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Agent Skill · 待分类

learning-path-creator(学习路径创建者)

learning-path-creator 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 Codex、Claude、Cursor、Gemini CLI 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。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。

learning-path-creator 是待分类类 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,按安装量在待分类类的 11,209 个 Skill 里排第 1,302。

总安装 3,967周安装 181GitHub Stars 3下载 2,616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:learning-path-creator(学习路径创建者)
来源仓库:https://github.com/jmsktm/claude-settings
仓库路径:skills/learning-path-creator
安装命令:
npx skills add https://github.com/jmsktm/claude-settings --skill 'Learning Path Creator'
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/jmsktm/claude-settings --skill 'Learning Path Creator'

这个 Skill 是做什么的

这个 Skill 的分类还没最终确定,用法以来源仓库的说明为准。

learning-path-creator 记录任务执行中的错误、纠正和经验缺口。适合让 Agent 持续沉淀问题、修正和最佳实践,支持能力成长追踪。通过 npx skills add 命令从指定 GitHub 仓库安装,需结合原始 README 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。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/learning-path-creator

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/jmsktm/claude-settings --skill 'Learning Path…;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

learning-path-creator 怎么安装?

在终端执行 npx skills add https://github.com/jmsktm/claude-settings --skill 'Learning Path Creator'。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

learning-path-creator 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。

learning-path-creator 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。

learning-path-creator 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。

learning-path-creator 用的人多吗?

公开数据是累计安装 3,967 次,近一周安装 181 次,下载 2,616 次,GitHub 3 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。

learning-path-creator 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。
  • 需要处理待分类类任务、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。

待分类类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
infsh-cli

infsh CLI · Codex、Claude

4,374,576382权限未标注未明确未收录
ai-video-generation

AI 视频生成 · Codex、Claude

4,313,376382调用外部服务未明确有提醒
lark-openapi-explorer

云雀 OpenAPI 浏览器 · Codex、Claude

2,064,1209,011需要联网未明确有提醒
lark-workflow-standup-report

Lark 工作流程站立报告 · Codex、Claude

2,041,6328,978需要联网未明确通过
use-my-browser

使用我的浏览器 · Codex、Claude

1,848,49140操作浏览器未明确未通过
learning-path-creator

学习路径创建者 · 本页

3,9673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

Learning Path Creator

The Learning Path Creator skill helps you design structured, effective learning plans for acquiring new skills or deepening existing knowledge. Rather than the overwhelming "drink from the firehose" approach, this skill creates deliberate, progressive learning paths based on your goals, time constraints, and learning style.

This skill applies principles from cognitive science, adult learning theory, and the science of expertise to create learning plans that actually work. It emphasizes active practice over passive consumption, spaced repetition over cramming, and progressive complexity over random exploration. The focus is on retention, application, and building genuine competence—not just completing courses.

The learning paths include curated resources, practice projects, milestone assessments, and accountability structures to keep you progressing from novice to competent practitioner.

Core Workflows

Workflow 1: Learning Path Design

Create comprehensive learning plan:

  1. Goal Clarification: What do you want to be able to do?
  2. Baseline Assessment: What do you already know?
  3. Context Understanding: Why are you learning this? By when?
  4. Resource Curation: Books, courses, tutorials, communities
  5. Project Planning: Hands-on practice opportunities
  6. Milestone Definition: Checkpoints to validate progress
  7. Schedule Design: Time allocation and pacing
  8. Accountability Setup: Tracking and external commitment

Workflow 2: Skill Assessment

Evaluate current level:

  1. Self-Assessment: Rate your current knowledge
  2. Gap Identification: What do you need to learn?
  3. Priority Mapping: What matters most to your goal?
  4. Learning Style: How do you learn best?
  5. Constraint Analysis: Time, money, access limitations

Workflow 3: Resource Recommendation

Curate best learning materials:

  1. Format Preferences: Books, videos, courses, tutorials?
  2. Quality Filtering: Beginner-friendly vs. advanced
  3. Time Considerations: Quick wins vs. deep dives
  4. Budget: Free vs. paid options
  5. Sequencing: What to consume in what order

Workflow 4: Progress Tracking

Monitor learning journey:

  1. Completion Tracking: What's done vs. what's left
  2. Comprehension Check: Can you explain/apply concepts?
  3. Project Milestones: Build real things to prove competence
  4. Difficulty Adjustment: Too easy? Too hard? Just right?
  5. Motivation Check: What's working? What's dragging?

Workflow 5: Learning Review

Periodic assessment and adjustment:

  1. Progress Evaluation: Where are you vs. where you planned to be?
  2. Knowledge Retention: What have you actually retained?
  3. Application Success: Can you use this in real contexts?
  4. Path Adjustment: Speed up, slow down, pivot?
  5. Next Phase: What's the next level of skill development?

Learning Frameworks

The Four Stages of Competence

Stage 1: Unconscious Incompetence You don't know what you don't know

  • Strategy: Exposure and awareness-building
  • Activities: Surveys, introductory content, big picture

Stage 2: Conscious Incompetence You know what you don't know (hardest stage)

  • Strategy: Deliberate practice with immediate feedback
  • Activities: Tutorials, exercises, projects with guidance

Stage 3: Conscious Competence You can do it, but it requires focus

  • Strategy: Repetition and variation
  • Activities: Projects, teaching others, real-world application

Stage 4: Unconscious Competence You do it naturally without thinking

  • Strategy: Maintenance and mastery
  • Activities: Complex projects, mentoring, pushing boundaries

Progressive Learning Layers

Layer 1: Foundation (20% of time) Core concepts, vocabulary, mental models

  • Goal: Build scaffolding for deeper learning
  • Methods: Introductory courses, overview books

Layer 2: Active Practice (50% of time) Hands-on application, building things

  • Goal: Develop practical competence
  • Methods: Tutorials, exercises, small projects

Layer 3: Real Application (20% of time) Use in actual work or meaningful projects

  • Goal: Context and integration
  • Methods: Side projects, freelance work, contributions

Layer 4: Teaching & Refinement (10% of time) Explain to others, fill knowledge gaps

  • Goal: Deep understanding and retention
  • Methods: Writing, teaching, mentoring

The Project-Based Learning Approach

Rather than learn → then apply: Build → Learn → Build → Learn

Example for learning web development:

  1. Project: Build a personal website
  2. Learn: HTML/CSS basics (just enough)
  3. Project: Add interactivity
  4. Learn: JavaScript fundamentals
  5. Project: Add database
  6. Learn: Backend basics
  7. Continue: Each project drives next learning

Quick Reference

ActionCommand/Trigger
Create new path"create learning path for [skill]"
Skill assessment"assess my [skill] level"
Resource recommendations"what should I learn from for [skill]"
Progress update"learning progress update"
Project ideas"practice projects for [skill]"
Review learning"review my learning path"
Adjust plan"my learning path needs adjustment"
Next steps"what should I learn next in [path]"

Learning Path Templates

30-Day Skill Sprint Template

SKILL: [Skill Name]
GOAL: [Specific capability you want]
TIME COMMITMENT: [Hours per day/week]

WEEK 1: FOUNDATION
- [ ] Resource 1: [Book/Course - Core concepts]
- [ ] Resource 2: [Tutorial - Getting started]
- [ ] Mini-Project: [Something tiny to prove basics]
- Checkpoint: Can you explain the fundamentals to someone?

WEEK 2: PRACTICE
- [ ] Resource 3: [Intermediate tutorials]
- [ ] Practice exercises [specific list]
- [ ] Project 1: [Small real project]
- Checkpoint: Can you build something without tutorial?

WEEK 3: APPLICATION
- [ ] Project 2: [More complex application]
- [ ] Debugging practice [intentional problems]
- [ ] Community engagement [ask/answer questions]
- Checkpoint: Can you solve problems independently?

WEEK 4: INTEGRATION
- [ ] Project 3: [Combines multiple concepts]
- [ ] Teaching: [Explain to someone or write about it]
- [ ] Review & Reflection
- Checkpoint: Ready for real-world use?

SUCCESS CRITERIA:
- [ ] Can build [specific thing] from scratch
- [ ] Can explain [core concepts] clearly
- [ ] Can debug [common problems]
- [ ] Feel confident using in real work

Career Skill Development Template

SKILL: [Skill Name]
CURRENT LEVEL: [Novice/Intermediate/Advanced]
TARGET LEVEL: [Where you want to be]
TIMELINE: [3/6/12 months]
WHY: [Career goal or requirement]

PHASE 1: FOUNDATION (Month 1-2)
Learning: [Core concepts, basics]
Resources:
- [ ] Book: [Title]
- [ ] Course: [Title]
Practice:
- [ ] Project 1
- [ ] Project 2
Milestone: [Specific capability]

PHASE 2: DEPTH (Month 3-4)
Learning: [Advanced topics]
Resources:
- [ ] Advanced course/book
- [ ] Documentation deep-dive
Practice:
- [ ] Project 3 (more complex)
- [ ] Contribute to open source
Milestone: [Specific capability]

PHASE 3: MASTERY (Month 5-6)
Learning: [Specialized areas, best practices]
Resources:
- [ ] Expert-level content
- [ ] Community involvement
Practice:
- [ ] Real-world application
- [ ] Teaching/mentoring others
Milestone: [Professional competence]

WEEKLY TIME BUDGET: [Hours breakdown]
- Learning: [X hours]
- Practice: [X hours]
- Projects: [X hours]
- Review: [X hours]

SUCCESS METRICS:
- Portfolio pieces: [Number and type]
- Certifications: [If applicable]
- Real-world use: [Job, freelance, etc.]

Topic Deep Dive Template

TOPIC: [Subject Area]
DEPTH TARGET: [Working knowledge / Expert / Mastery]
PURPOSE: [Why you're learning this]

PRE-WORK: BUILD CONTEXT
- [ ] Survey the field: [Overview resources]
- [ ] Understand why it matters
- [ ] Identify key sub-topics

CORE CURRICULUM
Resources (in order):
1. [Introductory resource]
2. [Foundational resource]
3. [Advanced resource]

Active Learning:
- [ ] Take notes in own words
- [ ] Create concept maps
- [ ] Explain to others
- [ ] Write summary essays

Application:
- [ ] Real-world examples
- [ ] Case studies
- [ ] Problem sets
- [ ] Teaching moment

INTEGRATION
Connect to:
- [Related topic 1]
- [Related topic 2]
- [Practical application area]

MASTERY EVIDENCE:
- [ ] Can teach this to a beginner
- [ ] Can debate nuances with expert
- [ ] Can apply in novel situations

Best Practices

  • Start with the end - Define what you want to be able to DO, not just know
  • Build before you're ready - Projects teach more than courses
  • Learn just-in-time - Study what you need for your current project
  • Teach to solidify - The best way to learn is to explain to others
  • Practice deliberately - Focus on weak points, get immediate feedback
  • Space your learning - Distributed practice beats cramming
  • Test yourself regularly - Retrieval practice aids retention
  • Vary your practice - Different contexts deepen understanding
  • Seek feedback early - Don't practice mistakes
  • Focus on fundamentals - Deep basics beat surface-level breadth
  • Join communities - Learning is social
  • Track your progress - Visible progress maintains motivation
  • Schedule learning time - It won't happen spontaneously
  • Limit work-in-progress - One major skill at a time
  • Review and reflect - Spaced review prevents forgetting

Learning Anti-Patterns

Tutorial Hell

Problem: Constantly following tutorials but can't build independently Solution: After each tutorial, build something similar without instructions

Collector's Fallacy

Problem: Hoarding courses and books but never finishing Solution: One resource at a time, completion over collection

Perfectionism Paralysis

Problem: Waiting to "fully understand" before practicing Solution: Build messy first drafts, iterate toward quality

Passive Consumption

Problem: Watching videos and reading without doing Solution: 80% practice, 20% consumption

Random Walk Learning

Problem: Jumping between topics without structure Solution: Follow a deliberate path, finish what you start

Recommended Learning Resources

Meta-Learning (How to Learn)

  • Book: "Make It Stick" - Science of successful learning
  • Book: "Ultralearning" - Self-directed learning strategies
  • Book: "Peak" - Secrets of expertise
  • Article: Bloom's Taxonomy - Levels of understanding

Platforms by Learning Style

Project-Based Learners

  • freeCodeCamp, The Odin Project, Frontend Mentor
  • Build real projects from day one

Structured Learners

  • Coursera, edX, Udacity
  • Full curricula with certificates

Video Learners

  • YouTube channels, Udemy, Skillshare
  • Visual demonstrations

Book Learners

  • Technical books, O'Reilly, Manning
  • Deep, comprehensive coverage

Community Learners

  • Discord servers, Reddit, Stack Overflow
  • Learn through collaboration

Integration Points

This skill works well with:

  • Goal Setter: Align learning with career/personal goals
  • Daily Planner: Schedule dedicated learning time
  • Reading List Manager: Track learning resources
  • Habit Tracker: Build consistent learning habits
  • Journal Prompter: Reflect on learning progress

Example Learning Path Session

User: "create learning path for Python"

Skill Response: "Great choice—Python is incredibly versatile. Let's design a learning path that works for you.

First, some context questions to personalize this:

1. What do you want to DO with Python?

  • Data analysis/science?
  • Web development?
  • Automation/scripting?
  • Machine learning?
  • General programming skills?

2. What's your current programming experience?

  • Never coded before?
  • Know another language?
  • Dabbled a bit?

3. How much time can you dedicate?

  • Hours per week?
  • Timeline/deadline?

4. How do you learn best?

  • Videos and tutorials?
  • Books and documentation?
  • Building projects?
  • Mix of all?

Take your time—good answers create a better path."

User: "Want to do data analysis, never coded before, can do 5 hours per week, learn best by doing projects"

Skill Response: "Perfect. Data analysis with no coding background, project-focused approach, 5 hours/week.

Here's your personalized path:

Python for Data Analysis: 12-Week Learning Path

Phase 1: Python Foundations (Weeks 1-4)

*Goal: Basic Python competence*

Week 1-2: Basics

  • Resource: freeCodeCamp Python course (first 4 hours)
  • Mini-project: Build a simple calculator
  • Practice: 30 Python exercises on Exercism
  • Time: 3hrs learning, 2hrs practice

Week 3-4: Data structures

  • Resource: Automate the Boring Stuff (chapters on lists, dicts)
  • Project: Build a contact manager (stores/retrieves data)
  • Time: 2hrs reading, 3hrs project

Checkpoint: Can you write loops, functions, and work with lists/dicts?

Phase 2: Data Analysis Tools (Weeks 5-8)

*Goal: pandas, numpy, visualization basics*

Week 5-6: pandas basics

  • Resource: Kaggle's pandas course
  • Project: Analyze a simple dataset (we'll pick one together)
  • Time: 2hrs tutorials, 3hrs project

Week 7-8: Visualization

  • Resource: matplotlib/seaborn tutorials
  • Project: Create charts from your dataset
  • Time: 1hr tutorials, 4hrs project work

Checkpoint: Can you load, clean, and visualize data?

Phase 3: Real Analysis (Weeks 9-12)

*Goal: End-to-end data analysis*

Week 9-10: Full analysis project

  • Pick real dataset (Kaggle, data.gov)
  • Clean → Analyze → Visualize → Insights
  • Time: Full 5 hours on project

Week 11-12: Advanced techniques

  • Learn as needed for your project
  • Polish and document your work
  • Share your analysis

Final Checkpoint: Published analysis on GitHub


Weekly Structure (5 hours):

  • Monday: 1 hour learning (tutorial/reading)
  • Wednesday: 2 hours hands-on practice
  • Saturday: 2 hours project work

Success looks like: After 12 weeks, you can take a dataset, clean it, analyze it, create visualizations, and present insights.

Shall we lock this in and I'll help you get started with Week 1?"

平台分布

Codex

36.61%
按下载量换算958

Claude

28.64%
按下载量换算749

Cursor

17.53%
按下载量换算459

Gemini CLI

8.29%
按下载量换算217

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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