OpenClaw 30-Day Learning Plan: From Beginner to Skill Development Master

Hello everyone, this is programmer Wan Feng actively working on various AI projects.

Do you also want to:

  • Have your own personal AI assistant?
  • Automate daily workflows?
  • Develop custom skills to improve efficiency?
  • But don't know where to start?

This 30-day learning plan helps you:

  • ✅ Systematically master OpenClaw core functions
  • ✅ Develop personal skills from 0 to 1
  • ✅ Build automated workflows
  • ✅ Integrate into open source community

📅 Learning Roadmap

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Week 1: Basic Introduction (Install + Configure + Basic Skills)
Week 2: Advanced Usage (Workflows + Integration + Automation)
Week 3: Skill Development (SDK + Practice + Debugging)
Week 4: Project Practice (Comprehensive Projects + Community Contribution)

Week 1: Basic Introduction

Day 1-2: Environment Setup

Learning Objectives:

  • Successfully install OpenClaw
  • Complete basic configuration
  • Run your first skill

Specific Tasks:

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# 1. Install
curl -fsSL https://openclaw.ai/install.sh | bash

# 2. Configure model
openclaw config set model claude-sonnet-4
openclaw config set api_key $YOUR_API_KEY

# 3. Start service
openclaw start

# 4. Test
openclaw status

Acceptance Criteria:

  • Service started normally
  • Can send messages and get replies
  • Understand basic directory structure

Day 3-4: Basic Skill Usage

Learning Objectives:

  • Understand skill concept
  • Use preset skills
  • Modify simple configurations

Practice Skills:

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# Skill 1: Weather query
skills/weather.yaml:
  trigger: "weather"
  action: weather_query
  location: auto

# Skill 2: To-do items
skills/todo.yaml:
  trigger: "remind me"
  action: create_todo
  storage: local

# Skill 3: Document search
skills/search.yaml:
  trigger: "search"
  action: semantic_search
  index: ~/docs

Acceptance Criteria:

  • Successfully run 3 preset skills
  • Understand YAML configuration format
  • Can modify parameters

Day 5-7: Message Platform Integration

Learning Objectives:

  • Integrate WeChat/Telegram
  • Understand message routing
  • Configure auto-reply

Practice Project: WeChat Auto-Reply

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# skills/wechat-reply.py
from openclaw import Skill

class WechatReply(Skill):
    def __init__(self):
        self.keywords = {
            "Are you there": "Hello! How can I help you?",
            "Working hours": "Monday to Friday 9:00-18:00",
            "Address": "Chaoyang District, Beijing xxx",
        }

    async def handle(self, message):
        for keyword, reply in self.keywords.items():
            if keyword in message:
                return reply
        return "Got it, will reply shortly~"

Acceptance Criteria:

  • WeChat message auto-reply
  • Accurate keyword recognition
  • 7×24 hour operation

Week 2: Advanced Usage

Day 8-10: Workflow Automation

Learning Objectives:

  • Understand workflow concept
  • Design multi-step processes
  • Implement conditional branching

Practice Project: Auto Daily Report Generation

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# skills/daily-report.py
from openclaw import Workflow, cron

@cron("0 18 * * *")  # Every day at 6 PM
class DailyReport(Workflow):
    def run(self):
        # 1. Get today's data
        stats = self.get_today_stats()

        # 2. AI generate summary
        summary = self.ai_generate(stats)

        # 3. Send to group
        self.send_to_group("Daily Report", summary)

        # 4. Log
        self.log("Daily report sent")

Acceptance Criteria:

  • Scheduled task runs normally
  • Daily report content accurate
  • Exception handling complete

Day 11-12: Knowledge Base Integration

Learning Objectives:

  • Build personal knowledge base
  • Implement semantic search
  • AI intelligent Q&A

Practice Project: Personal Knowledge Assistant

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# skills/knowledge-base.py
from openclaw import Skill
from openclaw.vector import VectorStore

class KnowledgeBase(Skill):
    def __init__(self):
        self.store = VectorStore("~/knowledge")
        self.store.index("~/documents")

    async def handle(self, query):
        # Semantic search
        results = self.store.search(query, top_k=5)

        # AI generate answer
        answer = self.ai_generate(query, results)

        return answer

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