techical-prd

Name: techical-prd
Availability: InStock
Author: nagy-ag

Define the Media Echo Romania platform architecture with BigQuery and Convex storage boundaries.

Updated Mar 9, 2026

Search Tags:#architecture #data pipeline #bigquery #convex #gdelt #system specification

Ready to run

npx skills add https://github.com/nagy-ag/MediaEchoRomania --skill techical-prd

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: techical-prd
Source: https://github.com/nagy-ag/MediaEchoRomania/tree/main/.agents/skills/techical-prd
Command: npx skills add https://github.com/nagy-ag/MediaEchoRomania --skill techical-prd

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides the technical blueprint for the Media Echo Romania platform, ensuring a robust and scalable architecture for media intelligence analysis.

Core Features & Use Cases

Architecture Design: Outlines the system's structure, data flow, and storage boundaries.
Data Management: Specifies ingestion jobs, serving layers, and worker cadences.
Use Case: When designing new features or optimizing existing ones, consult this Skill to understand the agreed-upon split between BigQuery for analytics and Convex for application state, ensuring efficient data handling.

Quick Start

Use the techical-prd skill to understand the data storage boundaries for the Media Echo Romania platform.

Frequently Asked Questions about techical-prd

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

FAQPage Schema

How do I split data management between BigQuery and Convex for a media intelligence platform?▼

To split data management, use BigQuery for raw, normalized, derived, and historical analytics, and use Convex for application state and lightweight cached payloads. This separation prevents live heavy warehouse scans during normal page loads.

What should be included in a technical system specification for a data pipeline?▼

A technical system specification should detail architecture, storage boundaries, ingestion jobs, serving layers, and worker cadences. It defines the system structure and data flow to ensure robust and scalable media intelligence analysis.

Why should I avoid running live BigQuery scans during normal application page loads?▼

Running live BigQuery scans during page loads creates heavy warehouse query loads. To avoid this, enforce a data management strategy using Convex for application state and lightweight cached payloads, reserving BigQuery for backend analytics.

How do I design an ingestion and serving architecture for a GDELT data pipeline?▼

Design the ingestion and serving architecture by defining specific worker cadences and storage boundaries. The architecture separates raw data ingestion from the serving layer, ensuring efficient data flow for GDELT media intelligence analysis.

Does this architecture specification support scaling data pipeline worker processes?▼

Yes, the architecture specification supports scaling by detailing specific worker cadences and ingestion jobs. It outlines the system structure and data flow to ensure robust and scalable media intelligence analysis.