domain-grading-pipeline

Manage Hifz revision grading with human judgment and structured feedback.

Updated Jun 17, 2026

Search Tags:#accuracy #grading #quranic memorization #hifz #traditional education

Ready to run

npx skills add https://github.com/zakariaf/Hifz-Companion --skill domain-grading-pipeline

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: domain-grading-pipeline
Source: https://github.com/zakariaf/Hifz-Companion/tree/main/.claude/skills/domain-grading-pipeline
Command: npx skills add https://github.com/zakariaf/Hifz-Companion --skill domain-grading-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines the grading process for Hifz revision, ensuring accuracy without relying on AI or audio, adhering to traditional methods and maintaining privacy.

Core Features & Use Cases

No AI/No Audio Grading: Human-based grading for Hifz revision, maintaining traditional methods.
Self-Rating and Teacher Sign-Off: Supports both self-rating and teacher sign-off for verification.
Error Line Capture and Confidence Weights: Captures error lines and assigns confidence weights for each source (self or teacher).
Use Case: When building or editing the grading flow for Hifz revision, this Skill provides a structured way to grade and validate Quranic memorization without the use of technology like microphones or AI.

Quick Start

Use the domain-grading-pipeline skill to grade a Hifz revision and capture error lines from the recitation.

Frequently Asked Questions about domain-grading-pipeline

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

FAQPage Schema

How do I grade Hifz revision without using audio or AI?▼

Grading Hifz revision without AI or audio requires a structured human evaluation pipeline that captures error lines and assigns confidence weights to self-ratings and teacher sign-offs for accurate manual verification.

What is the best way to track Quranic memorization errors during manual revision?▼

Tracking Quranic memorization errors during manual revision involves capturing specific error lines and assigning confidence weights to each grading source, maintaining traditional methods while ensuring structured feedback and grade normalization.

Can I use a self-rating system for Hifz revision that still requires a teacher sign-off?▼

Yes, this Hifz revision grading pipeline supports both self-rating and teacher sign-off, allowing students to assess their recitation while teachers validate the results and assign confidence weights for verification.

Does traditional Quranic memorization grading work without microphones or voice recognition?▼

Traditional Quranic memorization grading works without microphones or voice recognition by relying entirely on human judgment to evaluate recitations, capture errors, and normalize grades while maintaining traditional methods and privacy.

How do I normalize grades for Quranic memorization across different students and teachers?▼

Normalizing grades for Quranic memorization across students and teachers involves applying confidence weights to each grading source, balancing self-ratings and teacher evaluations to produce consistent and accurate final grades.

When should I avoid using AI for grading Hifz revision?▼

You should avoid using AI for grading Hifz revision in educational settings that prioritize traditional methods, human judgment, and privacy, requiring structured feedback and teacher verification without relying on microphones or voice recognition.