Agent skill · research science · aiskillstore
advanced-evaluation
This skill should be used for advanced LLM evaluation: LLM-as-judge systems, direct scoring, pairwise comparison, rubric calibration, evaluator bias mitigation, confidence scoring, and automated quality assessment.
Why this skill is useful
Provides domain-specific knowledge on evaluation methodologies and bias mitigation techniques for LLM outputs that the AI wouldn't reliably generate on its own.
What it needs
About 9k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 criteria exist (factual accuracy, instruction following, toxicity). A single LLM rates one response on a defined scale. Achieves moderate-to-high reliability for well-defined criteria. Watch for score calibration drift and inconsistent scale interpretation. Pairwise Comparison — Use for subjective preferences (tone, style, persuasiveness). An LLM compares two responses and selects the better one. Pairwise methods often correlate better with human preference than open-ended direct scoring for subjective tasks (claim-advanced-evaluation-position-swap). …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/advanced-evaluation--f2b668