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ai-collaborate-teachingAI 协作教学

Agent Skill

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。

总安装

1,310

周安装

53

GitHub Stars

158

下载量

411
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: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

简介

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。

SKILL.md

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

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

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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

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

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通过

Snyk

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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

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