Evaluate LLM pipeline outputs against gold standard datasets using LLM-as-judge scoring.
npx skills add https://github.com/Tony363/SuperClaude --skill sc-evaluate
Please help me install this Agent Skill. Skill: sc-evaluate Source: https://github.com/Tony363/SuperClaude/tree/main/.claude/skills/sc-evaluate Command: npx skills add https://github.com/Tony363/SuperClaude --skill sc-evaluate
This Skill automates the process of evaluating the quality of LLM-generated outputs against a set of predefined standards, identifying weaknesses and suggesting improvements.
Run a full evaluation of all test cases and pipeline steps using the default judge model.
High-intent search queries and answers about installing and using this skill.
LLM-as-judge evaluation scores LLM outputs against gold standard datasets across weighted dimensions. It pinpoints underperforming pipeline steps and generates actionable prompt improvement recommendations.
Automating LLM evaluation involves running pipeline outputs against gold standard datasets using LLM-as-judge scoring. This identifies weaknesses in specific cases and provides concrete prompt improvement suggestions.
Yes, performance analysis identifies specific steps or test cases where the LLM performs poorly. It evaluates output quality across weighted dimensions to pinpoint exactly where the pipeline fails.
By evaluating LLM outputs against gold standard datasets, the analysis generates actionable prompt improvement recommendations based on identified weaknesses and underperforming test cases.
Yes, the evaluation orchestrates LLM pipeline analysis and integrates with PAL and Rube MCPs for advanced performance analysis and automated reporting.
You need LLM quality assurance when testing new prompt variations before deployment, ensuring consistent output quality and identifying areas for refinement against predefined standards.
vecearAutomate LLM evaluation with golden datasets, rubrics, thresholds, and regression reports.
yiwei79Evaluate and refine agent outputs through structured reflection loops.
Tony363Analyze code for quality, security, performance, and architecture issues.
simplysmartaiEvaluate LLM applications with automated metrics, human feedback, and LLM-as-judge patterns.
