[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6H6E2ZGS7SK6HDJW0KD1QMD":3,"skill-same:01M6H6E2ZGS7SK6HDJW0KD1QMD":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},"01M6H6E2ZGS7SK6HDJW0KD1QMD","exa-reference-architecture",null,"Implement Exa reference architecture with best-practice project layout.\nUse when designing new Exa integrations, reviewing project structure,\nor establishing architecture standards for Exa applications.\nTrigger with phrases like \"exa architecture\", \"exa best practices\",\n\"exa project structure\", \"how to organize exa\", \"exa layout\".","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\u002Fexa-pack\u002Fskills\u002Fexa-reference-architecture\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"architecture","exa reference architecture","reference","exa","for","openclaw","how to","trigger","io","layout","project","structure","organize","standards","read","integrations","how","applications","code","project structure","2026-11-10 10:12:30",false,"---\nname: exa-reference-architecture\ndescription: |\n  Implement Exa reference architecture with best-practice project layout.\n  Use when designing new Exa integrations, reviewing project structure,\n  or establishing architecture standards for Exa applications.\n  Trigger with phrases like \"exa architecture\", \"exa best practices\",\n  \"exa project structure\", \"how to organize exa\", \"exa layout\".\nallowed-tools: Read, Grep\nversion: 1.0.0\nlicense: MIT\nauthor: Jeremy Longshore \u003Cjeremy@intentsolutions.io>\ncompatible-with: claude-code, codex, openclaw\n---\n# Exa Reference Architecture\n\n## Overview\nProduction architecture for Exa neural search integration. Covers search pipeline design, content extraction patterns, embedding-based retrieval, and caching strategies for semantic search applications.\n\n## Prerequisites\n- Exa API key\n- Understanding of neural vs keyword search\n- TypeScript project with async support\n- Cache layer for search result deduplication\n\n## Architecture Diagram\n\n```\n┌─────────────────────────────────────────────────────┐\n│                Application Layer                     │\n│   RAG Pipeline │ Research Agent │ Content Discovery  │\n└───────────┬─────────────┬────────────┬──────────────┘\n            │             │            │\n            ▼             ▼            ▼\n┌─────────────────────────────────────────────────────┐\n│              Exa Search Service                      │\n│  ┌───────────┐  ┌───────────┐  ┌────────────────┐   │\n│  │ Neural    │  │ Keyword   │  │ Auto (hybrid)  │   │\n│  │ Search    │  │ Search    │  │ Search         │   │\n│  └─────┬─────┘  └─────┬─────┘  └──────┬─────────┘   │\n│        │              │               │              │\n│        ▼              ▼               ▼              │\n│  ┌──────────────────────────────────────────────┐    │\n│  │           Content Extraction                  │    │\n│  │   Text │ Highlights │ Full HTML │ Summary    │    │\n│  └──────────────────────┬───────────────────────┘    │\n│                         │                            │\n│  ┌──────────────────────┴───────────────────────┐    │\n│  │           Result Cache (LRU + Redis)          │    │\n│  └──────────────────────────────────────────────┘    │\n└─────────────────────────────────────────────────────┘\n```\n\n## Instructions\n\n### Step 1: Exa Client Service Layer\n```typescript\nimport Exa from 'exa-js';\n\nconst exa = new Exa(process.env.EXA_API_KEY!);\n\ninterface SearchOptions {\n  query: string;\n  type?: 'neural' | 'keyword' | 'auto';\n  numResults?: number;\n  startDate?: string;\n  endDate?: string;\n  includeDomains?: string[];\n  excludeDomains?: string[];\n  category?: string;\n}\n\nasync function searchWithContents(options: SearchOptions) {\n  return exa.searchAndContents(options.query, {\n    type: options.type || 'auto',\n    numResults: options.numResults || 10,\n    text: { maxCharacters: 3000 },  # 3000: 3 seconds in ms\n    highlights: { numSentences: 3 },\n    startPublishedDate: options.startDate,\n    endPublishedDate: options.endDate,\n    includeDomains: options.includeDomains,\n    excludeDomains: options.excludeDomains,\n    category: options.category,\n  });\n}\n```\n\n### Step 2: Search Pipeline with Content Extraction\n```typescript\nasync function researchTopic(topic: string) {\n  \u002F\u002F Step 1: Broad neural search for relevant sources\n  const sources = await exa.searchAndContents(topic, {\n    type: 'neural',\n    numResults: 20,\n    text: true,\n    highlights: { numSentences: 5 },\n    startPublishedDate: '2024-01-01',  # 2024 year\n    category: 'research paper',\n  });\n\n  \u002F\u002F Step 2: Find similar content to top results\n  const topUrl = sources.results[0]?.url;\n  const similar = topUrl\n    ? await exa.findSimilarAndContents(topUrl, {\n        numResults: 5,\n        text: { maxCharacters: 2000 },  # 2000: 2 seconds in ms\n      })\n    : { results: [] };\n\n  return {\n    primary: sources.results,\n    similar: similar.results,\n    totalSources: sources.results.length + similar.results.length,\n  };\n}\n```\n\n### Step 3: RAG Integration Pattern\n```typescript\nasync function ragSearch(\n  userQuery: string,\n  contextWindow = 5\n) {\n  const results = await exa.searchAndContents(userQuery, {\n    type: 'neural',\n    numResults: contextWindow,\n    text: { maxCharacters: 2000 },  # 2000: 2 seconds in ms\n    highlights: { numSentences: 3 },\n  });\n\n  \u002F\u002F Format for LLM context injection\n  const context = results.results.map((r, i) => (\n    `[Source ${i + 1}] ${r.title}\\n` +\n    `URL: ${r.url}\\n` +\n    `Content: ${r.text}\\n` +\n    `Key highlights: ${r.highlights?.join(' | ')}\\n`\n  )).join('\\n---\\n');\n\n  return {\n    context,\n    sources: results.results.map(r => ({\n      title: r.title,\n      url: r.url,\n      score: r.score,\n    })),\n  };\n}\n```\n\n### Step 4: Domain-Specific Search Configuration\n```typescript\nconst SEARCH_PROFILES = {\n  technical: {\n    includeDomains: ['github.com', 'stackoverflow.com', 'arxiv.org', 'docs.python.org'],\n    category: 'github repo' as const,\n  },\n  news: {\n    includeDomains: ['techcrunch.com', 'theverge.com', 'arstechnica.com'],\n    category: 'news' as const,\n  },\n  research: {\n    includeDomains: ['arxiv.org', 'scholar.google.com', 'nature.com'],\n    category: 'research paper' as const,\n  },\n};\n\nasync function profiledSearch(query: string, profile: keyof typeof SEARCH_PROFILES) {\n  const config = SEARCH_PROFILES[profile];\n  return searchWithContents({ query, ...config, numResults: 10 });\n}\n```\n\n## Error Handling\n| Issue | Cause | Solution |\n|-------|-------|----------|\n| No results | Query too specific | Broaden query, switch to neural search |\n| Low relevance | Wrong search type | Use `auto` type for hybrid results |\n| Content extraction empty | Site blocks scraping | Use highlights instead of full text |\n| Rate limit | Too many concurrent requests | Add request queue with delays |\n\n## Examples\n\n### Content Discovery Pipeline\n```typescript\nasync function discoverCompetitors(companyUrl: string) {\n  const similar = await exa.findSimilar(companyUrl, {\n    numResults: 10,\n    excludeDomains: [new URL(companyUrl).hostname],\n  });\n  return similar.results.map(r => ({ title: r.title, url: r.url }));\n}\n```\n\n## Resources\n- [Exa API Documentation](https:\u002F\u002Fdocs.exa.ai)\n- [Exa Search Types](https:\u002F\u002Fdocs.exa.ai\u002Freference\u002Fsearch)\n- [Exa Content Extraction](https:\u002F\u002Fdocs.exa.ai\u002Freference\u002Fcontents)\n\n## Output\n\n- Configuration files or code changes applied to the project\n- Validation report confirming correct implementation\n- Summary of changes made and their rationale","# Exa Reference Architecture\n\n## Overview\nProduction architecture for Exa neural search integration. Covers search pipeline design, content extraction patterns, embedding-based retrieval, and caching strategies for semantic search applications.\n\n## Prerequisites\n- Exa API key\n- Understanding of neural vs keyword search\n- TypeScript project with async support\n- Cache layer for search result deduplication\n\n## Architecture Diagram\n\n```\n┌─────────────────────────────────────────────────────┐\n│                Application Layer                     │\n│   RAG Pipeline │ Research Agent │ Content Discovery  │\n└───────────┬─────────────┬────────────┬──────────────┘\n            │             │            │\n            ▼             ▼            ▼\n┌─────────────────────────────────────────────────────┐\n│              Exa Search Service                      │\n│  ┌───────────┐  ┌───────────┐  ┌────────────────┐   │\n│  │ Neural    │  │ Keyword   │  │ Auto (hybrid)  │   │\n│  │ Search    │  │ Search    │  │ Search         │   │\n│  └─────┬─────┘  └─────┬─────┘  └──────┬─────────┘   │\n│        │              │               │              │\n│        ▼              ▼               ▼              │\n│  ┌──────────────────────────────────────────────┐    │\n│  │           Content Extraction                  │    │\n│  │   Text │ Highlights │ Full HTML │ Summary    │    │\n│  └──────────────────────┬───────────────────────┘    │\n│                         │                            │\n│  ┌──────────────────────┴───────────────────────┐    │\n│  │           Result Cache (LRU + Redis)          │    │\n│  └──────────────────────────────────────────────┘    │\n└─────────────────────────────────────────────────────┘\n```\n\n## Instructions\n\n### Step 1: Exa Client Service Layer\n```typescript\nimport Exa from 'exa-js';\n\nconst exa = new Exa(process.env.EXA_API_KEY!);\n\ninterface SearchOptions {\n  query: string;\n  type?: 'neural' | 'keyword' | 'auto';\n  numResults?: number;\n  startDate?: string;\n  endDate?: string;\n  includeDomains?: string[];\n  excludeDomains?: string[];\n  category?: string;\n}\n\nasync function searchWithContents(options: SearchOptions) {\n  return exa.searchAndContents(options.query, {\n    type: options.type || 'auto',\n    numResults: options.numResults || 10,\n    text: { maxCharacters: 3000 },  # 3000: 3 seconds in ms\n    highlights: { numSentences: 3 },\n    startPublishedDate: options.startDate,\n    endPublishedDate: options.endDate,\n    includeDomains: options.includeDomains,\n    excludeDomains: options.excludeDomains,\n    category: options.category,\n  });\n}\n```\n\n### Step 2: Search Pipeline with Content Extraction\n```typescript\nasync function researchTopic(topic: string) {\n  \u002F\u002F Step 1: Broad neural search for relevant sources\n  const sources = await exa.searchAndContents(topic, {\n    type: 'neural',\n    numResults: 20,\n    text: true,\n    highlights: { numSentences: 5 },\n    startPublishedDate: '2024-01-01',  # 2024 year\n    category: 'research paper',\n  });\n\n  \u002F\u002F Step 2: Find similar content to top results\n  const topUrl = sources.results[0]?.url;\n  const similar = topUrl\n    ? await exa.findSimilarAndContents(topUrl, {\n        numResults: 5,\n        text: { maxCharacters: 2000 },  # 2000: 2 seconds in ms\n      })\n    : { results: [] };\n\n  return {\n    primary: sources.results,\n    similar: similar.results,\n    totalSources: sources.results.length + similar.results.length,\n  };\n}\n```\n\n### Step 3: RAG Integration Pattern\n```typescript\nasync function ragSearch(\n  userQuery: string,\n  contextWindow = 5\n) {\n  const results = await exa.searchAndContents(userQuery, {\n    type: 'neural',\n    numResults: contextWindow,\n    text: { maxCharacters: 2000 },  # 2000: 2 seconds in ms\n    highlights: { numSentences: 3 },\n  });\n\n  \u002F\u002F Format for LLM context injection\n  const context = results.results.map((r, i) => (\n    `[Source ${i + 1}] ${r.title}\\n` +\n    `URL: ${r.url}\\n` +\n    `Content: ${r.text}\\n` +\n    `Key highlights: ${r.highlights?.join(' | ')}\\n`\n  )).join('\\n---\\n');\n\n  return {\n    context,\n    sources: results.results.map(r => ({\n      title: r.title,\n      url: r.url,\n      score: r.score,\n    })),\n  };\n}\n```\n\n### Step 4: Domain-Specific Search Configuration\n```typescript\nconst SEARCH_PROFILES = {\n  technical: {\n    includeDomains: ['github.com', 'stackoverflow.com', 'arxiv.org', 'docs.python.org'],\n    category: 'github repo' as const,\n  },\n  news: {\n    includeDomains: ['techcrunch.com', 'theverge.com', 'arstechnica.com'],\n    category: 'news' as const,\n  },\n  research: {\n    includeDomains: ['arxiv.org', 'scholar.google.com', 'nature.com'],\n    category: 'research paper' as const,\n  },\n};\n\nasync function profiledSearch(query: string, profile: keyof typeof SEARCH_PROFILES) {\n  const config = SEARCH_PROFILES[profile];\n  return searchWithContents({ query, ...config, numResults: 10 });\n}\n```\n\n## Error Handling\n| Issue | Cause | Solution |\n|-------|-------|----------|\n| No results | Query too specific | Broaden query, switch to neural search |\n| Low relevance | Wrong search type | Use `auto` type for hybrid results |\n| Content extraction empty | Site blocks scraping | Use highlights instead of full text |\n| Rate limit | Too many concurrent requests | Add request queue with delays |\n\n## Examples\n\n### Content Discovery Pipeline\n```typescript\nasync function discoverCompetitors(companyUrl: string) {\n  const similar = await exa.findSimilar(companyUrl, {\n    numResults: 10,\n    excludeDomains: [new URL(companyUrl).hostname],\n  });\n  return similar.results.map(r => ({ title: r.title, url: r.url }));\n}\n```\n\n## Resources\n- [Exa API Documentation](https:\u002F\u002Fdocs.exa.ai)\n- [Exa Search Types](https:\u002F\u002Fdocs.exa.ai\u002Freference\u002Fsearch)\n- [Exa Content Extraction](https:\u002F\u002Fdocs.exa.ai\u002Freference\u002Fcontents)\n\n## Output\n\n- Configuration files or code changes applied to the project\n- Validation report confirming correct implementation\n- Summary of changes made and their rationale",{"name":5,"author":43,"license":14,"version":44,"description":45,"allowed-tools":46,"compatible-with":47},"Jeremy Longshore \u003Cjeremy@intentsolutions.io>","1.0.0","Implement Exa reference architecture with best-practice project layout.\nUse when designing new Exa integrations, reviewing project structure,\nor establishing architecture standards for Exa applications.\nTrigger with phrases like \"exa architecture\", \"exa best practices\",\n\"exa project structure\", \"how to organize exa\", \"exa layout\".\n","Read, Grep","claude-code, codex, openclaw","master","3f5d983eb4ef375b749adcd6b07ec03bd82ae8a8","claude-code\u002Fplugins-plus-skills\u002Fplugins\u002Fsaas-packs\u002Fexa-pack\u002Fskills\u002Fexa-reference-architecture",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 exa-reference-architecture",{"tool":61,"command":62},"bunx","bunx skills add micsapp\u002Fmicstec-skills --skill exa-reference-architecture",{"tool":64,"command":65},"pnpm","pnpm dlx skills add micsapp\u002Fmicstec-skills --skill exa-reference-architecture","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\u002Fexa-pack\u002Fskills\u002Fexa-reference-architecture\u002FSKILL.md",true,[],[70,105,134,155,175,199],{"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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