Create and run LLM evaluation pipelines with OpenJudge graders.
npx skills add https://github.com/agentscope-ai/OpenJudge --skill openjudge
Please help me install this Agent Skill. Skill: openjudge Source: https://github.com/agentscope-ai/OpenJudge/tree/main/skills/openjudge Command: npx skills add https://github.com/agentscope-ai/OpenJudge --skill openjudge
This Skill streamlines the process of evaluating AI application outputs, enabling users to build robust quality assessment pipelines and drive continuous optimization.
Use the openjudge skill to evaluate LLM responses for correctness using a provided reference.
High-intent search queries and answers about installing and using this skill.
To build an LLM evaluation pipeline, you select, configure, and run various graders over datasets, combining scores with aggregators to systematically assess AI application outputs for correctness and relevance.
Automating LLM grading involves using pre-built or custom graders—such as LLM-based, function-based, or agentic graders—to automatically assess outputs against defined rubrics for correctness, hallucination, and relevance.
Yes, you can generate data-driven evaluation rubrics automatically from your dataset, allowing you to define grading criteria dynamically and rigorously evaluate chatbot responses against test queries.
Yes, the framework supports combining multiple grader scores using aggregators, enabling you to run graders over datasets and analyze the combined results to identify areas for continuous optimization.
You can configure LLM-based, function-based, and agentic graders for quality assurance, allowing flexible assessment of LLM outputs for correctness, relevance, and hallucination across various datasets.
0xFANGOSet up videocut dependencies and download models for offline operation.
baekenoughEvaluate software engineering tasks with a 15-task benchmark and scoring rubric.
DevelopersGlobalAutomate CI/CD pipelines with quality gates and deployment processes.
ArogyaReddyAutomates LLM output evaluation with metrics, human feedback, and benchmarking.
