Individual skill
01-graders-and-pipeline
Build custom LLM evaluation pipelines using the OpenJudge framework. Covers selecting and configuring graders (LLM-based, function-based, agentic), running batch evaluations with GradingRunner, combining scores with aggregators, applying evaluation strategies (voting, average), auto-generating graders from data, and analyzing results (pairwise win rates, statistics, validation metrics). Use when the user wants to evaluate LLM outputs, compare multiple models, design scoring criteria, or build an automated evaluation system.
skills/openjudge-core/01-graders-and-pipeline/SKILL.md
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- alignment Repository topic
- reward Repository topic
- reward-model Repository topic
- rlhf Repository topic
- agent Repository topic
- evaluation Repository topic
- grader Repository topic
- llm Repository topic
- agent-skills Repository topic
- ai-agent Repository topic
- data Derived category
- design Derived category
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SKILL.mdcore · 5,277 bytesSKILL.mdcore · 5,277 bytesSKILL.mdcore · 5,277 bytesSKILL.mdcore · 5,277 bytesSKILL.mdcore · 5,277 bytesanalyzer.mddoc · 9,171 bytesgenerator.mddoc · 8,053 bytesgraders.mddoc · 16,324 bytespipeline.mddoc · 8,913 bytes
Read excerpt
# OpenJudge Skill
Build evaluation pipelines for LLM applications using the `openjudge` library.
## When to Use This Skill
- User wants to evaluate LLM output quality (correctness, relevance, hallucination, etc.)
- User wants to compare two or more models and rank them
- User wants to design a scoring rubric and automate evaluation
- User wants to analyze evaluation results statistically
- User wants to build a reward model or quality filter
## Sub-documents — Read When Relevant