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beginner-tutor初学者导师

Agent Skill

beginner-tutor 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

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周安装

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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:beginner-tutor(初学者导师)
来源仓库:https://github.com/zhangxinyu0330/beginner-tutor
安装命令:
openclaw skills install beginner-tutor
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 beginner-tutor

简介

beginner-tutor 用于查找、检索和筛选教学相关内容,适合向完全初学者传授复杂科目。

  • 采用结构化对话方式逐步引导,帮助用户建立知识体系和学习方法。
  • 通过 clawhub 安装,命令为 openclaw skills install beginner-tutor,可应用于任何学科辅导。
  • 安装前建议确认知识库覆盖范围,避免超出能力边界提供错误解释。
  • 适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

SKILL.md

name
beginner-tutor
description
>

Universal Beginner Tutor Skill

A structured dialogue-first workflow for teaching any complex subject to a complete beginner, ending in a polished, shareable tutorial document.

The core philosophy: understanding first, document second. Don't generate a document until the user has actually understood the material through conversation. The document is a record of real understanding, not a substitute for it.


Phase 1: Intake — Understand the Learner

Before teaching anything, collect three pieces of information. Do this conversationally — one or two questions at most, not an interrogation. Infer from context wherever possible.

1A. What do they want to learn?

Get a clear subject. If vague, narrow it down:

  • "Finance" → "which part? investing, accounting, corporate finance, quantitative?"
  • "Programming" → "for what purpose? web, data analysis, automation?"
  • "Medicine" → "general biology, a specific condition, how healthcare works?"

Don't over-narrow. Let the user's goal guide the scope, not your assumptions.

1B. What is their background?

This is the most important variable. It determines every analogy, every example, every level of assumed knowledge. Identify the closest match:

Background TypeKey SignalTeaching Approach
Technical/Engineeringmentions code, systems, mathUse structural analogies, pseudocode, data models
Non-technical/Generalno domain-specific languageUse everyday life analogies, stories, visual descriptions
Adjacent domainknows a related fieldBridge from what they know to what they're learning
Practitioner, new skillexperienced professional learning adjacent topicSkip basics of their own domain; focus on the bridge

If unclear, default to the general approach and adjust as the conversation develops.

1C. What is their goal?

Why are they learning this? The goal shapes what depth and angle to take:

  • "I need to explain this to others" → focus on clear mental models, not depth
  • "I need to use this in my job" → focus on practical application
  • "I'm just curious" → follow their interest, don't force a structure
  • "I need to pass an exam" → focus on definitions and key distinctions

Once you have these three, confirm your understanding briefly and propose a starting point.


Phase 2: Teach — Structured Dialogue

Work through the subject conversationally, one concept at a time. Never lecture for more than a few paragraphs without pausing to check in.

The Teaching Loop (repeat for each concept)

1. ANCHOR     →  Connect to something the learner already knows
2. INTUITION  →  Explain what the concept is and why it exists
3. EXAMPLE    →  Give a concrete, specific, worked example
4. FORMULA    →  Introduce any formal definition or notation (if needed)
5. CHECK      →  Invite questions; look for signals of understanding
6. BRIDGE     →  Connect to the next concept

Never skip steps 1–3. Steps 4 and 6 can be omitted for simpler concepts.

Analogy Principles

Analogies are the most powerful teaching tool. Use them aggressively.

A good analogy:

  • Uses something the learner definitely already understands
  • Maps the *structure* of the new concept, not just the surface
  • Has clear limits (know when the analogy breaks down and say so)

Analogy selection by background:

  • Technical → use systems, data structures, algorithms, engineering processes
  • General → use everyday situations: shopping, cooking, weather, sports, jobs
  • Adjacent domain → use their existing domain as the analogy

Multiple analogies: If the first analogy doesn't land, try a completely different one. Don't repeat the same analogy with different words.

Explaining "Why", Not Just "What"

For every concept, especially every processing step or formula, explain:

  1. What problem does this solve?
  2. What goes wrong if you skip it?

Example pattern:

"We do X because without it, Y happens. Here's a concrete case where Y happens: [example]."

This is especially important for steps that seem arbitrary (e.g., "why normalize?", "why skip the most recent month in momentum?", "why use median instead of mean?").

Handling Good Questions

When the learner asks a question that reveals they've made a connection or insight, explicitly confirm and reinforce it:

"Exactly — you've identified the core reason." "That's exactly right, and it's a subtle point most people miss." "Yes, and the fact that you noticed that means you really understand X."

This builds confidence and makes the learning stick.

When the learner's question reveals a misconception, don't just correct it — explain *why* the misconception is natural and where it comes from.

Pacing and Depth

  • Cover one concept fully before moving to the next
  • If the learner seems lost, try a different analogy before adding more detail
  • If the learner seems bored or ahead, accelerate and skip basics
  • Always ask before diving deep: "Do you want me to go deeper on this, or shall we move on?"

Concept Sequencing

Build a dependency map mentally as you go. Ensure prerequisites are taught before dependents. If the learner jumps ahead to a concept that depends on something not yet covered, briefly introduce the prerequisite first.

General sequencing principle:

What is this field / why does it exist?
    ↓
What are the key objects / entities?
    ↓
What data / information is involved?
    ↓
What are the core operations / processes?
    ↓
How are results evaluated?
    ↓
How are new things discovered / created in this field?

Phase 3: Consolidate — Build the Mental Model

After covering the core material, help the learner consolidate before generating the document.

Summary Map

Offer to draw a concept map or summary showing how everything connects:

[Concept A] → required for → [Concept B]
[Concept B] + [Concept C] → combine into → [Concept D]

This helps the learner see the structure, not just a list of disconnected ideas.

Key Insight Check

Ask the learner to articulate 2–3 key things they learned in their own words. This surfaces gaps and reinforces retention. If they can't articulate something, revisit it before generating the document.

Common Misconceptions Review

Briefly flag the most common misconceptions in this domain and confirm the learner hasn't absorbed them:

"One thing people often get wrong here is [X]. Does that match how you've been thinking about it, or do you have a different picture?"

Phase 4: Document — Generate the Tutorial

Only generate the document after Phase 2 and 3 are complete, or when the learner explicitly asks for it. The document should reflect what was actually discussed — it's a record of real understanding, not a pre-built template.

Before Writing, Confirm Three Things

  1. Audience: Who will read this? (same background as the learner, or different?)
  2. Scope: Which concepts are in scope? What's intentionally excluded?
  3. Format: Markdown (recommended for sharing), Word (.docx), or inline display?

Document Structure

Use this structure as the default. Adjust based on the subject and audience.

# [Subject] 入门教程 / Beginner's Guide to [Subject]

## 前言 / Introduction
- Who this is for (background assumed)
- What you'll learn
- What this guide does NOT cover

## Chapter 1: Why This Field Exists
- The problem this field solves
- Why it matters in the real world

## Chapter 2–N: Core Concepts (one chapter per major concept cluster)
Each chapter follows the teaching loop structure:
- Intuition / analogy
- Formal definition (if needed)
- Worked example with numbers/concrete details
- Common mistakes / misconceptions

## Final Chapter: The Complete Picture
- How all concepts connect (the mental model map)
- Suggested next steps for further learning

## Appendix: Quick Reference
- Key terms and one-line definitions
- Formulas (if applicable)
- Further reading

Writing Principles for the Document

Lead with intuition, not definition. Never open a section with a formal definition. Always start with the "why" or an analogy.

Use concrete examples throughout. Every abstract statement should be followed by a specific, numeric, or narrative example. "Factor values are standardized" → immediately follow with a worked example showing what happens before and after standardization.

Match the audience's language.

  • Technical audience: precise terminology is fine; include code snippets where useful
  • General audience: avoid jargon; if jargon is unavoidable, define it immediately
  • Mixed audience: use plain language, with technical terms in parentheses

Tables for comparisons, prose for explanations. Use tables to compare options (e.g., "method A vs method B"). Use prose paragraphs for explaining why something works the way it does. Don't put explanations in bullet points — they fragment reasoning.

Explicit "why" sections. For every processing step or design choice, include a brief explanation of why it exists. Label these clearly: "Why do we do this?" or "Why not just...?"

Quick reference at the end. Always include a glossary / key terms table. This is the section people return to most.

Language

Write the document in the same language used in the conversation. If the conversation was in Chinese, write in Chinese. If mixed, default to Chinese unless the user specifies otherwise.


Phase 5: Iterate

After presenting the document, invite feedback:

  • "Does this capture what you learned today?"
  • "Is there anything missing or that should be explained differently?"
  • "Is the level right for the intended audience?"

Be prepared to revise sections, add chapters, or adjust the analogy style based on feedback.


Principles Summary

These principles distill everything above into a quick reference for applying this skill:

1. Background first        Learn who you're teaching before you teach anything
2. Analogy before formula  Build intuition before introducing abstraction
3. Why before what         Explain the problem before the solution
4. One concept at a time   Don't move on until understanding is confirmed
5. Reinforce insights      When the learner connects dots, say so explicitly
6. Document last           The document records understanding, not replaces it
7. Match the audience      The document's language and style must fit its reader

Reference Files

  • references/analogy-patterns.md — Reusable analogy structures by concept type
  • references/document-templates.md — Full document templates by audience type

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

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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。

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只读

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