菜单
Defining what "good" looks like for AI outputs — accuracy, relevance, helpfulness.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Proactively identifying failure modes, misuse, and unintended consequences.
Managing shared context, memory, and state across multiple agents.
Coordinating text, image, voice, and tool-use modalities in a single interaction.
Helping users form warranted trust in the AI — neither overtrust nor undertrust — through deliberate confidence and source signalling.
Reading user emotional state from text signals — caps, punctuation density, repetition, latency — and adapting before the user disengages.
Designing review workflows to surface and mitigate bias in AI outputs.
| name | output-quality-rubrics |
| description | Defining what "good" looks like for AI outputs — accuracy, relevance, helpfulness. |
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.
For each dimension, define a scale: Example — Accuracy (1-5):
Not all dimensions matter equally for every use case:
A rubric is only useful if evaluators use it consistently:
