skills.rest
spideynolove
spideynoloveCOMMUNITY
Explore all skills by this author →
Website𝕏@spideynoloveGitHub

llm-evaluation

Evaluate LLM outputs with automated metrics and human evaluation checklists.

2|1|Updated Feb 27, 2026
Search Tags:#llm#metrics#nlp#benchmark#evaluate#human-evaluation
One-click install
npx skills add https://github.com/spideynolove/claude-code-in-action --skill llm-evaluation-spideynolove
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-evaluation
Source: https://github.com/spideynolove/claude-code-in-action/tree/main/27-tdd-conductor-llmdev/.claude/skills/llm-evaluation
Command: npx skills add https://github.com/spideynolove/claude-code-in-action --skill llm-evaluation-spideynolove

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automatically evaluate LLM outputs by combining automated metrics, human feedback, and benchmarking to quantify performance and guide improvements.

Core Features & Use Cases

  • Automated Metrics: BLEU, ROUGE, METEOR, BERTScore, perplexity, and retrieval metrics.
  • Human Evaluation: Dimensional ratings (accuracy, relevance, clarity, safety) with a structured checklist.
  • Benchmarking & Guardrails: A/B testing, LangSmith integration, and baseline tracking for continuous improvement.

Quick Start

Run a quick evaluation on your latest model using prebuilt metrics and a human-judgment checklist.

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 outputs systematically for model comparisons?▼

To evaluate LLM outputs systematically, you can combine automated metrics like BLEU and ROUGE with human evaluation guidelines and benchmarking workflows to produce repeatable performance assessments for model comparisons.

What automated metrics can I use to measure LLM quality?▼

You can measure LLM quality using automated metrics including BLEU, ROUGE, METEOR, BERTScore, perplexity, and retrieval metrics to quantify performance and guide improvements.

How do I set up human evaluation for LLM accuracy and safety?▼

To set up human evaluation for LLM accuracy and safety, apply a structured checklist providing dimensional ratings for accuracy, relevance, clarity, and safety to systematically assess outputs.

Does LangSmith integration work with LLM benchmarking workflows?▼

LangSmith integration works with LLM benchmarking workflows by supporting A/B testing and baseline tracking to monitor production performance and enable continuous improvement.

What is the best way to run A/B testing for prompt tuning?▼

The best way to run A/B testing for prompt tuning is to use benchmarking workflows that track baselines and apply automated metrics alongside human feedback to quantify performance changes.

Can I monitor production LLM performance using prebuilt evaluation metrics?▼

You can monitor production LLM performance by applying prebuilt automated evaluation metrics and human-judgment checklists to continuously benchmark output quality and track baselines.

Related Skills

Explore More Data & Analytics Skills→
surus-latsurus-lat
8

synthesize-data

Generate synthetic benchmark datasets as JSONL input-output pairs from task specifications.

Official
Intermediate
hkcm91hkcm91

self-improving-ai

Automate AI self-evaluation and prompt refinement cycles for continuous improvement.

Community
Advanced
vadimcomanescuvadimcomanescu
24

agents-langchain

Build LangChain agents and RAG pipelines for LLM applications.

Community
Advanced
GamezarGamezar

sadd-judge

Launch an isolated sub-agent judge to score conversation work with evidence-based rubrics.

Community
Advanced