llm-eval-grounded-theory

Name: llm-eval-grounded-theory
Availability: InStock
Author: rajnishkhatri

Automate LLM evaluation pipelines from trace collection to continuous monitoring.

Updated Apr 17, 2026

Search Tags:#LLM evaluation #continuous monitoring #evaluation pipeline #AI explainability #LLM calibration

Ready to run

npx skills add https://github.com/rajnishkhatri/AgentsFramework --skill llm-eval-grounded-theory

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: llm-eval-grounded-theory
Source: https://github.com/rajnishkhatri/AgentsFramework/tree/main/.cursor/skills/llm-eval-grounded-theory
Command: npx skills add https://github.com/rajnishkhatri/AgentsFramework --skill llm-eval-grounded-theory

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of building and maintaining trustworthy LLM-as-judge pipelines, ensuring accurate evaluation and continuous monitoring of AI systems.

Core Features & Use Cases

Qualitative-to-Quantitative Evaluation: Automates the process of moving from qualitative error analysis to quantitative evaluation, including open coding, axial coding, and rubric design.
LLM Calibration: Provides a framework for calibrating LLM judges against gold sets and monitoring their performance in production.
Continuous Monitoring: Offers tools for ongoing evaluation and monitoring of LLM systems, including offline regression and online drift detection.

Quick Start

Execute the skill to begin the LLM evaluation pipeline by collecting trace data and proceeding through the stages outlined in the documentation.

Frequently Asked Questions about llm-eval-grounded-theory

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

FAQPage Schema

How do I build an LLM evaluation pipeline for continuous monitoring?▼

To build an LLM evaluation pipeline, you automate stages from trace collection to calibration and continuous monitoring. This process includes qualitative error analysis, rubric design, and offline regression testing for robust AI system explainability.

What is LLM calibration and how does it work for evaluation datasets?▼

LLM calibration is the process of aligning LLM-as-judge outputs against gold sets. It works by evaluating the judge's performance on known data, ensuring accurate quantitative scoring and reliable continuous monitoring in production environments.

How do I convert qualitative error analysis into quantitative LLM evaluation?▼

You convert qualitative error analysis into quantitative LLM evaluation by applying open coding and axial coding methodologies. This structured approach allows you to design scoring rubrics that translate observed errors into measurable performance metrics.

Can I use Python scripts for AI explainability in regulated environments?▼

Yes, you can use Python scripts for AI explainability in regulated environments. The framework provides processing and reporting tools specifically designed to ensure robust evaluation and compliance monitoring for AI systems.

What is the best way to detect online drift in LLM systems?▼

The best way to detect online drift in LLM systems is through continuous monitoring pipelines. By calibrating your LLM judge against a gold set and tracking production outputs, you can automatically identify performance regressions and drift.

Why do I need offline regression testing for my LLM-as-judge pipeline?▼

You need offline regression testing for your LLM-as-judge pipeline to maintain trustworthiness over time. It validates that updates or changes to the model do not degrade evaluation accuracy and explainability before production deployment.