
Llm Evaluation
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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- GitHub stars
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- last updated
- Jun 3, 2026
Install
$ npx -y skills add sickn33/antigravity-awesome-skills --skill llm-evaluation --agent claude-codeInstalls 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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