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AI/MLdvcrn

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.

Stars
15
Source
dvcrn/openclaw-skills-marketplace
Updated
2026-05-29
Slug
dvcrn--openclaw-skills-marketplace--llm-evaluator
View on GitHubRaw SKILL.md

// install — copy + paste into any project

mkdir -p .claude/skills && curl -fsSL https://raw.githubusercontent.com/dvcrn/openclaw-skills-marketplace/HEAD/plugins/aiwithabidi--llm-evaluator-pro/skills/llm-evaluator/SKILL.md -o .claude/skills/llm-evaluator.md

Drops the SKILL.md into .claude/skills/llm-evaluator.md. Works with Claude Code, Cursor, and any agent that loads SKILL.md files from .claude/skills/.

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.

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