How do I install the Design Human In Loop Workflow skill?

Run npx skillmds@latest add alexe-ev/design-human-in-loop-workflow in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Is Design Human In Loop Workflow free to use?

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Who published Design Human In Loop Workflow?

alexe-ev (@alexe-ev) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Design Human In Loop Workflow by alexe-ev

Name: Design Human In Loop Workflow
Author: alexe-ev

Design Human In Loop Workflow

Design workflows that appropriately balance AI automation with human oversight and intervention. Use this skill when a team is building an AI feature and needs to decide when and how humans stay in the loop.

alexe-ev dd3e1a5 5 files · 11.2 KB Updated Sep 17, 2026

File contents

Design Human-in-the-Loop Workflow

Purpose

Help teams design AI-assisted workflows that appropriately allocate tasks between AI and human judgment, with clear escalation, review, and override mechanisms.

Skill type

Conceptual skill

Use this skill when

An AI feature is being designed and human oversight needs to be structured
AI output quality is inconsistent and human review is needed
A high-stakes decision involves AI recommendations that need human validation
A team is reducing AI automation and wants to design the right handoff points

Do not use this skill when

The goal is model selection (use assess-model-capabilities)
The goal is AI quality monitoring (use evaluate-ai-quality-monitoring)

Required inputs

AI feature or workflow being designed
Stakes level (low / medium / high consequences of errors)
User segment interacting with the AI

Optional inputs

Model capability assessment
Known failure modes
Regulatory or compliance context
User trust level with AI

Upstream context

Works best when:

AI feature is defined
Model capabilities and limitations are assessed

Downstream handoff

Output can feed:

evaluate-ai-quality-monitoring
write-requirements-prd (HITL design becomes a requirement)

Instructions

Map the workflow steps where AI is involved.
For each step, assess: what happens if the AI is wrong? What are the stakes?
Determine the appropriate intervention mode: fully automated / AI-assisted human / human-reviewed AI / AI-augmented decision.
Design review, override, and escalation mechanisms.
Define when the AI should abstain or flag for human review.
Define the feedback loop: how do human corrections improve the AI?

Output

Provide:

Workflow map with AI involvement per step
Stakes assessment per step
Intervention mode recommendation per step
Review, override, and escalation design
Abstention and flagging criteria
Feedback loop design
Edge case handling

Risks / caveats

Full automation without oversight is appropriate only for low-stakes, well-validated tasks
"Human in the loop" that doesn't actually change the outcome is theater, not safety
Feedback loops are mandatory — AI without them won't improve

---name: design-human-in-loop-workflowdescription: Design workflows that appropriately balance AI automation with human oversight and intervention. Use this skill when a team is building an AI feature and needs to decide when and how humans stay in the loop.---# Design Human-in-the-Loop Workflow## PurposeHelp teams design AI-assisted workflows that appropriately allocate tasks between AI and human judgment, with clear escalation, review, and override mechanisms.## Skill typeConceptual skill## Use this skill when- An AI feature is being designed and human oversight needs to be structured- AI output quality is inconsistent and human review is needed- A high-stakes decision involves AI recommendations that need human validation- A team is reducing AI automation and wants to design the right handoff points## Do not use this skill when- The goal is model selection (use assess-model-capabilities)- The goal is AI quality monitoring (use evaluate-ai-quality-monitoring)## Required inputs- AI feature or workflow being designed- Stakes level (low / medium / high consequences of errors)- User segment interacting with the AI## Optional inputs- Model capability assessment- Known failure modes- Regulatory or compliance context- User trust level with AI## Upstream contextWorks best when:- AI feature is defined- Model capabilities and limitations are assessed## Downstream handoffOutput can feed:- evaluate-ai-quality-monitoring- write-requirements-prd (HITL design becomes a requirement)## Instructions1. Map the workflow steps where AI is involved.2. For each step, assess: what happens if the AI is wrong? What are the stakes?3. Determine the appropriate intervention mode: fully automated / AI-assisted human / human-reviewed AI / AI-augmented decision.4. Design review, override, and escalation mechanisms.5. Define when the AI should abstain or flag for human review.6. Define the feedback loop: how do human corrections improve the AI?## OutputProvide:- Workflow map with AI involvement per step- Stakes assessment per step- Intervention mode recommendation per step- Review, override, and escalation design- Abstention and flagging criteria- Feedback loop design- Edge case handling## Risks / caveats- Full automation without oversight is appropriate only for low-stakes, well-validated tasks- "Human in the loop" that doesn't actually change the outcome is theater, not safety- Feedback loops are mandatory — AI without them won't improve

Frequently asked questions