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q-educatorq 教育家

Agent Skill

q-educator 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

1,721

周安装

71

GitHub Stars

21

下载量

562
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:q-educator(q 教育家)
来源仓库:https://github.com/tyrealq/q-skills
仓库路径:skills/q-educator
安装命令:
npx skills add https://github.com/tyrealq/q-skills --skill q-educator
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/tyrealq/q-skills --skill q-educator

简介

用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。

  • 适合围绕仓库状态、代码变更或协作事项进行整理和分析。
  • 可结合来源仓库和原始 README 核验具体用法,确保功能匹配需求。
  • 安装方式:通过 npx skills add 从指定 GitHub 仓库添加。
  • 安装前建议确认权限范围和是否会触发联网或文件读写操作。

SKILL.md

Q-Educator

Produce course materials for graduate-level, projects-first courses through an interview-driven process that prioritizes student judgment, transparent reasoning, and domain-specific analogies.

References

  • references/teaching_philosophy.md — six governing principles
  • references/interview_protocol.md — six-question interview sequence
  • references/lecture_template.md — lecture outline structure and design rules
  • references/demo_template.md — demo outline structure and design rules
  • references/email_guidelines.md — follow-up email style rules
  • references/assignment_template.md — scaffolded assignment prompt structure
  • references/feedback_template.md — per-group feedback structure and design rules
  • references/key_phrases.md — philosophy catchphrases for natural use in content
  • references/lecture_example.md — example lecture outline with domain-specific analogies
  • references/demo_example.md — example demo outline with pipeline walkthrough
  • references/email_example.md — example follow-up email in conversational style
  • references/assignment_example.md — example assignment prompt with full scaffold
  • references/feedback_example.md — example per-group feedback document

Core Principles

  • Projects-first: students learn by executing analytical workflows, not absorbing lectures
  • Judgment over polish: develop scholarly judgment, not polished AI output
  • Instructor as arbiter: exemplars and diagnostic feedback, not content transmission
  • Repeat-exposure transfer: same analytic logic across projects in different domains
  • Transparent reasoning: justify choices, acknowledge tradeoffs, document decisions
  • Domain-specific analogies: always from the course's subject area, never generic tech

Workflow

Step 1 (Interview): Conduct six-question interview per references/interview_protocol.md. Only begin content generation after the interview is complete.

Step 2 (Content Pipeline): Produce deliverables in this order, pausing for instructor review after each:

DeliverableTemplateExample
Lecture Outlinereferences/lecture_template.mdreferences/lecture_example.md
Demo Outlinereferences/demo_template.mdreferences/demo_example.md
Follow-Up Emailreferences/email_guidelines.mdreferences/email_example.md

Step 3 (Assessment, as needed):

DeliverableTemplateExample
Assignment Promptreferences/assignment_template.mdreferences/assignment_example.md
Per-Group Feedbackreferences/feedback_template.mdreferences/feedback_example.md

Scope

Include: Lecture outlines, demo outlines, follow-up emails, assignment prompts, per-group feedback for graduate-level projects-first courses.

Checklist

  • Interview completed before drafting (references/interview_protocol.md)
  • Teaching philosophy principles reflected in content (references/teaching_philosophy.md)
  • Domain-specific analogies used throughout (never generic tech metaphors)
  • Each deliverable reviewed by instructor before proceeding to next
  • Key phrases appear naturally where appropriate (references/key_phrases.md)
  • Deliverable follows its template structure and design rules

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

37.94%
按下载量换算213

Claude

27.24%
按下载量换算153

Cursor

16.77%
按下载量换算94

Gemini CLI

8.63%
按下载量换算49

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。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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