[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6H7F3R3DBF8WXE729PAM508":3,"skill-same:01M6H7F3R3DBF8WXE729PAM508":69},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":6,"ownerHtmlUrl":53,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":54,"install":55,"resources":68},"01M6H7F3R3DBF8WXE729PAM508","langchain-hello-world",null,"Create a minimal working LangChain example.\nUse when starting a new LangChain integration, testing your setup,\nor learning basic LangChain patterns with chains and prompts.\nTrigger with phrases like \"langchain hello world\", \"langchain example\",\n\"langchain quick start\", \"simple langchain code\", \"first langchain app\".","micsapp\u002Fmicstec-skills","micsapp","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F82411001?v=4","01M6GWCFQ6D3PV8EHNY62NN5VW","01M6GWCFVE1PA5HG0DKC7AMFJ3",4,"MIT","https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fclaude-code\u002Fplugins-plus-skills\u002Fplugins\u002Fsaas-packs\u002Flangchain-pack\u002Fskills\u002Flangchain-hello-world\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"hello world","langchain hello world","world","langchain","hello","code","learning","create","use","example","minimal","simple","basic","app","io","trigger","quick start","with","quick","testing","2026-11-10 10:12:30",false,"---\nname: langchain-hello-world\ndescription: |\n  Create a minimal working LangChain example.\n  Use when starting a new LangChain integration, testing your setup,\n  or learning basic LangChain patterns with chains and prompts.\n  Trigger with phrases like \"langchain hello world\", \"langchain example\",\n  \"langchain quick start\", \"simple langchain code\", \"first langchain app\".\nallowed-tools: Read, Write, Edit\nversion: 1.0.0\nlicense: MIT\nauthor: Jeremy Longshore \u003Cjeremy@intentsolutions.io>\ncompatible-with: claude-code, codex, openclaw\n---\n# LangChain Hello World\n\n## Overview\nMinimal working example demonstrating core LangChain functionality with chains and prompts.\n\n## Prerequisites\n- Completed `langchain-install-auth` setup\n- Valid LLM provider API credentials configured\n- Python 3.9+ or Node.js 18+ environment ready\n\n## Instructions\n\n### Step 1: Create Entry File\nCreate a new file `hello_langchain.py` for your hello world example.\n\n### Step 2: Import and Initialize\n```python\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.prompts import ChatPromptTemplate\n\nllm = ChatOpenAI(model=\"gpt-4o-mini\")\n```\n\n### Step 3: Create Your First Chain\n```python\nfrom langchain_core.output_parsers import StrOutputParser\n\nprompt = ChatPromptTemplate.from_messages([\n    (\"system\", \"You are a helpful assistant.\"),\n    (\"user\", \"{input}\")\n])\n\nchain = prompt | llm | StrOutputParser()\n\nresponse = chain.invoke({\"input\": \"Hello, LangChain!\"})\nprint(response)\n```\n\n## Output\n- Working Python file with LangChain chain\n- Successful LLM response confirming connection\n- Console output showing:\n```\nHello! I'm your LangChain-powered assistant. How can I help you today?\n```\n\n## Error Handling\n| Error | Cause | Solution |\n|-------|-------|----------|\n| Import Error | SDK not installed | Run `pip install langchain langchain-openai` |\n| Auth Error | Invalid credentials | Check environment variable is set |\n| Timeout | Network issues | Increase timeout or check connectivity |\n| Rate Limit | Too many requests | Wait and retry with exponential backoff |\n| Model Not Found | Invalid model name | Check available models in provider docs |\n\n## Examples\n\n### Simple Chain (Python)\n```python\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langchain_core.output_parsers import StrOutputParser\n\nllm = ChatOpenAI(model=\"gpt-4o-mini\")\nprompt = ChatPromptTemplate.from_template(\"Tell me a joke about {topic}\")\nchain = prompt | llm | StrOutputParser()\n\nresult = chain.invoke({\"topic\": \"programming\"})\nprint(result)\n```\n\n### With Memory (Python)\n```python\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\nfrom langchain_core.messages import HumanMessage, AIMessage\n\nllm = ChatOpenAI(model=\"gpt-4o-mini\")\nprompt = ChatPromptTemplate.from_messages([\n    (\"system\", \"You are a helpful assistant.\"),\n    MessagesPlaceholder(variable_name=\"history\"),\n    (\"user\", \"{input}\")\n])\n\nchain = prompt | llm\n\nhistory = []\nresponse = chain.invoke({\"input\": \"Hi!\", \"history\": history})\nprint(response.content)\n```\n\n### TypeScript Example\n```typescript\nimport { ChatOpenAI } from \"@langchain\u002Fopenai\";\nimport { ChatPromptTemplate } from \"@langchain\u002Fcore\u002Fprompts\";\nimport { StringOutputParser } from \"@langchain\u002Fcore\u002Foutput_parsers\";\n\nconst llm = new ChatOpenAI({ modelName: \"gpt-4o-mini\" });\nconst prompt = ChatPromptTemplate.fromTemplate(\"Tell me about {topic}\");\nconst chain = prompt.pipe(llm).pipe(new StringOutputParser());\n\nconst result = await chain.invoke({ topic: \"LangChain\" });\nconsole.log(result);\n```\n\n## Resources\n- [LangChain LCEL Guide](https:\u002F\u002Fpython.langchain.com\u002Fdocs\u002Fconcepts\u002Flcel\u002F)\n- [Prompt Templates](https:\u002F\u002Fpython.langchain.com\u002Fdocs\u002Fconcepts\u002Fprompt_templates\u002F)\n- [Output Parsers](https:\u002F\u002Fpython.langchain.com\u002Fdocs\u002Fconcepts\u002Foutput_parsers\u002F)\n\n## Next Steps\nProceed to `langchain-local-dev-loop` for development workflow setup.","# LangChain Hello World\n\n## Overview\nMinimal working example demonstrating core LangChain functionality with chains and prompts.\n\n## Prerequisites\n- Completed `langchain-install-auth` setup\n- Valid LLM provider API credentials configured\n- Python 3.9+ or Node.js 18+ environment ready\n\n## Instructions\n\n### Step 1: Create Entry File\nCreate a new file `hello_langchain.py` for your hello world example.\n\n### Step 2: Import and Initialize\n```python\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.prompts import ChatPromptTemplate\n\nllm = ChatOpenAI(model=\"gpt-4o-mini\")\n```\n\n### Step 3: Create Your First Chain\n```python\nfrom langchain_core.output_parsers import StrOutputParser\n\nprompt = ChatPromptTemplate.from_messages([\n    (\"system\", \"You are a helpful assistant.\"),\n    (\"user\", \"{input}\")\n])\n\nchain = prompt | llm | StrOutputParser()\n\nresponse = chain.invoke({\"input\": \"Hello, LangChain!\"})\nprint(response)\n```\n\n## Output\n- Working Python file with LangChain chain\n- Successful LLM response confirming connection\n- Console output showing:\n```\nHello! I'm your LangChain-powered assistant. How can I help you today?\n```\n\n## Error Handling\n| Error | Cause | Solution |\n|-------|-------|----------|\n| Import Error | SDK not installed | Run `pip install langchain langchain-openai` |\n| Auth Error | Invalid credentials | Check environment variable is set |\n| Timeout | Network issues | Increase timeout or check connectivity |\n| Rate Limit | Too many requests | Wait and retry with exponential backoff |\n| Model Not Found | Invalid model name | Check available models in provider docs |\n\n## Examples\n\n### Simple Chain (Python)\n```python\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.prompts import ChatPromptTemplate\nfrom langchain_core.output_parsers import StrOutputParser\n\nllm = ChatOpenAI(model=\"gpt-4o-mini\")\nprompt = ChatPromptTemplate.from_template(\"Tell me a joke about {topic}\")\nchain = prompt | llm | StrOutputParser()\n\nresult = chain.invoke({\"topic\": \"programming\"})\nprint(result)\n```\n\n### With Memory (Python)\n```python\nfrom langchain_openai import ChatOpenAI\nfrom langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\nfrom langchain_core.messages import HumanMessage, AIMessage\n\nllm = ChatOpenAI(model=\"gpt-4o-mini\")\nprompt = ChatPromptTemplate.from_messages([\n    (\"system\", \"You are a helpful assistant.\"),\n    MessagesPlaceholder(variable_name=\"history\"),\n    (\"user\", \"{input}\")\n])\n\nchain = prompt | llm\n\nhistory = []\nresponse = chain.invoke({\"input\": \"Hi!\", \"history\": history})\nprint(response.content)\n```\n\n### TypeScript Example\n```typescript\nimport { ChatOpenAI } from \"@langchain\u002Fopenai\";\nimport { ChatPromptTemplate } from \"@langchain\u002Fcore\u002Fprompts\";\nimport { StringOutputParser } from \"@langchain\u002Fcore\u002Foutput_parsers\";\n\nconst llm = new ChatOpenAI({ modelName: \"gpt-4o-mini\" });\nconst prompt = ChatPromptTemplate.fromTemplate(\"Tell me about {topic}\");\nconst chain = prompt.pipe(llm).pipe(new StringOutputParser());\n\nconst result = await chain.invoke({ topic: \"LangChain\" });\nconsole.log(result);\n```\n\n## Resources\n- [LangChain LCEL Guide](https:\u002F\u002Fpython.langchain.com\u002Fdocs\u002Fconcepts\u002Flcel\u002F)\n- [Prompt Templates](https:\u002F\u002Fpython.langchain.com\u002Fdocs\u002Fconcepts\u002Fprompt_templates\u002F)\n- [Output Parsers](https:\u002F\u002Fpython.langchain.com\u002Fdocs\u002Fconcepts\u002Foutput_parsers\u002F)\n\n## Next Steps\nProceed to `langchain-local-dev-loop` for development workflow setup.",{"name":5,"author":43,"license":14,"version":44,"description":45,"allowed-tools":46,"compatible-with":47},"Jeremy Longshore \u003Cjeremy@intentsolutions.io>","1.0.0","Create a minimal working LangChain example.\nUse when starting a new LangChain integration, testing your setup,\nor learning basic LangChain patterns with chains and prompts.\nTrigger with phrases like \"langchain hello world\", \"langchain example\",\n\"langchain quick start\", \"simple langchain code\", \"first langchain app\".\n","Read, Write, Edit","claude-code, codex, openclaw","master","3f5d983eb4ef375b749adcd6b07ec03bd82ae8a8","claude-code\u002Fplugins-plus-skills\u002Fplugins\u002Fsaas-packs\u002Flangchain-pack\u002Fskills\u002Flangchain-hello-world",0,"https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills","https:\u002F\u002Fgithub.com\u002Fmicsapp","2026-03-20 11:24:18",{"source":8,"skillName":5,"commands":56,"prompt":66,"zipAvailable":67},[57,60,63],{"tool":58,"command":59},"npx","npx skills add micsapp\u002Fmicstec-skills --skill langchain-hello-world",{"tool":61,"command":62},"bunx","bunx skills add micsapp\u002Fmicstec-skills --skill langchain-hello-world",{"tool":64,"command":65},"pnpm","pnpm dlx skills add micsapp\u002Fmicstec-skills --skill langchain-hello-world","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmicsapp\u002Fmicstec-skills\u002Fblob\u002Fmaster\u002Fclaude-code\u002Fplugins-plus-skills\u002Fplugins\u002Fsaas-packs\u002Flangchain-pack\u002Fskills\u002Flangchain-hello-world\u002FSKILL.md",true,[],[70,107,136,156,176,200],{"skillNo":71,"name":72,"nameZh":6,"description":73,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":74,"categories":75,"keywords":86,"publishedAt":38},"01M6HBTJ02FXBFG9NYEHHYAP52","azure-apim-architecture","Analyzes and explains Azure API Management architecture decisions for enterprise API marketplace implementations using VNet Internal mode, Front Door, hybrid authentication, and multi-environment strategies. 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