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learning-path-generator
by
huajianjiu
· GitHub ↗
· v1.0.0
· MIT-0
137
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Install in OpenClaw
/install learning-path-generator
Description
Generate personalized learning paths and curriculum for any skill or subject
README (SKILL.md)
Learning Path Generator
Description
Learning Path Generator is your personalized education architect that creates structured, progressive learning plans for any skill or subject. Whether you're learning a new programming language, picking up a musical instrument, or mastering a business skill, this skill designs optimal learning sequences based on your goals, current level, and available time.
Features
- Skill Assessment: Evaluate your current proficiency level
- Goal Setting: Define clear, measurable learning objectives
- Curriculum Design: Create logical learning sequences
- Time Planning: Optimize study schedules with realistic milestones
- Resource Curation: Suggest quality learning materials
- Progress Tracking: Build accountability checkpoints
How to Use
-
Define Learning Goals
- Target skill or subject
- Desired proficiency level (beginner/intermediate/advanced)
- Timeframe for achievement
- Weekly study hours available
-
Assess Current Level
- Self-assessment quiz
- Identify prerequisite knowledge
- Note any related skills
-
Generate Learning Path
- Review structured curriculum
- Adjust module sequence as needed
- Set checkpoint milestones
-
Execute & Refine
- Follow daily/weekly schedule
- Complete assessments
- Adjust based on progress
Example
Input: "Create a learning path for mastering Python programming from beginner to job-ready in 6 months, studying 15 hours per week"
Output:
# Learning Path: Python Programming
## Target: Job-Ready Proficiency | Duration: 6 Months | 15 hrs/week
---
## 📊 Skill Assessment
**Current Level**: Complete Beginner
**Prerequisites**: Basic computer literacy
**Related Experience**: None required
---
## 🎯 Proficiency Goals
By the end of this path, you will:
- [ ] Write clean, Pythonic code
- [ ] Build RESTful APIs
- [ ] Work with databases (SQL + NoSQL)
- [ ] Use Git for version control
- [ ] Apply testing principles
- [ ] Complete a portfolio project
---
## 📅 Phase 1: Foundations (Weeks 1-4)
### Week 1: Python Basics
| Day | Topic | Duration | Practice |
|-----|-------|----------|----------|
| Mon | Installing Python & IDE | 2 hrs | Setup environment |
| Tue | Variables & Data Types | 2 hrs | Exercises |
| Wed | Strings & String Methods | 2 hrs | Mini-project |
| Thu | Lists & List Methods | 2 hrs | Exercises |
| Fri | Conditionals (if/elif/else) | 2 hrs | Quiz |
| Sat | **Project**: Number Guessing Game | 3 hrs | Submit |
| Sun | Review & Practice | 2 hrs | Challenge |
### Week 2: Control Flow & Functions
| Day | Topic | Duration | Practice |
|-----|-------|----------|----------|
| Mon | Loops (for, while) | 2 hrs | Exercises |
| Tue | Functions - Basics | 2 hrs | Exercises |
| Wed | Functions - Parameters & Returns | 2 hrs | Exercises |
| Thu | Scope & Built-in Functions | 2 hrs | Quiz |
| Fri | Debugging Basics | 2 hrs | Debug exercise |
| Sat | **Project**: Calculator App | 3 hrs | Submit |
| Sun | Review & Practice | 2 hrs | Challenge |
### Week 3: Data Structures
| Day | Topic | Duration | Practice |
|-----|-------|----------|----------|
| Mon | Tuples & Sets | 2 hrs | Exercises |
| Tue | Dictionaries | 2 hrs | Exercises |
| Wed | List/Dict Comprehensions | 2 hrs | Exercises |
| Thu | File I/O Basics | 2 hrs | Mini-project |
| Fri | **Project**: Contact Book | 3 hrs | Submit |
| Sat-Sun | Review & Week 1-3 Assessment | 5 hrs | Test |
### Week 4: Object-Oriented Programming
| Day | Topic | Duration | Practice |
|-----|-------|----------|----------|
| Mon | Classes & Objects | 2 hrs | Exercises |
| Tue | Attributes & Methods | 2 hrs | Exercises |
| Wed | Inheritance | 2 hrs | Exercises |
| Thu | Magic Methods & Dunder | 2 hrs | Quiz |
| Fri | **Project**: Bank Account System | 3 hrs | Submit |
| Sat-Sun | Phase 1 Assessment | 5 hrs | Test |
---
## 📅 Phase 2: Intermediate (Weeks 5-10)
### Weeks 5-6: Working with Data
- JSON handling
- CSV/Excel files
- Basic data analysis with Pandas
- **Mini-project**: Data Analysis Report Generator
### Weeks 7-8: Web Fundamentals
- HTTP & APIs
- Flask framework basics
- Routing & templates
- **Mini-project**: Personal Blog
### Weeks 9-10: Databases & ORM
- SQL fundamentals
- SQLite & PostgreSQL
- SQLAlchemy ORM
- **Mini-project**: Task Management API
---
## 📅 Phase 3: Advanced & Portfolio (Weeks 11-18)
### Weeks 11-12: Advanced Topics
- Testing (pytest)
- Error handling & logging
- Decorators & generators
- Context managers
### Weeks 13-14: Specialization
Choose ONE track:
- **Track A**: Django + Full Stack
- **Track B**: Data Science & ML
- **Track C**: Automation & Scripts
### Weeks 15-16: Portfolio Development
- Design and plan capstone project
- Implement core features
- Code review & refactor
### Weeks 17-18: Career Preparation
- GitHub profile optimization
- Resume & LinkedIn
- Mock interviews
- Technical interview practice
---
## 📚 Recommended Resources
### Free Resources
- Python.org Official Tutorial
- CS50P (Harvard's Python Course)
- Real Python Articles
### Paid Resources
- "Automate the Boring Stuff" (Book)
- Udemy Python Bootcamp
- Codecademy Pro
### Practice Platforms
- LeetCode (Easy-Medium)
- HackerRank
- Project Euler
---
## ⏰ Weekly Time Allocation
| Activity | Hours/Week |
|----------|------------|
| Video Lectures | 4 hrs |
| Coding Practice | 6 hrs |
| Projects | 4 hrs |
| Review & Reading | 1 hr |
---
## 🏆 Milestone Checkpoints
| Week | Milestone | Verification |
|------|-----------|--------------|
| 4 | Phase 1 Complete | Pass assessment |
| 10 | Phase 2 Complete | Portfolio piece #2 |
| 16 | Phase 3 Complete | Portfolio piece #3 |
| 18 | Job-Ready | Interview ready |
---
## 💡 Learning Tips
1. **Code every day** - Even 30 minutes helps
2. **Build projects** - Real-world application beats tutorials
3. **Teach others** - Explaining reinforces learning
4. **Join communities** - Reddit, Discord, local meetups
5. **Embrace struggle** - Confusion means growth
Tips
- Start with "why" before "how"
- Build projects at each phase
- Review previous material weekly
- Join learning communities for accountability
- Adjust pace based on difficulty
Usage Guidance
This skill appears internally consistent and low-risk because it's instruction-only and requests no credentials. Before installing: (1) confirm you trust the skill author if you care about provenance, (2) avoid pasting sensitive or personally identifiable information into prompts or assessments the skill might use, (3) treat recommended external paid resources as unvetted—verify links/providers before paying, and (4) if you prefer tighter control, disable autonomous invocation for this skill in your agent settings so it only runs when you explicitly invoke it.
Capability Analysis
Type: OpenClaw Skill
Name: learning-path-generator
Version: 1.0.0
The skill bundle is purely informational and contains no executable code or malicious instructions. It provides a structured template and guidelines for an AI agent to generate educational learning paths, as seen in SKILL.md, with no evidence of data exfiltration, prompt injection, or unauthorized system access.
Capability Assessment
Purpose & Capability
The skill name and description match the SKILL.md content: creating personalized learning plans, assessments, schedules, and resource suggestions. It declares no binaries, credentials, or config paths that would be unrelated to that purpose.
Instruction Scope
The SKILL.md contains only guidance for assessing skill level, defining goals, designing curricula, scheduling, and recommending resources. It does not instruct the agent to read system files, access environment variables, call arbitrary external endpoints, or exfiltrate data.
Install Mechanism
There is no install spec and no code files (instruction-only). Nothing will be written to disk or downloaded by the skill itself during installation, which minimizes install-time risk.
Credentials
No environment variables, credentials, or config paths are required. The declared requirements are proportionate to a pure instructional/curriculum tool.
Persistence & Privilege
always:false (default) and the skill does not request persistent or elevated privileges or modifications to other skills' configurations. Autonomous invocation is allowed by platform default but is not escalated by the skill's design.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install learning-path-generator - After installation, invoke the skill by name or use
/learning-path-generator - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of Learning Path Generator.
- Generate personalized, structured learning paths for any skill or subject.
- Features include skill assessment, goal setting, curriculum design, time planning, resource curation, and progress tracking.
- Allows users to define goals, assess proficiency, and receive optimized, phase-based curriculums with milestones and resource recommendations.
- Supports customizable plans based on learning objectives, available time, and current skill level.
- Example curriculum included for mastering Python programming from beginner to job-ready.
Metadata
Frequently Asked Questions
What is learning-path-generator?
Generate personalized learning paths and curriculum for any skill or subject. It is an AI Agent Skill for Claude Code / OpenClaw, with 137 downloads so far.
How do I install learning-path-generator?
Run "/install learning-path-generator" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is learning-path-generator free?
Yes, learning-path-generator is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does learning-path-generator support?
learning-path-generator is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created learning-path-generator?
It is built and maintained by huajianjiu (@huajianjiu000); the current version is v1.0.0.
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