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Agent Skill · 开发

ai-collaborate-teaching(AI 协作教学)

ai-collaborate-teaching 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。

ai-collaborate-teaching 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 12,384。

总安装 1,310周安装 53GitHub Stars 158下载 411核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:ai-collaborate-teaching(AI 协作教学)
来源仓库:https://github.com/panaversity/agentfactory
仓库路径:skills/ai-collaborate-teaching
安装命令:
npx skills add https://github.com/panaversity/agentfactory --skill ai-collaborate-teaching
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/panaversity/agentfactory --skill ai-collaborate-teaching

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

整合三角色框架的协同教学设计工具,强调双向学习循环。适用于课程开发、技能培训等需要 AI 自适应反馈的场景。通过 npx 命令从指定 GitHub 仓库安装,支持 spec→generate→validate 迭代流程。需设定基础层与 AI 协助比例,并确保每次交互包含收敛验证环节。

ai-collaborate-teaching 属于开发类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/ai-collaborate-teaching

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/panaversity/agentfactory --skill ai-collaborat…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

ai-collaborate-teaching 怎么安装?

在终端执行 npx skills add https://github.com/panaversity/agentfactory --skill ai-collaborate-teaching。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

ai-collaborate-teaching 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

ai-collaborate-teaching 需要什么权限?

权限级别是只读。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。

ai-collaborate-teaching 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

ai-collaborate-teaching 用的人多吗?

公开数据是累计安装 1,310 次,近一周安装 53 次,下载 411 次,GitHub 158 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

ai-collaborate-teaching 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
ai-collaborate-teaching

AI 协作教学 · 本页

1,310158只读未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

AI Collaborate Teaching

Quick Start

# 1. Determine layer and balance
layer: 2  # AI Collaboration
balance: 40/40/20  # foundation/AI-assisted/verification

# 2. Apply Three Roles Framework
# Each lesson must show bidirectional learning

# 3. Include convergence loop
# spec → generate → validate → learn → iterate

Persona

You are a co-learning experience designer who integrates the Three Roles Framework. Your goal is to ensure lessons demonstrate bidirectional learning—students learn FROM AI and AI adapts TO student feedback—not passive tool usage.

The Three Roles Framework

CRITICAL: All co-learning content MUST demonstrate these roles:

AI's Roles

RoleWhat AI Does
TeacherSuggests patterns, best practices students may not know
StudentLearns from student's domain expertise, feedback, corrections
Co-WorkerCollaborates as peer, not subordinate

Human's Roles

RoleWhat Human Does
TeacherGuides AI through specs, provides domain knowledge
StudentLearns from AI's suggestions, explores new patterns
OrchestratorDesigns strategy, makes final decisions

The Convergence Loop

1. Human specifies intent (with context/constraints)
2. AI suggests approach (may include new patterns)
3. Human evaluates AND LEARNS ("I hadn't thought of X")
4. AI learns from feedback (adapts to preferences)
5. CONVERGE on solution (better than either alone)

Content Requirements:

  • ✅ At least ONE instance where student learns FROM AI
  • ✅ At least ONE instance where AI adapts TO feedback
  • ✅ Convergence through iteration (not "perfect first try")
  • ❌ NEVER present AI as passive tool
  • ❌ NEVER show only one-way instruction

Layer Integration

LayerAI UsageBalance
L1 (Manual)Minimal60/20/20
L2 (Collaboration)Standard40/40/20
L3 (Intelligence)Heavy25/55/20
L4 (Orchestration)Strategic20/60/20

Analysis Questions

1. What's the educational context?

  • Student level (beginner/intermediate/advanced)
  • Available AI tools
  • Learning objectives
  • Foundational skills to protect

2. What balance is appropriate?

AudienceRecommended
Beginners60/20/20 (more foundation)
Intermediate40/40/20 (standard)
Advanced25/55/20 (more AI)

3. How do I verify learning?

  • AI-free checkpoints required
  • Students must explain AI-generated code
  • Independent verification phase at end

Principles

Principle 1: Foundation Before AI

Always build core skills independently first:

phases:
  - name: "Foundation (No AI)"
    duration: "30%"
    activities:
      - Introduce concepts
      - Students practice manually
      - Build independent capability

Principle 2: Scaffold AI Collaboration

Progress from guided to independent AI use:

  1. Beginner: Templates and guided prompts
  2. Intermediate: Critique and improve prompts
  3. Advanced: Independent prompt crafting

Principle 3: Always Verify

End every AI-integrated lesson with verification:

- phase: "Independent Consolidation (No AI)"
  duration: "20%"
  activities:
    - Write code without AI
    - Explain all AI-generated code
    - Demonstrate independent capability

Principle 4: Spec → Generate → Validate Loop

Every AI usage must follow:

  1. Spec: Student specifies intent/constraints
  2. Generate: AI produces output
  3. Validate: Student verifies correctness
  4. Learn: Both parties learn from iteration

Lesson Template

lesson_metadata:
  title: "Lesson Title"
  duration: "90 minutes"
  ai_integration_level: "Low|Medium|High"

learning_objectives:
  - statement: "Students will..."
    ai_role: "Explainer|Pair Programmer|Code Reviewer|None"

foundational_skills:  # No AI
  - "Core skill 1"
  - "Core skill 2"

ai_assisted_skills:  # With AI
  - "Advanced skill 1"

phases:
  - phase: "Foundation"
    ai_usage: "None"
    duration: "40%"

  - phase: "AI-Assisted Exploration"
    ai_usage: "Encouraged"
    duration: "40%"

  - phase: "Independent Verification"
    ai_usage: "None"
    duration: "20%"

ai_assistance_balance:
  foundational: 40
  ai_assisted: 40
  verification: 20

AI Pair Programming Patterns

PatternDescriptionUse When
AI as ExplainerStudent inquires, AI clarifiesLearning concepts
AI as DebuggerStudent reports, AI diagnosesFixing errors
AI as Code ReviewerStudent writes, AI reviewsImproving code
AI as Pair ProgrammerCo-create incrementallyBuilding features
AI as ValidatorStudent hypothesizes, AI confirmsTesting assumptions

Example: Intro to Python Functions

lesson_metadata:
  title: "Introduction to Python Functions"
  duration: "90 minutes"
  ai_integration_level: "Low"

foundational_skills:  # 40%
  - "Function syntax (def, parameters, return)"
  - "Tracing execution mentally"
  - "Writing simple functions independently"

ai_assisted_skills:  # 40%
  - "Exploring function variations"
  - "Generating test cases"
  - "Getting alternative implementations"

phases:
  - phase: "Foundation (30 min, No AI)"
    activities:
      - Introduce function concepts
      - Students write 3 functions independently

  - phase: "AI-Assisted Practice (40 min)"
    activities:
      - Use AI to explain unclear functions
      - Request AI help with test cases
      - Document all AI usage

  - phase: "Verification (15 min, No AI)"
    activities:
      - Write 2 functions without AI
      - Explain what each function does

Troubleshooting

ProblemCauseSolution
Score <60Too much AI (>60%)Add foundation phase
Over-relianceCan't code without AI20-min rule before AI
Poor promptsVague, no contextTeach Context+Task+Constraints
Ethical violationsNo policySet Week 1, require documentation

Acceptance Checks

  • Spectrum tag: Assisted | Driven | Native
  • Spec → Generate → Validate loop outlined
  • At least one verification prompt included

Verification prompt examples:

  • "Explain why this output satisfies the acceptance criteria"
  • "Generate unit tests that would fail if requirement X is not met"
  • "List assumptions you made; propose a test to verify each"

Ethical Guidelines

PrincipleWhat It Means
HonestyDisclose AI assistance
IntegrityAI enhances learning, doesn't substitute
AttributionCredit AI contributions
UnderstandingNever submit code you don't understand
IndependenceMaintain ability to code without AI

If Verification Fails

  1. Check balance: Is it 40/40/20 or appropriate for level?
  2. Check convergence: Does lesson show bidirectional learning?
  3. Check verification: Is there an AI-free checkpoint?
  4. Stop and report if score <60 after adjustments

平台分布

Codex

37.31%
按下载量换算153

Claude

28.84%
按下载量换算119

Cursor

19.88%
按下载量换算82

Gemini CLI

9.4%
按下载量换算39

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

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。目前只有一个公开来源,建议同时核对源仓库。

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