output-quality-rubrics
Defining what "good" looks like for AI outputs — accuracy, relevance, helpfulness.
适合你，如果你需要为AI输出制定清晰的质量准则
/ 通过 npx 安装 校验哈希
npx oh-my-skill add owl-listener/ai-design-skills/output-quality-rubrics/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- owl-listener/ai-design-skills/output-quality-rubrics/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify owl-listener/ai-design-skills/output-quality-rubrics安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。
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怎么用
商店整理自技能原文 · 版本 f41b650 · 表述以原文为准它做什么
装上后，Claude 会按照准确、相关、完整、有用、清晰、语气恰当、安全等维度来评估和优化自己的输出，并可能给出评分和改进建议。
什么时候触发
当你要求 Claude 评估输出质量，或请它按照特定标准改进回答时触发。
装好后可以这样说
Claude 会给出各维度评分和改进建议。
Claude 会自查并报告分数及理由。
技能原文 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:
- 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
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