skills add https://github.com/Pear-Commerce/pear-ai-skills --skill instacart-list-scraper

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill eliminates scattered documentation lookup and guesswork for engineers working on the Instacart prewarm and list-scraper subsystem, providing a single source of truth for operating, tuning, and debugging the InstacartBatchInventoryList processing pipeline on api.pearcommerce.com.

Core Features & Use Cases

  • Full subsystem operational reference: Covers the Quartz job schedule, InstacartPrewarmer modes, list population filtering, and URZA write cascade logic for complete context of the prewarm workflow.
  • Tuning and troubleshooting guidance: Includes AppConfig knob documentation, Datadog dashboard widget interpretation, throughput calculation formulas, and documented anti-patterns to avoid for capacity planning and issue resolution.
  • Engineering onboarding support: Serves as a reference for new team members working on prewarm path changes, with clear package responsibility breakdowns and existing instrumentation details.
  • Use Case: For example, if an engineer needs to resolve accumulating prewarm backlog, this skill provides the correct AppConfig keys to adjust, the proper tuning order, and the health metrics to monitor for impact.

Quick Start

Use the instacart-list-scraper skill to interpret the Instacart Prewarm Datadog dashboard and identify the root cause of a negative Throughput Gap metric.

Frequently Asked Questions about instacart-list-scraper

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

FAQPage Schema
How do I resolve accumulating prewarm backlog in the Instacart list-scraper?▼

To resolve accumulating prewarm backlog, adjust the correct AppConfig keys for the Instacart list-scraper, follow the proper tuning order, and monitor health metrics in Datadog to verify the throughput impact.

How does the Quartz job schedule affect InstacartBatchInventoryList processing?▼

The Quartz job schedule controls the timing and execution of InstacartBatchInventoryList processing, coordinating with InstacartPrewarmer modes and list population filtering to manage the overall prewarm workflow throughput.

What does a negative Throughput Gap metric mean on the Instacart Prewarm Datadog dashboard?▼

A negative Throughput Gap metric on the Instacart Prewarm Datadog dashboard indicates the prewarm system is processing inventory lists slower than expected, requiring investigation into AppConfig knobs and URZA write cascade logic.

Which AppConfig knobs should I adjust first when tuning the Instacart prewarm subsystem?▼

When tuning the Instacart prewarm subsystem, adjust AppConfig knobs in the documented proper tuning order to ensure capacity planning aligns with throughput calculation formulas and avoids known anti-patterns.

How do I onboard new engineers to the Instacart prewarm and list-scraper codebase?▼

Onboard new engineers to the Instacart prewarm subsystem by referencing the package responsibility breakdown, existing instrumentation details, and code review guidance for prewarm path changes to establish operational context.

What anti-patterns should I avoid when operating the Instacart prewarm scraper?▼

Avoid documented anti-patterns when operating the Instacart prewarm scraper by following the throughput calculation formulas, respecting URZA write cascade logic, and monitoring Datadog dashboard widgets for capacity planning.

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