llm-evaluator
LLM-as-a-Judge evaluation system using Langfuse. Score AI outputs on relevance, accuracy, hallucination, and helpfulness. Backfill scoring on historical traces. Uses GPT-5-nano for cost-efficient judging. Use when evaluating AI quality, building evals, or monitoring output accuracy.
Best use case
llm-evaluator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
LLM-as-a-Judge evaluation system using Langfuse. Score AI outputs on relevance, accuracy, hallucination, and helpfulness. Backfill scoring on historical traces. Uses GPT-5-nano for cost-efficient judging. Use when evaluating AI quality, building evals, or monitoring output accuracy.
Teams using llm-evaluator should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/llm-evaluator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How llm-evaluator Compares
| Feature / Agent | llm-evaluator | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
LLM-as-a-Judge evaluation system using Langfuse. Score AI outputs on relevance, accuracy, hallucination, and helpfulness. Backfill scoring on historical traces. Uses GPT-5-nano for cost-efficient judging. Use when evaluating AI quality, building evals, or monitoring output accuracy.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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)Related Skills
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