Can I monitor these metrics in production?

The skill includes YAML-based monitoring configurations for tracking accuracy, success rates, and latency using standard observability tools.

How does it help with AI agent development?

The skill provides a benchmark suite to measure key agent metrics like success rate, resource usage (tokens/API calls), and time-to-complete across different iterations.

Does it support statistical A/B testing?

Yes, it includes an experiment design framework and statistical analysis tools to calculate lift, p-values, and confidence intervals for different model versions.

What is the 'LLM-as-judge' feature?

It is a methodology where a high-performing LLM evaluates the output of another model or agent based on specific rubrics, providing quantitative scores and qualitative reasoning.

Can I use this for traditional software testing?

Yes, it includes frameworks for code quality assessment that cover architectural design, security vulnerabilities, performance efficiency, and maintainability.

AI Evaluation & Quality Frameworks

Name: AI Evaluation & Quality Frameworks
Author: bradtaylorsf

bybradtaylorsf

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Security & Testing

Implements comprehensive assessment methodologies and rubrics for evaluating LLM responses, code quality, and agent performance.

The Evaluation Frameworks skill provides a robust toolkit for measuring the effectiveness and reliability of AI systems and software. It includes detailed rubrics for LLM response quality, 'LLM-as-judge' implementation patterns, automated code quality scoring, and agent benchmark suites. By integrating statistical A/B testing and continuous monitoring configurations, this skill enables developers to move beyond vibes-based assessments to data-driven quality assurance for complex AI-driven applications and agents.

Key Features

01Comprehensive code quality rubrics covering SOLID, security, and performance

02LLM-as-Judge evaluation prompts and automated scoring logic

03Statistical A/B testing framework with hypothesis and significance analysis

04Agent benchmark suites for measuring task success, latency, and token efficiency

056 GitHub stars

06Continuous evaluation monitoring with predefined metrics and alerting rules

Use Cases

01Comparing the performance of different agent architectures using standardized benchmarks

02Automating code reviews based on logic, security, and maintainability rubrics

03Establishing a systematic QA process for LLM-powered application outputs

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add bradtaylorsf/alphaagent-team evaluation-frameworks

For use in Claude.ai and ChatGPT

Download Skill