general-quality

Name: general-quality
Availability: InStock
Author: leary-poken

Evaluate AI-generated responses using a rubric for clarity, accuracy, and safety.

1|Updated Mar 17, 2026

Search Tags:#clarity #evaluation #safety #structure #rubric #hallucination #ai-response

Ready to run

npx skills add https://github.com/leary-poken/ai-dev-kit --skill general-quality

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: general-quality
Source: https://github.com/leary-poken/ai-dev-kit/tree/main/.test/eval-criteria/general-quality
Command: npx skills add https://github.com/leary-poken/ai-dev-kit --skill general-quality

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides a structured framework to evaluate AI-generated responses for actionability, clarity, structure, and truthfulness, reducing hallucinations and inconsistent outputs.

Core Features & Use Cases

Evaluation rubric covering Actionable Output, Structured Response, No Hallucination, Conciseness, and Error Handling.
Scenario-based assessments across domains to identify gaps in guidance and safety alignment.
Guidance for iterative feedback to improve agent responses in production settings.

Quick Start

Provide a structured evaluation of the latest AI reply using the rubric and report actionable improvements.

Frequently Asked Questions about general-quality

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

FAQPage Schema

How do I evaluate AI responses for clarity and accuracy?▼

You can evaluate AI responses using a rubric-based scoring framework that audits actionable output, structured response, conciseness, error handling, and resistance to hallucinations across various domains.

What is a rubric for assessing AI response quality?▼

An AI response quality rubric is a structured evaluation framework that scores outputs on actionability, structure, conciseness, error handling, and hallucination resistance to identify guidance gaps and safety issues.

How do I check AI outputs for hallucinations and safety issues?▼

You check AI outputs for hallucinations and safety issues using scenario-based assessments that apply evaluation rubrics to identify gaps in guidance and verify safety alignment in production settings.

Can I use a rubric to evaluate customer support AI replies?▼

Yes, you can use this rubric to evaluate customer support AI replies, as it applies to a broad range of domains including customer support, coding assistants, and knowledge work to audit clarity and accuracy.

How do I improve AI agent responses in production?▼

You improve AI agent responses in production by applying iterative feedback based on rubric evaluations that highlight areas needing better actionability, conciseness, and error handling to reduce inconsistent outputs.

What are the limitations of using a rubric to evaluate AI responses?▼

A limitation of using a rubric to evaluate AI responses is that it requires scenario-based assessments and iterative feedback to effectively address safety alignment and hallucination gaps across diverse domains.