eval-audit

Audit LLM evaluation pipelines for flaws in error analysis and evaluator design.

1.6k|164|Updated Mar 1, 2026

Search Tags:#quality assurance #debugging #audit #llm #evaluation #pipeline

Ready to run

npx skills add https://github.com/hamelsmu/evals-skills --skill eval-audit-hamelsmu

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: eval-audit
Source: https://github.com/hamelsmu/evals-skills/tree/main/skills/eval-audit
Command: npx skills add https://github.com/hamelsmu/evals-skills --skill eval-audit-hamelsmu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill identifies critical flaws in your LLM evaluation pipelines, ensuring your metrics are trustworthy and your AI product is genuinely improving.

Core Features & Use Cases

Diagnostic Checks: Assesses six key areas: Error Analysis, Evaluator Design, Judge Validation, Human Review Process, Labeled Data, and Pipeline Hygiene.
Prioritized Findings: Delivers a report of problems ordered by their impact on your evaluation's reliability.
Actionable Next Steps: Provides concrete recommendations, often suggesting other skills to fix identified issues.
Use Case: You've inherited an LLM evaluation system and are unsure if its results are reliable. Run eval-audit to get a clear picture of potential issues and a roadmap for improvement.

Quick Start

Use the eval-audit skill to audit my current LLM evaluation pipeline.

Frequently Asked Questions about eval-audit

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

FAQPage Schema

How do I audit my LLM evaluation pipeline for reliability issues?▼

To audit an LLM evaluation pipeline, you need to assess error analysis, evaluator design, judge validation, human review processes, labeled data, and pipeline hygiene. This process identifies critical flaws and delivers prioritized findings to ensure metrics are trustworthy.

What is LLM evaluator design and how does judge validation work?▼

LLM evaluator design involves structuring how models assess outputs, while judge validation verifies that the judging model produces accurate and consistent results. Auditing these areas ensures your automated evaluation metrics genuinely reflect product quality.

Do I need labeled data and eval traces to audit my LLM evaluation system?▼

Yes, auditing an LLM evaluation system requires access to eval artifacts like traces, evaluator configs, and labeled data. These inputs are necessary to diagnose pipeline hygiene and identify flaws in human review processes.

What's the best way to identify flaws in an inherited LLM evaluation system?▼

The best way to identify flaws in an inherited LLM evaluation system is to run a comprehensive diagnostic audit across six key areas. This generates a prioritized report of reliability issues and provides actionable next steps for remediation.

Why does my LLM evaluation pipeline produce inconsistent metrics?▼

An LLM evaluation pipeline produces inconsistent metrics due to critical flaws in evaluator design, judge validation, or labeled data quality. Auditing the pipeline hygiene and human review processes helps identify the root cause of the inconsistency.

Can I get actionable recommendations after auditing my LLM eval pipeline?▼

Yes, auditing your LLM eval pipeline provides actionable next steps ordered by impact. It often recommends specific skills for remediation, giving you a clear roadmap to fix identified issues in error analysis and evaluator design.