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EnergySystemsGroup
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Storage Agent Skill

Transfer funding opportunity data from staging to production PostgreSQL.

Updated Aug 27, 2026
Search Tags:#database#production#data pipeline#staging#sql#etl#funding opportunities
One-click install
npx skills add https://github.com/EnergySystemsGroup/Meridian-ESG --skill storage-agent-skill
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Storage Agent Skill
Source: https://github.com/EnergySystemsGroup/Meridian-ESG/tree/main/.claude/skills/storage
Command: npx skills add https://github.com/EnergySystemsGroup/Meridian-ESG --skill storage-agent-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and scripts (resource) components.

What problem does it solve?

This Skill automates the critical process of moving funding opportunity data from a staging environment to production, ensuring data integrity and efficient pipeline progression.

Core Features & Use Cases

  • Data Transfer: Safely and efficiently UPSERTs records from staging to the production funding_opportunities table.
  • Data Sanitization & Mapping: Applies predefined sanitization functions and field mappings to ensure data quality and consistency.
  • Coverage Area Linking: Automatically links funding opportunities to relevant geographical coverage areas using fuzzy matching.
  • Status Management: Updates staging records to 'complete' or 'failed' based on processing outcomes.
  • Use Case: After an analysis agent has completed its work on a batch of potential funding opportunities, this Skill takes over to clean, validate, and permanently store that data in the main database, ready for review and promotion.

Quick Start

Process the next batch of pending storage records using the storage agent.

Frequently Asked Questions about Storage Agent Skill

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

FAQPage Schema
How do I automate data transfer from a staging database to production PostgreSQL?▼

Automating data transfer to production PostgreSQL involves UPSERTing records from a staging table, applying field mappings, and executing batch SQL for efficient pipeline progression.

How does fuzzy matching work for linking coverage areas in a data pipeline?▼

Fuzzy matching for coverage areas links funding opportunities to relevant geographical regions during the ETL process by comparing sanitized field values against existing database records.

What is the best way to sanitize and map fields before moving staging data to production?▼

Sanitizing and mapping fields before production transfer requires applying predefined functions to raw staging data, ensuring data quality and consistency prior to executing batch SQL UPSERTs.

Can I use batch SQL execution for ETL pipelines without hitting data integrity errors?▼

Batch SQL execution supports ETL pipelines safely by including robust error handling and verification steps, automatically updating staging statuses to failed if processing errors occur.

Does this staging to production transfer process require a specific database schema?▼

The staging to production transfer process requires a schema containing staging tables like manual_funding_opportunities_staging and production tables like funding_opportunities to map fields correctly.

Why does a staging record status update to complete or failed after processing?▼

A staging record status updates to complete or failed to track processing outcomes, ensuring data pipeline integrity by verifying successful UPSERTs or flagging errors during production transfer.

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