human-in-the-loop
Designing intervention points where humans review, approve, or redirect agent work.
适合你,如果你需要让智能体在关键步骤等待人工确认或干预
/ 通过 npx 安装 校验哈希
npx oh-my-skill add owl-listener/ai-design-skills/human-in-the-loop/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- owl-listener/ai-design-skills/human-in-the-loop/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify owl-listener/ai-design-skills/human-in-the-loop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。
144GitHub stars
~570上下文体积 · 单文件
索引托管
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 会在工作流程中设置人工干预点,在关键步骤前暂停,等你审核、批准或重定向后再继续执行。
什么时候触发
当任务涉及高风险(如不可逆操作)、系统对答案不确定,或你明确要求人工参与时触发。
装好后可以这样说
Claude 会暂停并等待你确认。
Claude 会展示详情供你判断。
Claude 会停止操作等你指令。
技能原文 SKILL.md
Human-in-the-Loop
Human-in-the-loop design defines when, where, and how humans intervene in automated workflows. Too little human involvement and the system makes dangerous mistakes. Too much and you've just built an expensive notification system.
Intervention Types
- Approval gates: The system pauses and waits for human approval before proceeding
- Review checkpoints: The system presents results for human review but can continue if no objection
- Correction opportunities: The system shows its work and the human can edit before it's finalised
- Override controls: The human can stop, redirect, or undo the system's actions at any time
- Monitoring dashboards: The human passively observes the system and intervenes only when needed
When to Require Human Intervention
- High stakes: Actions that are expensive, irreversible, or affect many people
- Low confidence: The system is uncertain about the right action
- Novel situations: The input or context is outside the system's training distribution
- Ethical judgments: Decisions that require moral reasoning or value trade-offs
- Legal requirements: Regulatory or compliance requirements mandate human review
- User request: The user explicitly asks for human involvement
Designing Intervention Points
For each intervention point:
- Trigger: What causes the intervention? (confidence threshold, stakes level, user request, policy requirement)
- Presentation: What does the human see? (summary, full context, recommendations, options)
- Time constraint: How quickly must the human respond? What happens if they don't?
- Decision options: What can the human do? (approve, reject, edit, escalate, defer)
- Feedback integration: How does the human's decision feed back into the system?
Avoiding Human Bottlenecks
Human intervention is expensive and slow. Design to minimise unnecessary intervention:
- Graduated autonomy: Start with more human oversight, reduce as the system proves reliable
- Batch review: Group similar decisions for efficient human processing
- Smart routing: Send interventions to the right human based on expertise and availability
- Default actions: If the human doesn't respond within a time window, take a safe default action
- Learning from interventions: Use human decisions to improve the system so it needs less intervention over time
Design Artefacts
- Intervention point map (where in the workflow, what triggers each)
- Intervention interface designs (what the human sees and can do)
- Time constraint specifications
- Graduated autonomy plans
- Intervention analytics specifications (tracking how often, why, and what humans decide)
按 MIT 许可原样转载,未经改动 · 在 GitHub 查看 →
评论
登录即可评论;带「已验证安装」的,是发布者名下有本店的安装或持有记录。
…