This is a solid reference for measuring LLM output quality across automated metrics (BLEU, ROUGE, BERTScore), human evaluation rubrics, and LLM-as-judge patterns. You get working Python examples for scoring translations and summaries, comparing model outputs pairwise, and building custom metrics like groundedness checks. The automated metrics are fast but often miss nuance, so the guide walks through when to layer in human ratings or use a stronger model as a judge. Most useful when you're trying to catch regressions before shipping prompt changes or need to justify which of two models actually performs better on your specific use case.

skills.sh installs
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last updated
Jun 3, 2026

Install

$ npx -y skills add sickn33/antigravity-awesome-skills --skill llm-evaluation --agent claude-code

Installs into .claude/skills of the current project.

Files

LLM Evaluation

Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing.

Do not use this skill when

  • The task is unrelated to llm evaluation
  • You need a different domain or tool outside this scope

Instructions

  • Clarify goals, constraints, and required inputs.
  • Apply relevant best practices and validate outcomes.
  • Provide actionable steps and verification.
  • Use the examples below; no separate implementation playbook is bundled.

Use this skill when

  • Measuring LLM application performance systematically
  • Comparing different models or prompts
  • Detecting performance regressions before deployment
  • Validating improvements from prompt changes
  • Building confidence in production systems
  • Establishing baselines and tracking progress over time
  • Debugging unexpected model behavior

Core Evaluation Types

1. Automated Metrics

Fast, repeatable, scalable evaluation using computed scores.

Sponsors

Details

Repository
github.com/sickn33/antigravity-awesome-skills
Category
AI & Agent Building
First seen
Jun 3, 2026

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