A comprehensive framework for implementing reliable LLM-as-a-judge systems using direct scoring and pairwise comparison patterns.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install advanced-evaluation
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install advanced-evaluation using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Advanced Evaluation?
The Advanced Evaluation skill provides a professional-grade methodology for assessing Large Language Model outputs using AI-driven judgment. It moves beyond simple prompting by introducing research-backed patterns like Chain-of-Thought scoring and multi-pass consistency checks. By leveraging Openclaw Skills, developers can build evaluation systems that correlate highly with human judgment while maintaining the speed and scalability of automated pipelines.
This skill addresses the core challenges of evaluation, including position bias, length bias, and self-enhancement tendencies in models. It synthesizes academic research from sources like the MT-Bench paper into actionable workflows for software engineers and AI researchers building production LLM applications.
Advanced Evaluation Use Cases
- Building automated CI/CD pipelines to evaluate LLM output quality before deployment.
- Conducting systematic A/B testing between different model versions or prompt templates.
- Creating domain-specific rubrics for technical fields like medical, legal, or software engineering.
- Mitigating position and length bias in preference-based model selection.
- Generating high-quality datasets for fine-tuning through automated quality filtering.
How Advanced Evaluation Works
- Identify the evaluation type (Direct Scoring for objective facts or Pairwise Comparison for subjective style).
- Load or generate a structured rubric with clear criteria, weights, and scale definitions.
- Execute the primary scoring pass using Chain-of-Thought prompting to ensure evidence-based justification.
- Apply bias mitigation protocols, such as swapping response positions to eliminate seat-order bias.
- Calculate a final confidence score based on consistency across multiple passes.
- Output structured JSON data containing scores, justifications, and actionable improvement suggestions.
Advanced Evaluation Setup
To integrate Advanced Evaluation patterns into your workflow, ensure your environment is configured for structured JSON output and multi-pass logic. Use the following structure to initialize a pipeline:
# Example: Setting up a custom evaluation criteria loader
mkdir -p evaluation/rubrics
touch evaluation/rubrics/accuracy.json
Ensure your agent has access to advanced models (e.g., GPT-4o or Claude 3.5 Sonnet) to serve as the judge, as model reasoning capability directly impacts evaluation reliability.
Advanced Evaluation Data Schema & Taxonomy
The skill utilizes a structured data taxonomy to ensure evaluation results are machine-readable and ready for analytics:
| Field | Type | Description |
|---|---|---|
criterion |
String | The specific aspect being measured (e.g., Accuracy). |
score |
Integer/Float | The numerical rating based on the defined scale. |
evidence |
Array | Specific quotes or snippets from the response supporting the score. |
justification |
String | A detailed reasoning block explaining the score assignment. |
confidence |
Float | A 0.0-1.0 value representing judge certainty and consistency. |
metadata |
Object | Includes position mapping, model versions, and timestamps. |
Advanced Evaluation Advanced Features
- Position Bias Mitigation Protocol: Automatic double-pass evaluation with swapped positions and consistency checking.
- Chain-of-Thought Scoring: Forces the judge to provide evidence before assigning a numerical score, improving reliability by 15-25%.
- Panel of LLMs (PoLL): Support for multi-judge voting systems to reduce individual model idiosyncrasies.
- Domain Adaptation: Tools for generating calibrated rubrics that use specific industry terminology and safety standards.
- Confidence Calibration: Dynamic confidence scoring that lowers the rating when judges show inconsistent behavior or weak evidence.
SKILL.md
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