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llm-evaluator

LLM-as-a-Judge evaluator via Langfuse. Scores traces on relevance, accuracy, hallucination, and helpfulness using GPT-5-nano as judge. Supports single trace scoring, batch backfill, and test mode. Integrates with Langfuse dashboard for observability. Triggers: evaluate trace, score quality, check accuracy, backfill scores, test evaluator, LLM judge.

aiwithabidi0 来源安装数☆ 0 仓库 Starsv1.0.0

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

SkillHub CLI

skillhub install aiwithabidi/llm-evaluator-pro

打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent,让它先读取说明、核对依赖,并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

LLM Evaluator ⚖️

LLM-as-a-Judge evaluation system powered by Langfuse. Uses GPT-5-nano to score AI outputs.

When to Use

  • Evaluating quality of search results or AI responses
  • Scoring traces for relevance, accuracy, hallucination detection
  • Batch scoring recent unscored traces
  • Quality assurance on agent outputs

Usage

# Test with sample cases
python3 {baseDir}/scripts/evaluator.py test

# Score a specific Langfuse trace
python3 {baseDir}/scripts/evaluator.py score <trace_id>

# Score with specific evaluator only
python3 {baseDir}/scripts/evaluator.py score <trace_id> --evaluators relevance

# Backfill scores on recent unscored traces
python3 {baseDir}/scripts/evaluator.py backfill --limit 20

Evaluators

Evaluator Measures Scale
relevance Response relevance to query 0–1
accuracy Factual correctness 0–1
hallucination Made-up information detection 0–1
helpfulness Overall usefulness 0–1

Credits

Built by M. Abidi | agxntsix.ai YouTube | GitHub Part of the AgxntSix Skill Suite for OpenClaw agents.

📅 Need help setting up OpenClaw for your business? Book a free consultation

来源信息

Source
github
Repository
openclaw/skills
Path
skills/aiwithabidi/llm-evaluator-pro
Version
1.0.0
Commit
b949d889520dfc2053605ef1f4c14304a9c575cd
分类来源
rules
状态
active
可见性
public

这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。

来源内容

SKILL.md
# LLM Evaluator ⚖️

LLM-as-a-Judge evaluation system powered by Langfuse. Uses GPT-5-nano to score AI outputs.

## When to Use

- Evaluating quality of search results or AI responses
- Scoring traces for relevance, accuracy, hallucination detection
- Batch scoring recent unscored traces
- Quality assurance on agent outputs

## Usage

```bash
# Test with sample cases
python3 {baseDir}/scripts/evaluator.py test

# Score a specific Langfuse trace
python3 {baseDir}/scripts/evaluator.py score <trace_id>

# Score with specific evaluator only
python3 {baseDir}/scripts/evaluator.py score <trace_id> --evaluators relevance

# Backfill scores on recent unscored traces
python3 {baseDir}/scripts/evaluator.py backfill --limit 20
```

## Evaluators

| Evaluator | Measures | Scale |
|-----------|----------|-------|
| relevance | Response relevance to query | 0–1 |
| accuracy | Factual correctness | 0–1 |
| hallucination | Made-up information detection | 0–1 |
| helpfulness | Overall usefulness | 0–1 |

## Credits

Built by [M. Abidi](https://www.linkedin.com/in/mohammad-ali-abidi) | [agxntsix.ai](https://www.agxntsix.ai)
[YouTube](https://youtube.com/@aiwithabidi) | [GitHub](https://github.com/aiwithabidi)
Part of the **AgxntSix Skill Suite** for OpenClaw agents.

📅 **Need help setting up OpenClaw for your business?** [Book a free consultation](https://cal.com/agxntsix/abidi-openclaw)

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