Learning Guide

Overview

This skill provides a comprehensive learning path for any technical or non-technical topic. When triggered by user requests like "帮我学习一下Python" or "帮我学习一下机器学习", it executes a structured 9-step learning journey that covers foundational concepts, practical applications, community resources, and advanced learning pathways.

When to Use This Skill

Use this skill when a user explicitly asks to learn something using patterns such as:

• "帮我学习一下xx技能/知识"
• "我想学习xx"
• "教我学习xx"
• "介绍一下如何学习xx"

This skill is particularly designed for comprehensive learning requests, not quick reference questions.

9-Step Learning Journey

When executing a learning request, follow this structured flow to provide comprehensive coverage of the topic:

Step 1: Define the Knowledge Domain

Search for and provide a clear, comprehensive definition of the topic. Include key characteristics, core concepts, and fundamental principles.

Tools to use:

• WebSearch: Use targeted search queries like "What is [topic]", "[topic] definition", "[topic] explained"

Expected output:

• Clear definition in 2-3 paragraphs
• Key characteristics and core concepts
• Historical context or origin if relevant

Step 2: Analyze Application Domains

Identify where this knowledge is commonly applied, its place in the field hierarchy, and related complementary knowledge.

Tools to use:

• WebSearch: "[topic] use cases", "[topic] applications", "when to use [topic]"

Expected output:

• Primary application domains (3-5 areas)
• Hierarchy position in the field
• Complementary technologies or knowledge areas that work well together

Step 3: Practical Usage Examples

Provide concrete examples showing how the knowledge is used in real-world scenarios including code examples, workflows, or case studies.

Tools to use:

• WebSearch: "[topic] tutorial", "[topic] examples", "[topic] getting started"

Expected output:

• 2-3 practical examples
• Code snippets or workflow diagrams where applicable
• Step-by-step demonstrations for complex concepts

Step 4: Discover GitHub Repositories

Find and summarize up to 3 popular GitHub repositories related to the topic.

Tools to use:

• WebSearch: "[topic] GitHub", "best [topic] repositories", "[topic] GitHub trending"

Expected output:

• Repository names with links
• Brief description of each (2-3 sentences)
• Star counts and last update information if available
• Key features and use cases for each repo

Step 5: Recommend Core Books

Identify and recommend essential books for mastering the topic.

Tools to use:

• WebSearch: "best [topic] books", "[topic] reading list", "[topic] must-read books"

Expected output:

• 3-5 book recommendations
• Author names and brief descriptions
• Target audience (beginner, intermediate, advanced)
• Why each book is recommended

Step 6: Identify Field Experts

Find prominent experts in the field and provide their contact information and resources.

Tools to use:

• WebSearch: "[topic] experts", "[topic] influencers", "[topic] thought leaders"

Expected output:

• 3-5 expert names
• Brief credentials and contributions
• Blog URLs
• Twitter/Social media links
• GitHub profiles if relevant

Step 7: Track HackerNews Discussions

Search HackerNews for recent discussions about the topic to show current trends and community sentiment.

Tools to use:

• WebSearch: "site:news.ycombinator.com [topic]", "[topic] hacker news"

Expected output:

• 2-3 recent relevant discussions
• Brief summary of each discussion
• Community sentiment and key insights

Step 8: Identify Community Resources

Find where to track the latest developments in this knowledge area including specialized news sites, forums, and communities.

Tools to use:

• WebSearch: "[topic] community", "[topic] forum", "[topic] news", "[topic] latest developments"

Expected output:

• 3-5 community resources
• Brief description of each resource
• Update frequency and content focus

Step 9: Suggest Related Topics

Recommend closely related knowledge areas for expanded learning.

Tools to use:

• WebSearch: "[topic] related technologies", "learning path [topic]", "what to learn after [topic]"

Expected output:

• 3-5 related topics
• How each relates to the main topic
• Learning sequence recommendations
• Brief explanation of why they're relevant