user-satisfaction-signals
Interpreting implicit and explicit feedback — edits, regenerations, abandonment.
适合你，如果你需要从用户编辑、重试等行为中挖掘改进方向
/ 通过 npx 安装 校验哈希
npx oh-my-skill add owl-listener/ai-design-skills/user-satisfaction-signals/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- owl-listener/ai-design-skills/user-satisfaction-signals/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify owl-listener/ai-design-skills/user-satisfaction-signals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
Claude 能够分析用户的操作行为（如编辑、重新生成、放弃任务等），从中推断用户的满意度，并给出解读。
什么时候触发
当用户与 AI 交互后留下隐式或显式反馈（如操作、评分、评论）时触发。
装好后可以这样说
Claude 会分析编辑程度并解读为不满或需要调整。
Claude 会识别这是负面信号，表示用户不满意。
Claude 会解释沉默可能是满意或困惑，需结合其他信号。
技能原文 SKILL.md
User Satisfaction Signals
Users rarely tell you directly whether they're satisfied. Most satisfaction signals are implicit — buried in behavior patterns that you have to design systems to capture and interpret.
Explicit Satisfaction Signals
These are signals users give intentionally:
- Thumbs up/down: Direct quality rating
- Star ratings: Graded satisfaction
- Written feedback: Comments about what worked or didn't
- NPS or satisfaction surveys: Periodic overall assessment
- Feature requests: Signals of engagement even when expressing a gap
Implicit Satisfaction Signals
These are behavioral signals that indicate satisfaction or dissatisfaction: Positive signals:
- Using the output as-is (no edits)
- Copying the output
- Returning to use the feature again
- Increasing usage over time
- Trying more advanced features
Negative signals:
- Regenerating the response (asking the AI to try again)
- Editing the output heavily
- Rephrasing the same request multiple times
- Abandoning mid-task
- Decreasing usage over time
- Switching to manual methods
Ambiguous signals:
- Long sessions (engaged or struggling?)
- Many turns (deep work or frustrated iteration?)
- Silence after a response (satisfied or confused?)
Designing Signal Collection
- Instrument the product: Track edits, regenerations, copy events, session duration, and return patterns
- Minimise explicit feedback burden: Don't ask for ratings on every response
- Contextualise signals: A regeneration during creative brainstorming means something different than a regeneration during fact-finding
- Segment by task type: Satisfaction patterns vary by what the user is trying to do
- Combine signals: No single signal is reliable. Look for patterns across multiple signals.
From Signals to Insights
Raw signals need interpretation:
- Signal clustering: Which negative signals appear together? That pattern indicates a specific problem.
- Trend analysis: Are signals improving or degrading over time?
- Cohort comparison: Do new users show different signals than experienced users?
- Correlation with outcomes: Which signals best predict task success or retention?
Design Artefacts
- Signal inventory (explicit and implicit) with collection methods
- Signal interpretation guidelines
- Satisfaction dashboard specifications
- Signal-to-insight analysis frameworks
- Feedback collection touchpoint map
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