task-success-metrics
Measuring whether the AI actually helped users accomplish their goals.
适合你，如果你需要量化AI助手的实际任务完成效果。
/ 通过 npx 安装 校验哈希
npx oh-my-skill add owl-listener/ai-design-skills/task-success-metrics/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- owl-listener/ai-design-skills/task-success-metrics/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify owl-listener/ai-design-skills/task-success-metrics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
安装后，Claude会引导你定义任务成功的标准（如完成目标、质量门槛），并通过完成率、耗时、交互轮次等指标，衡量AI是否真正帮你达成目标，而非只看输出质量。
什么时候触发
当你想评估AI助手是否有效完成了用户任务，或需要定义任务成功标准、分析用户行为数据时触发。
装好后可以这样说
Claude会制定度量方案。
Claude会列出成功标准。
Claude会结合指标给出建议。
技能原文 SKILL.md
Task Success Metrics
Output quality doesn't guarantee task success. The AI might produce a beautiful response that doesn't actually help the user do what they came to do. Task success metrics measure the end-to-end outcome.
Defining Task Success
For each user task, define:
- What does success look like? The user completed their goal (sent the email, found the information, finished the design)
- What are the success criteria? Specific, observable conditions that indicate the task is done
- What's the time expectation? How long should this task take with AI assistance vs. without?
- What's the quality bar? Not just done, but done well enough
Task Success Metrics
- Task completion rate: Percentage of users who complete the task (not just get a response)
- Time to completion: How long from first input to task done
- Turns to completion: How many back-and-forth exchanges needed
- First-attempt success rate: Did the AI's first response accomplish the task, or did it require iteration?
- Intervention rate: How often did the user need to correct, redirect, or override the AI?
- Abandonment rate: How often did users give up before completing the task?
Measuring Task Success
- Direct measurement: Track task completion through product analytics (user clicked "done", saved the output, moved to next step)
- Inferred measurement: Infer success from proxy signals (session length, return rate, output edits)
- Self-reported measurement: Ask users whether the AI helped them accomplish their goal
- Comparative measurement: Compare task success with AI vs. without AI, or with version A vs. version B
Task Success vs. Output Quality
These can diverge:
- High output quality, low task success: The AI's answer is well-written but doesn't address the real need
- Low output quality, high task success: The AI's answer is rough but gives the user exactly what they needed
- Both matter: Track both and investigate when they diverge
Design Artefacts
- Task success definitions per key user task
- Metrics framework with measurement methods
- Success criteria specifications
- Baseline measurements (before AI, or current version)
- Task success dashboard specifications
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