advanced-evaluationlisted

# Advanced Evaluation This skill covers production-grade techniques for evaluating LLM outputs using LLMs as judges. It synthesizes research from academic papers, industry practices, and practical implementation experience into actionable patterns for building reliable evaluation systems. **Key insight**: LLM-as-a-Judge is not a single technique but a family of approaches, each suited to different evaluation contexts. Choosing the right approach and mitigating known biases is the core competency this skill develops. ## When to Activate Activate this skill when: - Building LLM-as-judge systems for LLM outputs - Comparing multiple model responses to select the best one - Establishing consistent quality standards across evaluation teams - Debugging evaluation systems that show inconsistent results - Designing A/B tests for prompt or model changes - Creating rubrics specifically for LLM or human/LLM hybrid judges - Analyzing correlation between automated and human judgments Do not activate this skill for adjacent work owned by other skills: - General deterministic checks, regression suites, production quality gates, or outcome metrics: `evaluation`. - Autonomous loop governance, locked rubrics, rollback, or PR approval boundaries: `harness-engineering`. - Tool API contracts for evaluation tools: `tool-design`. ## Core Concepts ### The Evaluation Taxonomy Select between two primary approaches based on whether ground truth exists: **Direct Scoring** — Use when objective c