What is the primary role of CLIProxyAPI in this skill?

CLIProxyAPI acts as the exclusive upstream client for all LLM requests, handling model discovery and routing to ensure no direct provider SDK calls are made within the backend.

How does the 6-stage workflow function?

The workflow is orchestrated through core packages that manage stage-specific prompts, artifact logic, and structured JSON validation for peer and final reviews.

Does this skill support PDF processing?

Yes, it includes logic for PDF text extraction, enhanced OCR, and page image rendering, specifically optimized for report-quality medical data.

What are the technical requirements for the qEEG Council Backend?

It requires Python 3.11 or higher, the 'uv' package manager, and a reachable CLIProxyAPI instance (defaulting to port 8317).

qEEG Council Backend Manager

Name: qEEG Council Backend Manager
Author: DMontgomery40

byDMontgomery40

API Development

Builds and maintains the FastAPI-powered qEEG Council backend for automated EEG analysis workflows and clinical report generation.

About

The qEEG Council Backend skill provides specialized capabilities for managing a sophisticated medical analysis platform. It enables Claude to implement and refactor a complex 6-stage workflow orchestration, manage SQLite persistence, and handle multi-format artifact exports. The skill focuses on integrating CLIProxyAPI for model discovery, performing high-quality PDF text extraction with enhanced OCR, and managing Server-Sent Events (SSE) for real-time progress tracking. It ensures strict adherence to architectural patterns, such as vendor-agnostic LLM routing and structured filesystem storage for patient data.

Key Features

6-stage clinical workflow orchestration and data-pack validation
PDF processing including enhanced OCR and page image rendering
FastAPI backend architecture and endpoint implementation
CLIProxyAPI integration for standardized LLM request routing
SQLite persistence and structured artifact storage management
0 GitHub stars

Use Cases

Debugging artifact storage paths and SQLite data persistence layers
Implementing multi-stage automated peer-review logic for qEEG data
Refactoring FastAPI endpoints for medical report processing

About

Key Features

6-stage clinical workflow orchestration and data-pack validation
PDF processing including enhanced OCR and page image rendering
FastAPI backend architecture and endpoint implementation
CLIProxyAPI integration for standardized LLM request routing
SQLite persistence and structured artifact storage management
0 GitHub stars

Use Cases

Debugging artifact storage paths and SQLite data persistence layers
Implementing multi-stage automated peer-review logic for qEEG data
Refactoring FastAPI endpoints for medical report processing