customaize-agent:agent-evaluation

Name: customaize-agent:agent-evaluation
Availability: InStock
Author: LAI-YEN-CHUN

Evaluate and improve Claude Code agent prompts with structured evaluation workflows.

1|Updated Apr 21, 2025

Search Tags:#evaluation #LLM #agents #rubrics #prompt-engineering #bias-mitigation

Ready to run

npx skills add https://github.com/LAI-YEN-CHUN/VSCode-Settings --skill customaize-agent-agent-evaluation-lai-yen-chun

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: customaize-agent:agent-evaluation
Source: https://github.com/LAI-YEN-CHUN/VSCode-Settings/tree/main/.github/skills/agent-evaluation
Command: npx skills add https://github.com/LAI-YEN-CHUN/VSCode-Settings --skill customaize-agent-agent-evaluation-lai-yen-chun

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Traditional AI evaluation often struggles with non-determinism, context drift, and inconsistent judgments when assessing Claude Code agents. This Skill provides a structured framework to measure agent performance, surface actionable feedback, and drive continuous improvements.

Core Features & Use Cases

Multi-dimensional rubrics to evaluate factual accuracy, completeness, tool efficiency, reasoning quality, and response coherence.
Bias mitigation techniques, including position swapping, model cross-checks, and length-normalized scoring, to improve reliability.
End-to-end evaluation templates and prompts that guide both automated and human evaluation across prompt-testing, prompt-iteration, and deployment monitoring.

Quick Start

Run an initial evaluation pass on a sample Claude Code agent prompt to establish baseline metrics.

Frequently Asked Questions about customaize-agent:agent-evaluation

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

FAQPage Schema

How do I evaluate LLM agents using multi-dimensional rubrics?▼

To evaluate LLM agents using multi-dimensional rubrics, apply a structured framework measuring factual accuracy, completeness, tool efficiency, reasoning quality, and response coherence to surface actionable feedback and drive continuous prompt improvements.

What is bias mitigation in LLM evaluation and how does it work?▼

Bias mitigation in LLM evaluation reduces inconsistent judgments through techniques like position swapping, model cross-checks, and length-normalized scoring. These methods improve reliability when assessing non-deterministic agent responses and context drift.

How to test Claude Code agent prompts for context drift?▼

Test Claude Code agent prompts for context drift by running an initial evaluation pass to establish baseline metrics. Use structured evaluation templates to guide automated and human evaluation across prompt-testing and deployment monitoring.

Does this evaluation framework work for multi-agent deployments?▼

Yes, this evaluation framework works for multi-agent deployments by providing performance measurement across multiple agents. It applies context-engineering validation and structured prompts to manage non-determinism and ensure consistent judgments.

What are the limitations of evaluating non-deterministic AI agents?▼

Evaluating non-deterministic AI agents faces limitations like context drift and inconsistent judgments. Mitigate these constraints by applying bias mitigation techniques and length-normalized scoring to improve reliability during prompt iteration.