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training-quiz培训测验

Agent Skill

training-quiz 用于辅助前端页面、组件、样式和交互逻辑开发,适合在 OpenClaw 中需要维护前端项目、生成组件或检查界面实现时使用。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

5,940

周安装

238

GitHub Stars

公开资料未说明

下载量

1,923
OpenClaw

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

MIT-0

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:training-quiz(培训测验)
来源仓库:https://github.com/fangwei-frank/training-quiz
安装命令:
openclaw skills install training-quiz
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install training-quiz

简介

针对零售人员的互动产品知识培训和问答系统。通过以下方式对员工进行产品规格、商店政策、销售技巧和常见问题解答的测试

SKILL.md

name
training-quiz
description
>
metadata
openclaw
emoji
🎓

Training Quiz

Overview

This skill turns the product knowledge base into an interactive learning system for staff. It adapts difficulty, tracks progress, and celebrates improvement — making training feel less like a chore and more like a game.

Depends on: products[] + policy_entries[] + faqs[] in knowledge base.


Quiz Modes

ModeTriggerFormatBest For
Flashcard"考我产品知识"Q → reveal AQuick daily review
Multiple Choice"选择题模式"Q + 4 optionsStructured testing
Scenario"情景练习"Role-play customer scenarioSales skill practice
Policy Drill"考我政策"Policy rule questionsCompliance training
New Product"考我新品"Focus on recently added itemsNew arrival onboarding

Default mode: Flashcard (lowest friction).


Session Flow

Start

  1. Greet the learner by name (if known from staff config)
  2. Ask or confirm: mode, topic focus, number of questions (default 10)
  3. Begin immediately — don't over-explain

During Quiz

  • Ask one question at a time
  • Wait for answer before revealing correct response
  • On correct: brief positive reinforcement ("✅ 答对了!") + optional fun fact
  • On incorrect: show correct answer + brief explanation (2 sentences max)
  • Track: correct / total, running accuracy %

End of Session

🎓 本次练习结束!

结果:[correct]/[total] — [score]%
[评级: 优秀 ≥90% | 良好 70-89% | 需加强 <70%]

[If score < 70%]: 建议重点复习:[list weak categories]
[If score ≥ 90%]: 太棒了!你已经达到优秀水平 🏆

下次想练习什么?

Reference: question-bank.md — question templates by type.


Question Generation

Questions are auto-generated from the knowledge base. No manual authoring needed.

From products:

  • "这款[产品名]的[属性]是什么?" → answer from description/variants
  • "下面哪个是[产品名]的正确价格?" → MCQ using real + nearby prices as distractors
  • "[顾客描述] → 你会推荐哪款产品?" → scenario from suitable_for

From policies:

  • "退货政策中,[条件],顾客可以享受什么?"
  • "以下哪种情况不在退货政策范围内?" → MCQ with real exceptions as options

From FAQs:

  • Use question field directly
  • Shuffle real FAQ answers as MCQ distractors

Script: scripts/generate_questions.py — generates a quiz set from the KB.


Progress Tracking

Store per-employee progress in agent memory under training_progress.<staff_id>:

{
  "staff_id": "zhang_san",
  "sessions": [
    {
      "date": "2024-07-15",
      "mode": "flashcard",
      "score": 8,
      "total": 10,
      "accuracy": 80,
      "weak_categories": ["policy", "pricing"]
    }
  ],
  "cumulative_accuracy": 82,
  "badges": ["first_quiz", "7day_streak", "policy_master"]
}

Report progress to manager on request:

"张三本月完成 5 次练习,平均正确率 82%,政策类题目需加强。"

Gamification

Keep it motivating:

AchievementTrigger
🌟 首次完成First quiz session
🔥 连续挑战3+ consecutive days
📚 政策达人5 policy quizzes with ≥90%
🏆 产品专家Overall accuracy ≥90% over 10+ sessions
⚡ 闪电手10 consecutive correct answers

Announce badges immediately when earned. Keep it brief and genuine.

适合场景

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用户想查找某类 Agent Skill 时

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需要根据任务场景推荐可安装能力包时

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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

补充不同宿主或平台的使用分布数据

能力 5

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

OpenClaw

74.4%
按下载量换算1,431

安全审计

VirusTotal

未展示

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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