[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CDY49757TMZDVGZN5VD14W":3,"skill-same:01M6CDY49757TMZDVGZN5VD14W":88},{"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":22,"publishedAt":35,"linkOnly":36,"markdownContent":37,"markdownBody":38,"frontMatter":39,"refName":41,"commitSha":42,"skillDirectory":43,"forksCount":13,"repositoryHtmlUrl":44,"repositoryHomepageUrl":6,"ownerHtmlUrl":45,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":46,"install":47,"resources":60},"01M6CDY49757TMZDVGZN5VD14W","llm-application-dev",null,"Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.","jijingkun-commits\u002Ffastapi","jijingkun-commits","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F240866387?v=4","01M6CDWBED8HV9VJWHVGFZR01X","01M6CDWBJMJ6C3CATJADQ7MNHS",0,"MIT","https:\u002F\u002Fgithub.com\u002Fjijingkun-commits\u002Ffastapi\u002Fblob\u002Fmaster\u002F.cursor\u002Fskills\u002Fllm-application-dev\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34],"dev","llm application dev","large language models","prompt engineering","automation","rag","integration","engineering","applications","prompt","use","models","2026-11-10 10:12:30",false,"---\nname: llm-application-dev\ndescription: Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.\nsource: wshobson\u002Fagents\nlicense: MIT\n---\n\n# LLM Application Development\n\n## Prompt Engineering\n\n### Structured Prompts\n```typescript\nconst systemPrompt = `You are a helpful assistant that answers questions about our product.\n\nRULES:\n- Only answer questions about our product\n- If you don't know, say \"I don't know\"\n- Keep responses concise (under 100 words)\n- Never make up information\n\nCONTEXT:\n{context}`;\n\nconst userPrompt = `Question: {question}`;\n```\n\n### Few-Shot Examples\n```typescript\nconst prompt = `Classify the sentiment of customer feedback.\n\nExamples:\nInput: \"Love this product!\"\nOutput: positive\n\nInput: \"Worst purchase ever\"\nOutput: negative\n\nInput: \"It works fine\"\nOutput: neutral\n\nInput: \"${customerFeedback}\"\nOutput:`;\n```\n\n### Chain of Thought\n```typescript\nconst prompt = `Solve this step by step:\n\nQuestion: ${question}\n\nLet's think through this:\n1. First, identify the key information\n2. Then, determine the approach\n3. Finally, calculate the answer\n\nStep-by-step solution:`;\n```\n\n## API Integration\n\n### OpenAI Pattern\n```typescript\nimport OpenAI from 'openai';\n\nconst openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });\n\nasync function chat(messages: Message[]): Promise\u003Cstring> {\n  const response = await openai.chat.completions.create({\n    model: 'gpt-4',\n    messages,\n    temperature: 0.7,\n    max_tokens: 500,\n  });\n\n  return response.choices[0].message.content ?? '';\n}\n```\n\n### Anthropic Pattern\n```typescript\nimport Anthropic from '@anthropic-ai\u002Fsdk';\n\nconst anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });\n\nasync function chat(prompt: string): Promise\u003Cstring> {\n  const response = await anthropic.messages.create({\n    model: 'claude-3-opus-20240229',\n    max_tokens: 1024,\n    messages: [{ role: 'user', content: prompt }],\n  });\n\n  return response.content[0].type === 'text'\n    ? response.content[0].text\n    : '';\n}\n```\n\n### Streaming Responses\n```typescript\nasync function* streamChat(prompt: string) {\n  const stream = await openai.chat.completions.create({\n    model: 'gpt-4',\n    messages: [{ role: 'user', content: prompt }],\n    stream: true,\n  });\n\n  for await (const chunk of stream) {\n    const content = chunk.choices[0]?.delta?.content;\n    if (content) yield content;\n  }\n}\n```\n\n## RAG (Retrieval-Augmented Generation)\n\n### Basic RAG Pipeline\n```typescript\nasync function ragQuery(question: string): Promise\u003Cstring> {\n  \u002F\u002F 1. Embed the question\n  const questionEmbedding = await embedText(question);\n\n  \u002F\u002F 2. Search vector database\n  const relevantDocs = await vectorDb.search(questionEmbedding, { limit: 5 });\n\n  \u002F\u002F 3. Build context\n  const context = relevantDocs.map(d => d.content).join('\\n\\n');\n\n  \u002F\u002F 4. Generate answer\n  const prompt = `Answer based on this context:\\n${context}\\n\\nQuestion: ${question}`;\n  return await chat(prompt);\n}\n```\n\n### Document Chunking\n```typescript\nfunction chunkDocument(text: string, options: ChunkOptions): string[] {\n  const { chunkSize = 1000, overlap = 200 } = options;\n  const chunks: string[] = [];\n\n  let start = 0;\n  while (start \u003C text.length) {\n    const end = Math.min(start + chunkSize, text.length);\n    chunks.push(text.slice(start, end));\n    start += chunkSize - overlap;\n  }\n\n  return chunks;\n}\n```\n\n### Embedding Storage\n```typescript\n\u002F\u002F Using Supabase with pgvector\nasync function storeEmbeddings(docs: Document[]) {\n  for (const doc of docs) {\n    const embedding = await embedText(doc.content);\n\n    await supabase.from('documents').insert({\n      content: doc.content,\n      metadata: doc.metadata,\n      embedding: embedding,  \u002F\u002F vector column\n    });\n  }\n}\n\nasync function searchSimilar(query: string, limit = 5) {\n  const embedding = await embedText(query);\n\n  const { data } = await supabase.rpc('match_documents', {\n    query_embedding: embedding,\n    match_count: limit,\n  });\n\n  return data;\n}\n```\n\n## Error Handling\n\n```typescript\nasync function safeLLMCall\u003CT>(\n  fn: () => Promise\u003CT>,\n  options: { retries?: number; fallback?: T }\n): Promise\u003CT> {\n  const { retries = 3, fallback } = options;\n\n  for (let i = 0; i \u003C retries; i++) {\n    try {\n      return await fn();\n    } catch (error) {\n      if (error.status === 429) {\n        \u002F\u002F Rate limit - exponential backoff\n        await sleep(Math.pow(2, i) * 1000);\n        continue;\n      }\n      if (i === retries - 1) {\n        if (fallback !== undefined) return fallback;\n        throw error;\n      }\n    }\n  }\n  throw new Error('Max retries exceeded');\n}\n```\n\n## Best Practices\n\n- **Token Management**: Track usage and set limits\n- **Caching**: Cache embeddings and common queries\n- **Evaluation**: Test prompts with diverse inputs\n- **Guardrails**: Validate outputs before using\n- **Logging**: Log prompts and responses for debugging\n- **Cost Control**: Use cheaper models for simple tasks\n- **Latency**: Stream responses for better UX\n- **Privacy**: Don't send PII to external APIs\n","# LLM Application Development\n\n## Prompt Engineering\n\n### Structured Prompts\n```typescript\nconst systemPrompt = `You are a helpful assistant that answers questions about our product.\n\nRULES:\n- Only answer questions about our product\n- If you don't know, say \"I don't know\"\n- Keep responses concise (under 100 words)\n- Never make up information\n\nCONTEXT:\n{context}`;\n\nconst userPrompt = `Question: {question}`;\n```\n\n### Few-Shot Examples\n```typescript\nconst prompt = `Classify the sentiment of customer feedback.\n\nExamples:\nInput: \"Love this product!\"\nOutput: positive\n\nInput: \"Worst purchase ever\"\nOutput: negative\n\nInput: \"It works fine\"\nOutput: neutral\n\nInput: \"${customerFeedback}\"\nOutput:`;\n```\n\n### Chain of Thought\n```typescript\nconst prompt = `Solve this step by step:\n\nQuestion: ${question}\n\nLet's think through this:\n1. First, identify the key information\n2. Then, determine the approach\n3. Finally, calculate the answer\n\nStep-by-step solution:`;\n```\n\n## API Integration\n\n### OpenAI Pattern\n```typescript\nimport OpenAI from 'openai';\n\nconst openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });\n\nasync function chat(messages: Message[]): Promise\u003Cstring> {\n  const response = await openai.chat.completions.create({\n    model: 'gpt-4',\n    messages,\n    temperature: 0.7,\n    max_tokens: 500,\n  });\n\n  return response.choices[0].message.content ?? '';\n}\n```\n\n### Anthropic Pattern\n```typescript\nimport Anthropic from '@anthropic-ai\u002Fsdk';\n\nconst anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });\n\nasync function chat(prompt: string): Promise\u003Cstring> {\n  const response = await anthropic.messages.create({\n    model: 'claude-3-opus-20240229',\n    max_tokens: 1024,\n    messages: [{ role: 'user', content: prompt }],\n  });\n\n  return response.content[0].type === 'text'\n    ? response.content[0].text\n    : '';\n}\n```\n\n### Streaming Responses\n```typescript\nasync function* streamChat(prompt: string) {\n  const stream = await openai.chat.completions.create({\n    model: 'gpt-4',\n    messages: [{ role: 'user', content: prompt }],\n    stream: true,\n  });\n\n  for await (const chunk of stream) {\n    const content = chunk.choices[0]?.delta?.content;\n    if (content) yield content;\n  }\n}\n```\n\n## RAG (Retrieval-Augmented Generation)\n\n### Basic RAG Pipeline\n```typescript\nasync function ragQuery(question: string): Promise\u003Cstring> {\n  \u002F\u002F 1. Embed the question\n  const questionEmbedding = await embedText(question);\n\n  \u002F\u002F 2. Search vector database\n  const relevantDocs = await vectorDb.search(questionEmbedding, { limit: 5 });\n\n  \u002F\u002F 3. Build context\n  const context = relevantDocs.map(d => d.content).join('\\n\\n');\n\n  \u002F\u002F 4. Generate answer\n  const prompt = `Answer based on this context:\\n${context}\\n\\nQuestion: ${question}`;\n  return await chat(prompt);\n}\n```\n\n### Document Chunking\n```typescript\nfunction chunkDocument(text: string, options: ChunkOptions): string[] {\n  const { chunkSize = 1000, overlap = 200 } = options;\n  const chunks: string[] = [];\n\n  let start = 0;\n  while (start \u003C text.length) {\n    const end = Math.min(start + chunkSize, text.length);\n    chunks.push(text.slice(start, end));\n    start += chunkSize - overlap;\n  }\n\n  return chunks;\n}\n```\n\n### Embedding Storage\n```typescript\n\u002F\u002F Using Supabase with pgvector\nasync function storeEmbeddings(docs: Document[]) {\n  for (const doc of docs) {\n    const embedding = await embedText(doc.content);\n\n    await supabase.from('documents').insert({\n      content: doc.content,\n      metadata: doc.metadata,\n      embedding: embedding,  \u002F\u002F vector column\n    });\n  }\n}\n\nasync function searchSimilar(query: string, limit = 5) {\n  const embedding = await embedText(query);\n\n  const { data } = await supabase.rpc('match_documents', {\n    query_embedding: embedding,\n    match_count: limit,\n  });\n\n  return data;\n}\n```\n\n## Error Handling\n\n```typescript\nasync function safeLLMCall\u003CT>(\n  fn: () => Promise\u003CT>,\n  options: { retries?: number; fallback?: T }\n): Promise\u003CT> {\n  const { retries = 3, fallback } = options;\n\n  for (let i = 0; i \u003C retries; i++) {\n    try {\n      return await fn();\n    } catch (error) {\n      if (error.status === 429) {\n        \u002F\u002F Rate limit - exponential backoff\n        await sleep(Math.pow(2, i) * 1000);\n        continue;\n      }\n      if (i === retries - 1) {\n        if (fallback !== undefined) return fallback;\n        throw error;\n      }\n    }\n  }\n  throw new Error('Max retries exceeded');\n}\n```\n\n## Best Practices\n\n- **Token Management**: Track usage and set limits\n- **Caching**: Cache embeddings and common queries\n- **Evaluation**: Test prompts with diverse inputs\n- **Guardrails**: Validate outputs before using\n- **Logging**: Log prompts and responses for debugging\n- **Cost Control**: Use cheaper models for simple tasks\n- **Latency**: Stream responses for better UX\n- **Privacy**: Don't send PII to external APIs\n",{"name":5,"source":40,"license":14,"description":7},"wshobson\u002Fagents","master","21d03858f4c4be97b1feaa9e948ca3c5bf3e8357",".cursor\u002Fskills\u002Fllm-application-dev","https:\u002F\u002Fgithub.com\u002Fjijingkun-commits\u002Ffastapi","https:\u002F\u002Fgithub.com\u002Fjijingkun-commits","2026-03-14 11:41:49",{"source":8,"skillName":5,"commands":48,"prompt":58,"zipAvailable":59},[49,52,55],{"tool":50,"command":51},"npx","npx skills add jijingkun-commits\u002Ffastapi --skill llm-application-dev",{"tool":53,"command":54},"bunx","bunx skills add jijingkun-commits\u002Ffastapi --skill llm-application-dev",{"tool":56,"command":57},"pnpm","pnpm dlx skills add jijingkun-commits\u002Ffastapi --skill llm-application-dev","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fjijingkun-commits\u002Ffastapi\u002Fblob\u002Fmaster\u002F.cursor\u002Fskills\u002Fllm-application-dev\u002FSKILL.md",true,[61,67,72,76,80,84],{"path":62,"resourceType":63,"resourceTypeDesc":64,"sizeBytes":65,"sourceHtmlUrl":66},".skill-meta.json","REFERENCE","参考文档",147,"https:\u002F\u002Fgithub.com\u002Fjijingkun-commits\u002Ffastapi\u002Fblob\u002Fmaster\u002F.cursor\u002Fskills\u002Fllm-application-dev\u002F.skill-meta.json",{"path":68,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":70,"sourceHtmlUrl":71},"LICENSE","许可证",1067,"https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fdev-tools\u002Friaomike-zkp-security-review\u002FLICENSE",{"path":73,"resourceType":63,"resourceTypeDesc":64,"sizeBytes":74,"sourceHtmlUrl":75},"references\u002Fevaluation-scenarios.md",895,"https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fdev-tools\u002Friaomike-zkp-security-review\u002Freferences\u002Fevaluation-scenarios.md",{"path":77,"resourceType":63,"resourceTypeDesc":64,"sizeBytes":78,"sourceHtmlUrl":79},"references\u002Fprotocol.md",2526,"https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fdev-tools\u002Friaomike-zkp-security-review\u002Freferences\u002Fprotocol.md",{"path":81,"resourceType":63,"resourceTypeDesc":64,"sizeBytes":82,"sourceHtmlUrl":83},"SOURCE.md",381,"https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fdev-tools\u002Friaomike-zkp-security-review\u002FSOURCE.md",{"path":85,"resourceType":63,"resourceTypeDesc":64,"sizeBytes":86,"sourceHtmlUrl":87},"TRUST.auto.yaml",2217,"https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fdev-tools\u002Friaomike-zkp-security-review\u002FTRUST.auto.yaml",[89,118,136,155,172,202],{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":93,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":102,"publishedAt":35},"01M6F3A92FXC8E0GZHN3HEZF9J","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. 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