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human-gate-designer人类大门设计师

Agent Skill

human-gate-designer 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

1,440

周安装

60

GitHub Stars

98

下载量

480
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:human-gate-designer(人类大门设计师)
来源仓库:https://github.com/erichowens/some_claude_skills
仓库路径:skills/human-gate-designer
安装命令:
npx skills add https://github.com/erichowens/some_claude_skills --skill human-gate-designer
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/erichowens/some_claude_skills --skill human-gate-designer

简介

human-gate-designer 用于处理 GitHub 仓库、Issue 和 Pull Request 信息,适合项目状态整理。

  • 适用于基础设施设计和代码协作事项管理场景。
  • 通过 npx skills add 命令从指定仓库安装,需确认权限范围。
  • 使用前建议核实维护状态及是否涉及敏感数据操作。
  • 可结合原始 SKILL.md 了解具体交互方式和输出格式。

SKILL.md

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

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

SituationGate PositionWhy
Irreversible action (deploy, send email, submit)Before the actionCan't undo
User-facing deliverable (report, website, PR)After generation, before deliveryQuality check
High cost remaining (>$0.50)Before expensive phaseBudget confirmation
Low confidence output (<0.7)After the uncertain nodeExpert judgment needed
Ambiguous task decompositionAfter planning, before executionValidate the plan
First run of a new template DAGAfter each phaseBuild 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

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.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.07%
按下载量换算173

Claude

27.48%
按下载量换算132

Cursor

19.94%
按下载量换算96

Gemini CLI

9.63%
按下载量换算46

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

权限需确认

当前来源未能明确判断权限范围,默认进入异常复核队列。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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