advanced-evaluation

Implements LLM-as-judge evaluation pipelines with bias mitigation and rubric generation.

Updated Feb 11, 2026
One-click install
npx skills add https://github.com/lalalala5678/policy-kg-pipeline --skill advanced-evaluation-lalalala5678
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: advanced-evaluation
Source: https://github.com/lalalala5678/policy-kg-pipeline/tree/main/.tmp_skill_install_test_3/advanced-evaluation
Command: npx skills add https://github.com/lalalala5678/policy-kg-pipeline --skill advanced-evaluation-lalalala5678

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve? Automated evaluation of LLM outputs is unreliable when judges exhibit position bias, length bias, or inconsistent scoring, making it hard to trust quality assessments or compare models fairly. ## Core Features & Use Cases - Direct Scoring and Pairwise Comparison: Choose the right evaluation approach for objective criteria versus subjective preferences, with structured JSON output formats. - Bias Mitigation Protocols: Apply position swapping, length normalization, cross-model evaluation, and evidence requirements to counter known LLM judge biases. - Rubric Generation: Create domain-specific scoring rubrics with level descriptions, edge cases, and strictness calibration to reduce evaluation variance. - Use Case: When comparing two model responses for a prompt, run pairwise comparison with position swapping to detect position bias and produce a calibrated confidence score. ## Quick Start Ask the AI to compare two model responses to a prompt using pairwise comparison with position bias mitigation and return a winner with confidence.

Frequently Asked Questions about advanced-evaluation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I implement LLM-as-a-judge evaluation?

Define clear criteria with weights, build a scoring prompt requiring evidence-based justification before scores, and parse structured JSON output. Use direct scoring for objective criteria like accuracy and pairwise comparison for subjective preferences like tone.

How to mitigate position bias in pairwise LLM comparison?

Run the comparison twice with swapped response positions, then check consistency. If both passes agree on the winner, average the confidences; if they disagree, return a tie with reduced confidence since position bias influenced the result.

When should I use direct scoring vs pairwise comparison?

Use direct scoring when objective ground truth exists, such as factual accuracy or instruction following. Use pairwise comparison for subjective preference judgments like tone, style, or persuasiveness, where it achieves higher agreement with human judges.

What metrics measure agreement between LLM judges and humans?

Use Spearman's rho or Kendall's tau for ordinal score correlation, Cohen's kappa for categorical judgments, and agreement rate plus position consistency for pairwise comparisons. Values above 0.8 for Spearman's rho indicate strong alignment.

Why do LLM judges give inconsistent evaluation scores?

Inconsistency stems from position bias, length bias, vague criteria, and missing rubrics. Fix this by requiring justification before scores, swapping positions in comparisons, and generating detailed rubrics with level descriptions and edge cases.