Skip to main content

general-quality

General response quality evaluation. Always applicable regardless of domain. Covers response structure, actionability, clarity, and hallucination detection.

معلومات المصدر

المستودع
databricks-solutions/ai-dev-kit
آخر نشاط في المصدر
١٦ مارس ٢٠٢٦ في ١٧:٢١
لغة SKILL.md المكتشفة
الإنجليزية
النجوم
١٬٩٥١
التفرعات
٤٢٨

خيارات التثبيت

يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.

مراجعة ملفات المصدر

اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.

عرض SKILL.md

SKILL.md
تعليمات المصدر · معاينة للقراءة فقط
name
general-quality
description
General response quality evaluation. Always applicable regardless of domain. Covers response structure, actionability, clarity, and hallucination detection.
metadata
{"category":"evaluation","version":"1.0","applies_to":[]}
## General Quality Rubric When evaluating any Claude Code agent trace, check these dimensions: ### 1. Actionable Output - Can the user directly use the response (copy-paste code, follow steps)? - Are code examples complete and runnable, not fragments? - Are file paths, table names, and API references concrete? ### 2. Structured Response - Code is in fenced code blocks with correct language tags - Steps are numbered or bulleted for multi-step tasks - Explanations are concise — answer first, context after ### 3. No Hallucination - All referenced APIs, functions, and parameters must actually exist - Databricks features must be current (not deprecated) - Tool names in traces must match real MCP tool names - No invented catalog/schema/table names unless the user specified them ### 4. Conciseness - Answers the question without excessive preamble or disclaimers - Doesn't repeat the question back - Doesn't explain basic concepts the user didn't ask about ### 5. Error Handling - If the agent encountered errors during execution, did it recover? - Did it explain errors to the user when relevant? - Did it avoid silently swallowing failures?
عرض على GitHub