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llm-evaluation

Evaluate LLM systems with automated metrics, human feedback, and benchmarking.

Updated Aug 27, 2026
Search Tags:#benchmarking#metrics#nlp#model-evaluation#llm-evaluation#human-evaluation#quality-assessment
One-click install
npx skills add https://github.com/cptunderpantsmoons/contract-hubv1 --skill llm-evaluation-cptunderpantsmoons
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/cptunderpantsmoons/contract-hubv1/tree/main/.claude/skills/llm-evaluation
Command: npx skills add https://github.com/cptunderpantsmoons/contract-hubv1 --skill llm-evaluation-cptunderpantsmoons

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Implement comprehensive evaluation strategies for LLM applications, enabling systematic measurement of model performance, reliability, and safety using automated metrics, human feedback, and benchmarking.

Core Features & Use Cases

  • Automated Metrics: fast, repeatable scoring for text generation, classification, and retrieval tasks.
  • Human Evaluation: structured qualitative assessment across accuracy, relevance, coherence, and safety.
  • Benchmarking & Regression: establish baselines, track progress over time, and detect regressions across models or prompts.
  • Use Case: compare two prompts for an LLM and quantify improvements with both automated scores and human judgments.

Quick Start

Run a baseline evaluation of an LLM using automated metrics and human judgments to benchmark performance.

Frequently Asked Questions about llm-evaluation

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

FAQPage Schema
How do I evaluate LLM performance using automated metrics and human feedback?▼

LLM evaluation combines automated metrics like BLEU, ROUGE, and F1 with structured human feedback to gauge quality and robustness. Apply this to model comparisons, prompt variation assessment, and regression checks across development and production environments.

What is the best way to compare two LLM prompts and quantify improvements?▼

Comparing two LLM prompts involves applying automated metrics for fast, repeatable scoring and structured human evaluation to quantify improvements. This establishes baselines to track progress and detect regressions across models or prompts.

Can I use benchmarking to detect regressions in my LLM application?▼

Yes, benchmarking detects regressions in LLM applications by establishing baselines and tracking progress over time. Apply regression checks across models or prompts in development and production environments to ensure reliability.

When do I need to run regression checks on my text generation models?▼

Run regression checks on text generation models when tracking progress over time across development and production environments. This ensures new models or prompt variations do not degrade established baseline performance and reliability.

Does LLM evaluation support integration with logging and versioning systems?▼

Yes, LLM evaluation supports integration with logging and versioning systems. This allows systematic measurement of model performance, reliability, and safety while tracking baselines and regressions across development and production environments.

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