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output-quality-rubrics

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Defining what "good" looks like for AI outputs — accuracy, relevance, helpfulness.

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I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/Owl-Listener/ai-design-skills/blob/HEAD/claude-plugin/evaluation/skills/output-quality-rubrics/SKILL.md

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Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

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:

  • A medical AI weights accuracy and safety highest
  • A creative writing AI weights helpfulness and tone highest
  • A coding AI weights accuracy and completeness highest
  • A customer service AI weights tone and helpfulness highest Define weights when creating the rubric. Make the priorities explicit.

Rubric Calibration

A rubric is only useful if evaluators use it consistently:

  • Anchor examples: Provide sample outputs at each score level
  • Calibration sessions: Have multiple evaluators score the same outputs and discuss disagreements
  • Inter-rater reliability: Measure agreement between evaluators and refine the rubric until agreement is high
  • Edge case guidance: Document how to score ambiguous cases

Design Artefacts

  • Scoring rubric with dimension definitions and scales
  • Anchor examples at each score level
  • Dimension weighting specifications per use case
  • Calibration session protocols
  • Scoring templates and checklists