llm-evaluation

Overview

What is llm-evaluation?

llm-evaluation is a specialized skill for systematically testing and benchmarking large language model (LLM) applications. It enables AI and ML teams to measure LLM performance, compare models or prompts, detect regressions, and validate improvements using both automated metrics and human feedback. This skill is essential for maintaining high-quality AI systems and establishing reliable evaluation frameworks.

Who Should Use This Skill?

• AI/ML engineers and data scientists developing LLM-powered applications
• Teams responsible for prompt engineering or model selection
• QA professionals validating LLM outputs before deployment
• Anyone needing to track LLM performance over time or debug unexpected model behavior

Problems It Solves

• Provides a repeatable process for evaluating LLMs
• Supports comparison between models, prompts, or system versions
• Helps detect regressions and validate improvements
• Facilitates building confidence in production AI systems

How to Use

Installation Steps

1. Add the skill to your agent environment:

   npx skills add https://github.com/wshobson/agents --skill llm-evaluation

2. Review the main documentation in SKILL.md for a high-level workflow and evaluation strategies.

3. Explore supporting files such as README.md, AGENTS.md, and metadata.json for integration details and context.

4. Check the rules/, resources/, references/, and scripts/ directories for reusable evaluation components and helper scripts.

Core Evaluation Types

Automated Metrics

• Text Generation: BLEU, ROUGE, METEOR, BERTScore, Perplexity
• Classification: Accuracy, Precision/Recall/F1, Confusion Matrix, AUC-ROC
• Retrieval (RAG): MRR, NDCG, Precision@K, Recall@K

Human Evaluation

• Manual review for accuracy, relevance, fluency, and other subjective criteria
• Useful for aspects not easily captured by automated metrics

Adapting to Your Workflow

• Use the provided evaluation strategies as templates and adapt them to your own repository, tools, and operational requirements.
• Establish baselines and track progress over time to ensure continuous improvement.

FAQ

When is llm-evaluation a good fit?

Use llm-evaluation when you need to systematically test, compare, or validate LLM application performance, especially before deploying changes to production.

What files should I review first?

Start with SKILL.md for an overview, then check README.md and metadata.json for integration details. Explore rules/ and scripts/ for practical examples.

Does llm-evaluation support both automated and human evaluation?

Yes, it provides guidance and templates for both automated metrics and manual human review, covering a wide range of LLM evaluation needs.

How do I customize the evaluation process?

Adapt the strategies and scripts to fit your specific models, prompts, and application requirements. The skill is designed to be flexible for different AI workflows.

Where can I find more resources?

Browse the repository's file tree for additional references, helper scripts, and supporting documentation.