output-quality-rubrics

Installation
SKILL.md

Output Quality Rubrics

Without a rubric, quality evaluation is subjective and inconsistent. A rubric defines what "good" means in concrete, measurable terms — so different evaluators reach the same conclusions.

Core Quality Dimensions

  • Accuracy: Is the information correct? Are claims verifiable? Are there hallucinations?
  • Relevance: Does the output address what the user actually asked? Is everything included necessary?
  • Completeness: Does the output cover everything needed? Are there gaps?
  • Helpfulness: Can the user actually use this output to accomplish their goal?
  • Clarity: Is the output easy to understand? Is it well-structured?
  • Tone appropriateness: Does the output match the expected tone for the context?
  • Safety: Is the output free from harmful, biased, or inappropriate content?

Building a Rubric

For each dimension, define a scale: Example — Accuracy (1-5):

  • 5: All claims are verifiable and correct. No hallucinations.
  • 4: Minor inaccuracies that don't affect usefulness. No hallucinations.
  • 3: Some inaccuracies that could mislead if not caught. No dangerous hallucinations.
  • 2: Significant inaccuracies. User would need to verify most claims.
  • 1: Major hallucinations or factually wrong information presented confidently.

Weighting Dimensions

Not all dimensions matter equally for every use case:

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First Seen
Jun 2, 2026
output-quality-rubrics — owl-listener/ai-design-skills