Designs human-in-the-loop review points for DAG workflows. Determines what to present to the human, how to collect feedback, and how to route approve/reject/modify decisions back into the DAG.
Scanned 5/29/2026
Install via CLI
openskills install curiositech/some_claude_skills---
name: human-gate-designer
description: Designs human-in-the-loop review points for DAG workflows. Determines what to present to the human, how to collect feedback, and how to route approve/reject/modify decisions back into the DAG.
Use when adding approval gates, designing review UX, or handling human feedback in agent workflows. Activate on "human review", "approval gate", "human-in-the-loop", "human gate", "approval workflow",
"user review step". NOT for executing human gates at runtime (use dag-runtime with Temporal signals), general UX design, or chatbot conversation design.
allowed-tools: Read,Write,Edit
metadata:
category: Design & Creative
tags:
- human
- gate
- designer
- human-review
- approval-gate
pairs-with:
- skill: task-decomposer
reason: Task decomposition identifies which DAG nodes require human approval gates
- skill: output-contract-enforcer
reason: Human gate inputs and outputs need schema validation before routing decisions
- skill: reactflow-expert
reason: Human gates are visualized as interactive approval nodes in DAG dashboards
---
# Human Gate Designer
Designs human-in-the-loop review points in DAG workflows: what to present, how to collect feedback, how to route decisions back into the DAG.
---
## When to Use
✅ **Use for**:
- Deciding WHERE in a DAG to place human gates
- Designing WHAT the human sees at each gate
- Defining HOW feedback routes back (approve/reject/modify)
- Balancing automation speed with human oversight
❌ **NOT for**:
- Runtime execution of human gates (use `dag-runtime` + Temporal signals)
- General UI/UX design (use design skills)
- Chatbot conversation flow (different pattern)
---
## Gate Placement Decision Tree
```mermaid
flowchart TD
A{Is the action irreversible?} -->|Yes| G1[Gate BEFORE the action]
A -->|No| B{Is output user-facing?}
B -->|Yes| G2[Gate AFTER generation, BEFORE delivery]
B -->|No| C{Cost > $0.50 for remaining nodes?}
C -->|Yes| G3[Gate at the cost threshold]
C -->|No| D{Confidence score < 0.7?}
D -->|Yes| G4[Gate on low-confidence outputs]
D -->|No| N[No gate needed]
```
### Where to Place Gates
| Situation | Gate Position | Why |
|-----------|-------------|-----|
| Irreversible action (deploy, send email, submit) | Before the action | Can't undo |
| User-facing deliverable (report, website, PR) | After generation, before delivery | Quality check |
| High cost remaining (>$0.50) | Before expensive phase | Budget confirmation |
| Low confidence output (<0.7) | After the uncertain node | Expert judgment needed |
| Ambiguous task decomposition | After planning, before execution | Validate the plan |
| First run of a new template DAG | After each phase | Build trust gradually |
---
## Gate Presentation Design
### What the Human Sees
```
┌──────────────────────────────────────────────────────┐
│ 🔍 Human Review: [Node Name] │
│ │
│ Context: [1-2 sentences: what happened so far] │
│ │
│ Output to Review: │
│ ┌──────────────────────────────────────────────────┐│
│ │ [The node's output, formatted for readability] ││
│ │ [Key decisions highlighted] ││
│ │ [Confidence: 0.82] ││
│ └──────────────────────────────────────────────────┘│
│ │
│ Cost so far: $0.08 / $0.50 budget │
│ Remaining nodes: 4 (est. $0.12) │
│ │
│ [✅ Approve] [✏️ Modify] [❌ Reject] │
│ │
│ If modifying, what should change? │
│ ┌──────────────────────────────────────────────────┐│
│ │ [text input for human feedback] ││
│ └──────────────────────────────────────────────────┘│
└──────────────────────────────────────────────────────┘
```
### Presentation Principles
1. **Show context, not just output**: The human needs to understand what the DAG has done so far, not just the current node's result.
2. **Highlight decisions**: Bold or annotate the choices the agent made. These are what the human is actually reviewing.
3. **Show confidence**: If the agent was uncertain, say so. Low-confidence outputs need more scrutiny.
4. **Show cost**: The human should know what they've spent and what's remaining.
5. **Make "Modify" easy**: A text input for feedback that gets injected into the retry prompt.
---
## Feedback Routing
```mermaid
flowchart TD
H[Human decision] --> A{Decision?}
A -->|Approve| C[Continue to next wave]
A -->|Modify| M[Re-execute node with human feedback injected]
M --> V[Validate modified output]
V --> H
A -->|Reject| R{Reject scope?}
R -->|This node only| RN[Re-plan this node with different approach]
RN --> H
R -->|Entire phase| RP[Re-plan from last successful phase]
RP --> H
R -->|Abort DAG| AB[Stop execution, return partial results]
```
### Feedback Injection
When the human selects "Modify," their text becomes part of the re-execution prompt:
```
Original task: [same as before]
Previous output: [the output the human rejected]
Human feedback: "[the human's modification text]"
Revise your output to address the human's feedback.
Preserve the parts they didn't comment on.
```
---
## Anti-Patterns
### Gate After Every Node
**Wrong**: Requiring human approval after every single node.
**Right**: Gate only at irreversible actions, user-facing outputs, and low-confidence decisions. Most internal nodes need no gate.
### Binary Approve/Reject Only
**Wrong**: The human can only approve or reject, with no way to provide specific feedback.
**Right**: Always include a "Modify" option with a text input for targeted feedback.
### No Context in the Gate
**Wrong**: Showing the human a raw JSON output with no explanation.
**Right**: Show: what the DAG is doing, what happened so far, what this output means, what happens next if approved.
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